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YOU’LL WALK AWAY WITH:

A robust AI decision framework, helping you understand the considerations associated with implementing AI in health care, and equipping you to ask the right questions about AI’s suitability to your context.

An awareness of how AI-powered solutions can transform health care, with opportunities including disease diagnosis and monitoring, clinical workflow augmentation, and hospital optimization.

Insights into the various AI-based techniques impacting and improving upon traditional health care structures, including natural language processing, data analytics, and machine learning.

COURSE CURRICULUM

Over the duration of this online program, you’ll work your way through the following modules:

Module 1:
AI and Machine Learning — Applications and Foundations
Become familiar with supervised machine learning and the types of problems it may be applied to.

Module 2:
Using AI for Disease Diagnosis and Patient Monitoring
Examine real-world applications of AI for diagnosis and patient monitoring.

Module 3:
Natural Language Processing and Data Analytics in Health Care
Use AI to extract value-adding outcomes from medical literature and pathology reports.

Module 4:
Interpretability in Machine Learning — Benefits and Challenges
Appreciate the importance and benefits of interpretable algorithms.

Module 5:
Patient Risk Stratification and Augmenting Clinical Workflows
Discover how AI can be applied to health care interventions and patient care.

Module 6:
Taking An Integrated Approach to Hospital Management and Optimization
Investigate a holistic approach to optimizing health care processes.

No longer science fiction, AI and robotics are transforming healthcare

AI is getting increasingly sophisticated at doing what humans do, but more efficiently, more quickly and at a lower cost. The potential for both AI and robotics in healthcare is vast. Just like in our every-day lives, AI and robotics are increasingly a part of our healthcare eco-system.

We have highlighted eight ways that showcase how this transformation is currently underway.

One of AI's biggest potential benefits is to help people stay healthy so they don't need a doctor, or at least not as often. The use of AI and the Internet of Medical Things (IoMT) in consumer health applications is already helping people.

Technology applications and apps encourage healthier behaviour in individuals and help with the proactive management of a healthy lifestyle. It puts consumers in control of health and well-being.

Additionally, AI increases the ability for healthcare professionals to better understand the day-to-day patterns and needs of the people they care for, and with that understanding they are able to provide better feedback, guidance and support for staying healthy.

AI is already being used to detect diseases, such as cancer, more accurately and in their early stages. According to the American Cancer Society, a high proportion of mammograms yield false results, leading to 1 in 2 healthy women being told they have cancer. The use of AI is enabling review and translation of mammograms 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies[1].

The proliferation of consumer wearables and other medical devices combined with AI is also being applied to oversee early-stage heart disease, enabling doctors and other caregivers to better monitor and detect potentially life-threatening episodes at earlier, more treatable stages.

[1] Wired (2016). http://www.wired.co.uk/article/cancer-risk-ai-mammograms

IBM’s Watson for Health is helping healthcare organizations apply cognitive technology to unlock vast amounts of health data and power diagnosis. Watson can review and store far more medical information – every medical journal, symptom, and case study of treatment and response around the world – exponentially faster than any human.

Google’s DeepMind Health is working in partnership with clinicians, researchers and patients to solve real-world healthcare problems. The technology combines machine learning and systems neuroscience to build powerful general-purpose learning algorithms into neural networks that mimic the human brain.

Improving care requires the alignment of big health data with appropriate and timely decisions, and predictive analytics can support clinical decision-making and actions as well as prioritise administrative tasks.

Using pattern recognition to identify patients at risk of developing a condition – or seeing it deteriorate due to lifestyle, environmental, genomic, or other factors – is another area where AI is beginning to take hold in healthcare.

Beyond scanning health records to help providers identify chronically ill individuals who may be at risk of an adverse episode, AI can help clinicians take a more comprehensive approach for disease management, better coordinate care plans and help patients to better manage and comply with their long-term treatment programmes.

Robots have been used in medicine for more than 30 years. They range from simple laboratory robots to highly complex surgical robots that can either aid a human surgeon or execute operations by themselves. In addition to surgery, they’re used in hospitals and labs for repetitive tasks, in rehabilitation, physical therapy and in support of those with long-term conditions.

We are living much longer than previous generations, and as we approach the end of life, we are dying in a different and slower way, from conditions like dementia, heart failure and osteoporosis. It is also a phase of life that is often plagued by loneliness.

Robots have the potential to revolutionise end of life care, helping people to remain independent for longer, reducing the need for hospitalisation and care homes. AI combined with the advancements in humanoid design are enabling robots to go even further and have ‘conversations’ and other social interactions with people to keep aging minds sharp.

The path from research lab to patient is a long and costly one. According to the California Biomedical Research Association, it takes an average of 12 years for a drug to travel from the research lab to the patient. Only five in 5,000 of the drugs that begin preclinical testing ever make it to human testing and just one of these five is ever approved for human usage. Furthermore, on average, it will cost a company US $359 million to develop a new drug from the research lab to the patient[1].

Drug research and discovery is one of the more recent applications for AI in healthcare. By directing the latest advances in AI to streamline the drug discovery and drug repurposing processes there is the potential to significantly cut both the time to market for new drugs and their costs.

[1] California Biomedical Research Association. New Drug Development Process. http://www.ca-biomed.org/pdf/media-kit/fact-sheets/CBRADrugDevelop.pdf (pdf 112kb)

AI allows those in training to go through naturalistic simulations in a way that simple computer-driven algorithms cannot. The advent of natural speech and the ability of an AI computer to draw instantly on a large database of scenarios, means the response to questions, decisions or advice from a trainee can challenge in a way that a human cannot. And the training programme can learn from previous responses from the trainee, meaning that the challenges can be continually adjusted to meet their learning needs.

And training can be done anywhere; with the power of AI embedded on a smartphone, quick catch up sessions, after a tricky case in a clinic or while travelling, will be possible.

The potential for artificial intelligence in healthcare

Thomas Davenport, president's distinguished professor of information technology and managementA and Ravi Kalakota, managing directorB

Author information Copyright and License information Disclaimer

This article has been cited by other articles in PMC.

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ABSTRACT

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

KEYWORDS: Artificial intelligence, clinical decision support, electronic health record systems

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Introduction

Artificial intelligence (AI) and related technologies are increasingly prevalent in business and society, and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations.

There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease. Today, algorithms are already outperforming radiologists at spotting malignant tumours, and guiding researchers in how to construct cohorts for costly clinical trials. However, for a variety of reasons, we believe that it will be many years before AI replaces humans for broad medical process domains. In this article, we describe both the potential that AI offers to automate aspects of care and some of the barriers to rapid implementation of AI in healthcare.

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Types of AI of relevance to healthcare

Artificial intelligence is not one technology, but rather a collection of them. Most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support vary widely. Some particular AI technologies of high importance to healthcare are defined and described below.

Machine learning – neural networks and deep learning

Machine learning is a statistical technique for fitting models to data and to ‘learn’ by training models with data. Machine learning is one of the most common forms of AI; in a 2018 Deloitte survey of 1,100 US managers whose organisations were already pursuing AI, 63% of companies surveyed were employing machine learning in their businesses.1 It is a broad technique at the core of many approaches to AI and there are many versions of it.

In healthcare, the most common application of traditional machine learning is precision medicine – predicting what treatment protocols are likely to succeed on a patient based on various patient attributes and the treatment context.2 The great majority of machine learning and precision medicine applications require a training dataset for which the outcome variable (eg onset of disease) is known; this is called supervised learning.

A more complex form of machine learning is the neural network – a technology that has been available since the 1960s has been well established in healthcare research for several decades3 and has been used for categorisation applications like determining whether a patient will acquire a particular disease. It views problems in terms of inputs, outputs and weights of variables or ‘features’ that associate inputs with outputs. It has been likened to the way that neurons process signals, but the analogy to the brain's function is relatively weak.

The most complex forms of machine learning involve deep learning, or neural network models with many levels of features or variables that predict outcomes. There may be thousands of hidden features in such models, which are uncovered by the faster processing of today's graphics processing units and cloud architectures. A common application of deep learning in healthcare is recognition of potentially cancerous lesions in radiology images.4 Deep learning is increasingly being applied to radiomics, or the detection of clinically relevant features in imaging data beyond what can be perceived by the human eye.5 Both radiomics and deep learning are most commonly found in oncology-oriented image analysis. Their combination appears to promise greater accuracy in diagnosis than the previous generation of automated tools for image analysis, known as computer-aided detection or CAD.

Deep learning is also increasingly used for speech recognition and, as such, is a form of natural language processing (NLP), described below. Unlike earlier forms of statistical analysis, each feature in a deep learning model typically has little meaning to a human observer. As a result, the explanation of the model's outcomes may be very difficult or impossible to interpret.

Natural language processing

Making sense of human language has been a goal of AI researchers since the 1950s. This field, NLP, includes applications such as speech recognition, text analysis, translation and other goals related to language. There are two basic approaches to it: statistical and semantic NLP. Statistical NLP is based on machine learning (deep learning neural networks in particular) and has contributed to a recent increase in accuracy of recognition. It requires a large ‘corpus’ or body of language from which to learn.

In healthcare, the dominant applications of NLP involve the creation, understanding and classification of clinical documentation and published research. NLP systems can analyse unstructured clinical notes on patients, prepare reports (eg on radiology examinations), transcribe patient interactions and conduct conversational AI.

Rule-based expert systems

Expert systems based on collections of ‘if-then’ rules were the dominant technology for AI in the 1980s and were widely used commercially in that and later periods. In healthcare, they were widely employed for ‘clinical decision support’ purposes over the last couple of decades5 and are still in wide use today. Many electronic health record (EHR) providers furnish a set of rules with their systems today.

Expert systems require human experts and knowledge engineers to construct a series of rules in a particular knowledge domain. They work well up to a point and are easy to understand. However, when the number of rules is large (usually over several thousand) and the rules begin to conflict with each other, they tend to break down. Moreover, if the knowledge domain changes, changing the rules can be difficult and time-consuming. They are slowly being replaced in healthcare by more approaches based on data and machine learning algorithms.

Physical robots

Physical robots are well known by this point, given that more than 200,000 industrial robots are installed each year around the world. They perform pre-defined tasks like lifting, repositioning, welding or assembling objects in places like factories and warehouses, and delivering supplies in hospitals. More recently, robots have become more collaborative with humans and are more easily trained by moving them through a desired task. They are also becoming more intelligent, as other AI capabilities are being embedded in their ‘brains’ (really their operating systems). Over time, it seems likely that the same improvements in intelligence that we've seen in other areas of AI would be incorporated into physical robots.

Surgical robots, initially approved in the USA in 2000, provide ‘superpowers’ to surgeons, improving their ability to see, create precise and minimally invasive incisions, stitch wounds and so forth.6 Important decisions are still made by human surgeons, however. Common surgical procedures using robotic surgery include gynaecologic surgery, prostate surgery and head and neck surgery.

Robotic process automation

This technology performs structured digital tasks for administrative purposes, ie those involving information systems, as if they were a human user following a script or rules. Compared to other forms of AI they are inexpensive, easy to program and transparent in their actions. Robotic process automation (RPA) doesn't really involve robots – only computer programs on servers. It relies on a combination of workflow, business rules and ‘presentation layer’ integration with information systems to act like a semi-intelligent user of the systems. In healthcare, they are used for repetitive tasks like prior authorisation, updating patient records or billing. When combined with other technologies like image recognition, they can be used to extract data from, for example, faxed images in order to input it into transactional systems.7

We've described these technologies as individual ones, but increasingly they are being combined and integrated; robots are getting AI-based ‘brains’, image recognition is being integrated with RPA. Perhaps in the future these technologies will be so intermingled that composite solutions will be more likely or feasible.

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Diagnosis and treatment applications

Diagnosis and treatment of disease has been a focus of AI since at least the 1970s, when MYCIN was developed at Stanford for diagnosing blood-borne bacterial infections.8 This and other early rule-based systems showed promise for accurately diagnosing and treating disease, but were not adopted for clinical practice. They were not substantially better than human diagnosticians, and they were poorly integrated with clinician workflows and medical record systems.

More recently, IBM's Watson has received considerable attention in the media for its focus on precision medicine, particularly cancer diagnosis and treatment. Watson employs a combination of machine learning and NLP capabilities. However, early enthusiasm for this application of the technology has faded as customers realised the difficulty of teaching Watson how to address particular types of cancer9 and of integrating Watson into care processes and systems.10 Watson is not a single product but a set of ‘cognitive services’ provided through application programming interfaces (APIs), including speech and language, vision, and machine learning-based data-analysis programs. Most observers feel that the Watson APIs are technically capable, but taking on cancer treatment was an overly ambitious objective. Watson and other proprietary programs have also suffered from competition with free ‘open source’ programs provided by some vendors, such as Google's TensorFlow.

Implementation issues with AI bedevil many healthcare organisations. Although rule-based systems incorporated within EHR systems are widely used, including at the NHS,11 they lack the precision of more algorithmic systems based on machine learning. These rule-based clinical decision support systems are difficult to maintain as medical knowledge changes and are often not able to handle the explosion of data and knowledge based on genomic, proteomic, metabolic and other ‘omic-based’ approaches to care.

This situation is beginning to change, but it is mostly present in research labs and in tech firms, rather than in clinical practice. Scarcely a week goes by without a research lab claiming that it has developed an approach to using AI or big data to diagnose and treat a disease with equal or greater accuracy than human clinicians. Many of these findings are based on radiological image analysis,12 though some involve other types of images such as retinal scanning13 or genomic-based precision medicine.14 Since these types of findings are based on statistically-based machine learning models, they are ushering in an era of evidence- and probability-based medicine, which is generally regarded as positive but brings with it many challenges in medical ethics and patient/clinician relationships.15

Tech firms and startups are also working assiduously on the same issues. Google, for example, is collaborating with health delivery networks to build prediction models from big data to warn clinicians of high-risk conditions, such as sepsis and heart failure.16 Google, Enlitic and a variety of other startups are developing AI-derived image interpretation algorithms. Jvion offers a ‘clinical success machine’ that identifies the patients most at risk as well as those most likely to respond to treatment protocols. Each of these could provide decision support to clinicians seeking to find the best diagnosis and treatment for patients.

There are also several firms that focus specifically on diagnosis and treatment recommendations for certain cancers based on their genetic profiles. Since many cancers have a genetic basis, human clinicians have found it increasingly complex to understand all genetic variants of cancer and their response to new drugs and protocols. Firms like Foundation Medicine and Flatiron Health, both now owned by Roche, specialise in this approach.

Both providers and payers for care are also using ‘population health’ machine learning models to predict populations at risk of particular diseases17 or accidents18 or to predict hospital readmission.19 These models can be effective at prediction, although they sometimes lack all the relevant data that might add predictive capability, such as patient socio-economic status.

But whether rules-based or algorithmic in nature, AI-based diagnosis and treatment recommendations are sometimes challenging to embed in clinical workflows and EHR systems. Such integration issues have probably been a greater barrier to broad implementation of AI than any inability to provide accurate and effective recommendations; and many AI-based capabilities for diagnosis and treatment from tech firms are standalone in nature or address only a single aspect of care. Some EHR vendors have begun to embed limited AI functions (beyond rule-based clinical decision support) into their offerings,20 but these are in the early stages. Providers will either have to undertake substantial integration projects themselves or wait until EHR vendors add more AI capabilities.

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Patient engagement and adherence applications

Patient engagement and adherence has long been seen as the ‘last mile’ problem of healthcare – the final barrier between ineffective and good health outcomes. The more patients proactively participate in their own well-being and care, the better the outcomes – utilisation, financial outcomes and member experience. These factors are increasingly being addressed by big data and AI.

Providers and hospitals often use their clinical expertise to develop a plan of care that they know will improve a chronic or acute patient's health. However, that often doesn't matter if the patient fails to make the behavioural adjustment necessary, eg losing weight, scheduling a follow-up visit, filling prescriptions or complying with a treatment plan. Noncompliance – when a patient does not follow a course of treatment or take the prescribed drugs as recommended – is a major problem.

In a survey of more than 300 clinical leaders and healthcare executives, more than 70% of the respondents reported having less than 50% of their patients highly engaged and 42% of respondents said less than 25% of their patients were highly engaged.21

If deeper involvement by patients results in better health outcomes, can AI-based capabilities be effective in personalising and contextualising care? There is growing emphasis on using machine learning and business rules engines to drive nuanced interventions along the care continuum.22 Messaging alerts and relevant, targeted content that provoke actions at moments that matter is a promising field in research.

Another growing focus in healthcare is on effectively designing the ‘choice architecture’ to nudge patient behaviour in a more anticipatory way based on real-world evidence. Through information provided by provider EHR systems, biosensors, watches, smartphones, conversational interfaces and other instrumentation, software can tailor recommendations by comparing patient data to other effective treatment pathways for similar cohorts. The recommendations can be provided to providers, patients, nurses, call-centre agents or care delivery coordinators.

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Administrative applications

There are also a great many administrative applications in healthcare. The use of AI is somewhat less potentially revolutionary in this domain as compared to patient care, but it can provide substantial efficiencies. These are needed in healthcare because, for example, the average US nurse spends 25% of work time on regulatory and administrative activities.23 The technology that is most likely to be relevant to this objective is RPA. It can be used for a variety of applications in healthcare, including claims processing, clinical documentation, revenue cycle management and medical records management.24

Some healthcare organisations have also experimented with chatbots for patient interaction, mental health and wellness, and telehealth. These NLP-based applications may be useful for simple transactions like refilling prescriptions or making appointments. However, in a survey of 500 US users of the top five chatbots used in healthcare, patients expressed concern about revealing confidential information, discussing complex health conditions and poor usability.25

Another AI technology with relevance to claims and payment administration is machine learning, which can be used for probabilistic matching of data across different databases. Insurers have a duty to verify whether the millions of claims are correct. Reliably identifying, analysing and correcting coding issues and incorrect claims saves all stakeholders – health insurers, governments and providers alike – a great deal of time, money and effort. Incorrect claims that slip through the cracks constitute significant financial potential waiting to be unlocked through data-matching and claims audits.

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Implications for the healthcare workforce

There has been considerable attention to the concern that AI will lead to automation of jobs and substantial displacement of the workforce. A Deloitte collaboration with the Oxford Martin Institute26 suggested that 35% of UK jobs could be automated out of existence by AI over the next 10 to 20 years. Other studies have suggested that while some automation of jobs is possible, a variety of external factors other than technology could limit job loss, including the cost of automation technologies, labour market growth and cost, benefits of automation beyond simple labour substitution, and regulatory and social acceptance.27 These factors might restrict actual job loss to 5% or less.

To our knowledge thus far there have been no jobs eliminated by AI in health care. The limited incursion of AI into the industry thus far, and the difficulty of integrating AI into clinical workflows and EHR systems, have been somewhat responsible for the lack of job impact. It seems likely that the healthcare jobs most likely to be automated would be those that involve dealing with digital information, radiology and pathology for example, rather than those with direct patient contact.28

But even in jobs like radiologist and pathologist, the penetration of AI into these fields is likely to be slow. Even though, as we have argued, technologies like deep learning are making inroads into the capability to diagnose and categorise images, there are several reasons why radiology jobs, for example, will not disappear soon.29

First, radiologists do more than read and interpret images. Like other AI systems, radiology AI systems perform single tasks. Deep learning models in labs and startups are trained for specific image recognition tasks (such as nodule detection on chest computed tomography or hemorrhage on brain magnetic resonance imaging). However, thousands of such narrow detection tasks are necessary to fully identify all potential findings in medical images, and only a few of these can be done by AI today. Radiologists also consult with other physicians on diagnosis and treatment, treat diseases (for example providing local ablative therapies) and perform image-guided medical interventions such as cancer biopsies and vascular stents (interventional radiology), define the technical parameters of imaging examinations to be performed (tailored to the patient's condition), relate findings from images to other medical records and test results, discuss procedures and results with patients, and many other activities.

Second, clinical processes for employing AI-based image work are a long way from being ready for daily use. Different imaging technology vendors and deep learning algorithms have different foci: the probability of a lesion, the probability of cancer, a nodule's feature or its location. These distinct foci would make it very difficult to embed deep learning systems into current clinical practice.

Third, deep learning algorithms for image recognition require ‘labelled data’ – millions of images from patients who have received a definitive diagnosis of cancer, a broken bone or other pathology. However, there is no aggregated repository of radiology images, labelled or otherwise.

Finally, substantial changes will be required in medical regulation and health insurance for automated image analysis to take off.

Similar factors are present for pathology and other digitally-oriented aspects of medicine. Because of them, we are unlikely to see substantial change in healthcare employment due to AI over the next 20 years or so. There is also the possibility that new jobs will be created to work with and to develop AI technologies. But static or increasing human employment also mean, of course, that AI technologies are not likely to substantially reduce the costs of medical diagnosis and treatment over that timeframe.

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Ethical implications

Finally, there are also a variety of ethical implications around the use of AI in healthcare. Healthcare decisions have been made almost exclusively by humans in the past, and the use of smart machines to make or assist with them raises issues of accountability, transparency, permission and privacy.

Perhaps the most difficult issue to address given today's technologies is transparency. Many AI algorithms – particularly deep learning algorithms used for image analysis – are virtually impossible to interpret or explain. If a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. Deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide an explanation.

Mistakes will undoubtedly be made by AI systems in patient diagnosis and treatment and it may be difficult to establish accountability for them. There are also likely to be incidents in which patients receive medical information from AI systems that they would prefer to receive from an empathetic clinician. Machine learning systems in healthcare may also be subject to algorithmic bias, perhaps predicting greater likelihood of disease on the basis of gender or race when those are not actually causal factors.30

We are likely to encounter many ethical, medical, occupational and technological changes with AI in healthcare. It is important that healthcare institutions, as well as governmental and regulatory bodies, establish structures to monitor key issues, react in a responsible manner and establish governance mechanisms to limit negative implications. This is one of the more powerful and consequential technologies to impact human societies, so it will require continuous attention and thoughtful policy for many years.

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The future of AI in healthcare

We believe that AI has an important role to play in the healthcare offerings of the future. In the form of machine learning, it is the primary capability behind the development of precision medicine, widely agreed to be a sorely needed advance in care. Although early efforts at providing diagnosis and treatment recommendations have proven challenging, we expect that AI will ultimately master that domain as well. Given the rapid advances in AI for imaging analysis, it seems likely that most radiology and pathology images will be examined at some point by a machine. Speech and text recognition are already employed for tasks like patient communication and capture of clinical notes, and their usage will increase.

The greatest challenge to AI in these healthcare domains is not whether the technologies will be capable enough to be useful, but rather ensuring their adoption in daily clinical practice. For widespread adoption to take place, AI systems must be approved by regulators, integrated with EHR systems, standardised to a sufficient degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organisations and updated over time in the field. These challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature. As a result, we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10.

It also seems increasingly clear that AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients. Over time, human clinicians may move toward tasks and job designs that draw on uniquely human skills like empathy, persuasion and big-picture integration. Perhaps the only healthcare providers who will lose their jobs over time may be those who refuse to work alongside artificial intelligence.

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Artificial Intelligence (AI) is at the forefront of innovation in healthcare. It has infiltrated the healthcare space tremendously in the last decade bringing in billions of dollars to different sectors. Top AI applications in healthcare include robot-assisted surgery, virtual nursing assistants, dosage error reduction, and much more. These applications are projected to create $150 billion dollars in annual savings for the healthcare economy by 2026.

Because of this AI healthcare boom, insurers and investors are pouring millions of dollars into these technologies. Here are 9 top AI companies in healthcare to keep an eye on.

Remedy Health

Remedy Health’s AI-assisted platform equips non-physician staff with clinical expertise to uncover hidden chronic diseases through phone screening interviews. Early diagnosis allows them to find the best fulcrum point for intervention to positively affect health outcomes and decrease cost. Finding undiagnosed patients will also drastically increase a health system's RAF scores and profitability.

What’s Unique?

Competitors rely only on sparse historical medical records and claims, which are only generated when the patient is in the hospital system, usually after they are already sick. Remedy Health’s system empowers low cost, non-physician staff to proactively screen patients through virtual interviews and capture clinically relevant data outside of the 4-walls of the hospital for timely decision-making.

Subtle Medical

Subtle Medical has developed a suite of deep learning software solutions that enhance images during the acquisition phase of the radiology workflow, improving workflow efficiency and patient experience. SubtleMR and SubtlePET, both FDA cleared and CE Mark approved, utilize deep learning algorithms that integrate seamlessly with any scanner and PACS system with no change in the imaging specialists’ workflow. SubtlePET and SubtleMR bring the latest imaging enhancement technology to existing scanners.

What’s Unique?

Subtle Medical's technology is well recognized by the AI and radiology community and awarded by RSNA. Subtle Medical won the 2018 NVIDIA Inception Award as a Top 1 AI Healthcare startup globally. Subtle Medical is partnering with top industry vendors such as AWS, Google Cloud, NVIDIA, and Intel to bring the best AI solution to hospitals.

Quid

Quid inspires full-picture thinking by drawing connections across massive amounts of written content, enabling senior leaders to draw insights from big data (e.g., media, patents, employee reviews, analyst reports, company descriptions). Quid supports 300+ companies across the globe and was recently recognized by CNBC on their list of "2017’s top 50 disruptors” (joining the ranks of Google and Airbnb in this unique honor).

What’s Unique?

Leveraging proprietary algorithms, Quid is able to read through the world’s data in minutes, creating dynamic visualizations. These visualizations allow senior leaders to understand the contextual element of any topic/narrative, versus the traditional static report or list search.

BioSymetrics

Traditional ML technologies are incompatible with biomedical raw data formats, and there are few standards for data standardization, normalization, and harmonization. BioSymetrics solves this problem by deploying its primary solution, Augusta, which is a pre-processing and analytics platform that can process large amounts of data (siloed and raw data) for predictive analytics. This is useful for capturing the exabytes of data released from the 25B IoT devices and other biomedical data types (EEG, MRI and others) and deriving actionable insights from them. The customized and flexible tool can be used for scientists, providers, hospitals, biopharmaceutical companies.

What’s Unique?

Augusta is the first biomedical specific machine learning framework. Augusta is designed to transition time from data pre-processing and integration to model building and interrogation using familiar toolsets within Python. Augusta begins with diverse, raw medical data types (e.g. images, chemical structures, genomic data, tabular data), and operates across three modules: Pre-Processing, Machine Learning, and Architect.

Sensely

Sensely is an avatar-based, empathy-driven platform that leverages natural user interfaces to intelligently connect insurance plan members with advice and services. By utilizing Sensely’s scalable platform technology architecture, insurance companies can converse with their members in an entirely new way, combining the empathy of human conversation with the efficiency and scalability of technology.

What’s Unique?

Support for 32 languages is included, making Sensely ideal for large organizations with broad geographic and language coverage. With offices in London and San Francisco, Sensely’s global teams provide virtual assistant solutions to insurance companies, pharmaceutical clients, and hospital systems worldwide.

InformAI

InformAI is an AI company with a healthcare focus on products that speed up medical diagnosis at the point-of-care and improve radiologist productivity. InformAI’s AI-enabled image classifiers and patient outcome predictors are developed within the world’s largest medical center complex as well as with national physician groups and a leading medical imaging company. InformAI with its partners are transforming the way healthcare is being delivered.

What’s Unique?

The company has key differentiators, such as access to 10X larger privileged medical datasets, direct-access to world-class medical experts and proprietary AI data augmentation, model optimization and 3D neural network toolsets. InformAI was selected by NVIDIA in 2018 to join their AI Healthcare Inception Partnership and was cited by Forbes as one of 8 Startups Ahead Of The Pulse In Healthcare.

SaliencyAI

SaliencyAI enables pharmaceutical companies to leverage artificial intelligence in their R&D.

They provide a suite of tools that streamline each step in the data science pipeline for pharmaceutical companies:

1. Data Labeling:

Leverage a single user-friendly interface all of your data partners can use to label and submit data so you receive everything in a standardized format.

2. Data Unification:

Perform analyses on a combination of several preexisting data sets with ease. Retrieve many heterogeneous data sources as a single, uniform data set with a single line of code.

3. Training artificial intelligence models:

Efficiently create and train models with a few lines of high-level code. Automatically compare several cutting-edge models to find the best. Access models that have already learned from hundreds of thousands data points to minimize your own data collection efforts.

4. Deployment

Empower your teams with user-friendly research tools backed by AI. Automatically convert trained models into intuitive web apps that are HIPAA-compliant.

What’s Unique?

SaliencyAI focuses on the requirements of AI development and deployment that are specific to the pharmaceutical industry. This includes special focus on data security, HIPAA compliance, and algorithms designed to perform well on biomedical data. Meeting this industry's specific analytics needs requires a nuanced understanding of both biomedical research and data science. Their combined expertise spans AI, computer vision, software architecture, and medicine, placing them in a unique position to address these needs.

Owkin

Founded in 2016, Owkin combines life-science and machine learning expertise to make drug development and clinical trial design more targeted and more cost effective. Owkin's machine learning algorithms create models that predict disease evolution and treatment outcomes. These predictive models are used for enhanced analysis, surrogate endpoints, patient stratification and selection, and subgroup identification. The impact of this research is faster discovery of better treatments at a lower cost.

To train its models, Owkin has developed a real-world data access network through collaborations with top tier hospitals. This network is the first at-scale solution for federated on-site machine learning for the healthcare industry. Through this network, Owkin can interrogate heterogeneous real-world data, while preserving patient privacy. Owkin works hand in hand with world-class clinicians to interprets its models' features to discover and validate validate novel multimodal biomarkers.

What’s Unique?

OWKIN has launched an AI-powered network comprised of 44 hospitals and research institutions in the United States and Europe. Members include Cleveland Clinic, Mount Sinai and Groupe AP-HP, a group of 39 hospitals in France.

Binah.ai

Binah.ai is shaping the future of Artificial Intelligence (AI) by simplifying and accelerating AI adoption with our world-leading expertise in machine and deep learning, signal processing and AI, addressing high-value problems in multiple Industries. Binah.ai has released a series of non-invasive, video-based health and wellness monitoring solutions. Binah.ai gives an unparalleled advantage in health analytics as its technology transforms any device equipped with a simple camera into a medical-grade healthcare gadget. The video-based digital health use cases include heart rate and heart rate variability (HRV) measurements, providing the data for stress measurements.

What’s Unique?

Binah.ai is the only company offering clearly defined, pre-built use cases and offers signal processing-integrated data science. They have a custom-built, proprietary, comprehensive, and complete mathematical back end and actionable intelligence strengthens the onsite data science team's capabilities. With end-to-end solutions focused on business results, Binh.ai solves real-world challenges with a production focus.

If your company is making a difference to the healthcare space, be sure to join our health accelerator today.

The Rise of Patient Care Technology

Improving patient care with cutting-edge technology.

Read the rest of the collection.

Telemedicine. Remote Patient Monitoring. Digital Therapeutics. AI.

Find out about the revolution of technology in patient-centric care.

Find out more

Artificial Intelligence in Healthcare

11 Remote Patient Monitoring Companies You Should Know About

8 Top Telemedicine Companies in 2020

Clinical Applications of AI Today and in the Future

There are numerous applications of AI on the market today or awaiting approval that can improve patient care and potentially save lives.

Those applications involve pattern recognition, robotics and natural language processing, which includes speech recognition and translation. Machine learning, a “technique that trains software algorithms to learn from and act upon new data to continuously improve performance,” is also increasingly used today, Dr. Weber said.

He gave a few examples of the latest tools that leverage AI and its subsets to augment various areas of medicine and healthcare, such as:

Virtual assistants: This AI-driven technology can help people with Alzheimer’s disease with their daily activities, Dr. Weber said. For example, 59-year-old Brian Leblanc, who was diagnosed with early onset Alzheimer’s disease in 2014, started using Alexa on his Amazon Echo Dot for reminders to eat, bathe and take medication. “What it enables him to do is to have more control over his life,” Dr. Weber said.
MelaFind: This technology uses infrared light to evaluate pigmented lesions. Using algorithms, dermatologists can analyze irregular moles and diagnose serious skin cancers such as melanoma. Although this technology should not replace a biopsy, it helps with giving an early identification, Dr. Weber said.
Robotic assisted therapy: Bionik Laboratories in Toronto and Watertown, Mass., use robotics and AI to assist patients in their stroke recovery. A robotic arm and hand use digital algorithms to detect motions that patients can’t execute during therapy and guides them through it. Dr. Weber noted that it can help patients perform more recorded movements per hour than they would have if working with a physical therapist alone.
Caption Guidance: The Food and Drug Administration just approved this AI-powered software, which can help medical professionals capture, without any specialized training, echocardiographic images of a patient’s heart that are of acceptable diagnostic quality, Dr. Weber said. Machine learning trains the software to spot high-quality 2D ultrasound images of the heart and even record video clips of it, changing the way heart disease is diagnosed.

What Medical Professionals Should Consider Before Adopting AI

With this explosion in innovation, it’s important for healthcare professionals and other stakeholders to understand the regulations set in place for the effective development and deployment of these technologies.

“We can’t just put out all of these applications of artificial intelligence without getting approval from regulatory authorities,” Dr. Weber said.

Agencies have started thinking about how their regulatory framework can adapt to new and evolving technologies, he said. For example, the FDA introduced a new framework last year that enables it to pre-approve manufacturing of adaptive AI-powered software. “It allows for more testing and more rapid approval, and so you’ll see faster turnover, much like the tech industry with smartphones,” Dr. Weber said.

Medical professionals must also prioritize patient privacy and security when considering AI applications, he said. And despite the growing presence of AI in healthcare, the practitioner-patient relationship still endures, he said.

“We can talk about all of these devices, but patients still want to talk to their practitioners,” Dr. Weber said. “AI should not replace human interaction — in the end, someone still should be in charge of someone’s care.”

artificial-intelligence-application-heal

Artificial intelligence in health care

Initially, artificial intelligence (AI) was not used to solve geometric and algebraic problems until 1974. Between 1980 and 1987, the number of expert systems capable of solving problems in specific sectors increased. However, as Russian master Garry Kasparov (Garry Kasparov) was defeated by IBM's Deep Blue, the artificial intelligence boom declined in 1997. Since then, there have been many AI achievements, such as humanoid robots, the first home or pet robots, handwriting recognition and testing of self-driving cars.

In addition, artificial intelligence has many applications in various industries such as finance, transportation, technology, and healthcare. With the introduction of AI in the field of healthcare, the process of providing healthcare services to patients has undergone tremendous changes, and now healthcare providers are providing better healthcare services.

The role of artificial intelligence (AI) in healthcare

1. Virtual Health Assistant

The Virtual Health Assistant (VHA) can help sick patients take the necessary medications on time to stay healthy by sending reminders of prescription drugs. VHA can also provide patients with specific dietary recommendations based on their medical conditions, thereby helping patients track their diets. VHA can also help pharmacies remind patients to replenish medicines, and even recommend regular health checks.

2. Diagnosis

Artificial intelligence plays an important role in the diagnosis of any disease. Stanford University researchers also introduced an algorithm that can even diagnose skin cancer. Recently, AI-based technology commonly referred to as inferential vision is used to read CT scans and X-rays. As the parent company of Google, Alphabet is currently researching AI algorithms by using advanced image recognition technology, which can quickly detect transfers at an early stage. Because AI can easily analyze large amounts of patient data, it can help disease detection and make clinical decisions in advance.

3. Healthcare BOT

Robots in healthcare are meant to involve patients. The robot just sends text messages to help patients update their health status in real time. The health chatbot also provides answers to every health-related query raised by patients. It also helps maintain prescribed doses by sending medication and dose reminders.

Some other AI algorithms being developed in the medical field are:

1. Heart Sound Analysis

2. Senior Companion Robot

3. Mining medical records

4. Design a treatment plan

5. Help with repetitive tasks

6. Consult the patient

7. Create new drugs

The advantages of artificial intelligence in healthcare

• Treatment progress

Artificial intelligence plays an important role in treatment progress by helping to formulate treatment plans and analyzing big data that can help provide better treatments. AI also helps to quickly diagnose diseases, which further helps to initiate rapid treatment.

• Virtual assistant

AI also helps patients by providing real-time virtual assistance. Using AI, patients can now ask any health-related questions through messages. They can even receive reminders about medication schedules.

• cut cost

Artificial intelligence helps reduce treatment costs by 50% and increase results by 30-40%. It can also help doctors easily retrieve patient data.

What are professional health products?

ProHealthware is a healthcare solutions and healthcare service company dedicated to solving the needs of the healthcare industry.

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Healthware provides innovative health information technology (health IT) services for medical organizations throughout the value chain to help them develop their business.

What is health information technology?

Health information technology is responsible for the exchange of basic health information through electronic media. This is a broad concept involving a wide range of technologies responsible for storing, analyzing, and sharing useful health information. Experts have found that the use of information technology in the healthcare industry can expand people's convenient access to affordable healthcare solutions, while improving the overall quality of medical institutions, reducing diagnostic errors, reducing paperwork and improving administrative efficiency.

There are many types of health information technology systems. Some of the most useful methods are discussed below:

1. Electronic health record:

It is an electronic version of the patient's complete medical record. The detailed information includes basic management data of the subject, including radiology reports, laboratory data and immunizations, past medical history, vital signs, preferred drugs, progress reports and demographic information.

2. Electronic prescription:

In short, the electronic prescription system works like an electronic reference manual. The tool has sufficient capacity to create and supplement basic prescriptions for patients. It can view its medical history, manage medications and keep in touch with the pharmacy, while maintaining integration with the electronic medical record system.

3. Clinical and administrative health information system:

Clinical information systems are usually used by nurses, doctors and professional medical service providers in clinical care so that patients can get basic care. They can be further divided into outpatient clinical information system, inpatient clinical monitoring system, emergency information system and auxiliary information system. The administrative health information system further leads to the management of patient detailed information at the administrative level.

4. Financial Information System:

Financial information system is one of the most important tools in the field of medical and health systems. It helps to develop the best financial plan so that the diagnosis can be on the right track.

5. Decision support system:

For healthcare professionals, they are one of the essential tools because they can help them make correct disease decisions based on patient data. Decision support systems need to be highly accurate in order to provide greater accuracy for people’s sensitive health issues.

Security of health IT system:

With the continuous development of the healthcare industry, most professionals prefer to use advanced electronic tools to manage patient data to ensure high-quality, error-free services. Everything is stored in digital form, so it can be shared and evaluated more easily, but there are almost no troubles related to the security of digital data.

We cannot compromise the private personal medical health of any patient, so it is essential to incorporate high-quality security measures into the health information technology system. Some of the main solutions for security are enhanced management control, monitoring of system and physical access, use of privacy filters on workstations, use of media controls, and use of advanced data encryption algorithms.

Benefits of health information technology

• Even if the workload is reduced, time can be saved.

• Faster and more efficient turnover.

• Reliable laboratory results have higher accuracy.

• Several financial benefits.

• Easy access to medical data.

Our expertise

Healthware excels in providing leading-quality health IT services and focuses on Health Information Exchange (HIE) and Health Information Organization (HIO). Healthware provides integrated solutions for health IT products, with a focus on improving productivity, efficiency and reducing costs

In today’s healthcare IT products and healthcare IT solutions that need to be effective in healthcare management systems, since hospitals are accompanied by multiple healthcare information systems, it is always a challenge and possible to uniquely identify patient records Leading to duplicate medical records will increase costs.

Healthware's EMR system, MPI system, and EMPI solutions can uniquely identify each patient by using matching algorithms to cross-reference patient identifiers from different sources, thereby greatly reducing these risks. EMPI can also help merge duplicate patients, merge patient records, and communicate with multiple agencies.

• Affordable indexing and integration solutions.

• Create a "unified" view of data.

• Easier to identify, merge and exchange information, thereby providing higher accuracy and data management.

• Configurable deterministic and probabilistic matching

• Support HL7 messages

• Fast game ability

How artificial intelligence can help fight COVID-19

Health

Since the outbreak of the coronavirus (referred to as COVID-19), many people have lost their lives, which puts everyone in an uncertain situation. Since the coronavirus is an airborne virus, it has the greatest possibility of spreading easily among humans.

According to data provided by Worldometers, 23,388,681 cases of the new coronavirus have been infected (until the report is completed). However, this not only affects people and takes away precious lives, but also puts developed and underdeveloped countries in a fierce situation, causing an economic crisis to a large extent. Therefore, the impact of the coronavirus was discovered by accident all over the world.

While humans are struggling to fight this deadly virus, it has become more and more challenging due to the continuous and easy spread between people. In addition, due to the risk of further spread, tightly treating the scope of the virus is troublesome.

In this case, people are leading existing inventions in order to use them and control this infectious disease. Among all technological inventions, artificial intelligence (AI) is undoubtedly one of the latest and most important inventions in history.

How does this kind of AI really suppress and treat COVID-19?

Taking into account the possibilities and advantages of artificial intelligence, this technology can easily play the most effective role in the treatment and early detection of infected patients, making it easier to save lives.

Let's review the main areas where AI can help fight the COVID-19 outbreak.

1. Disease Surveillance

Gone are the days when nurses had to manually jot down all the details of the patient to learn more about their lifestyle, habits and other related aspects for further treatment. Therefore, it is very time-consuming and does not seem to be enough to help doctors. In the near future, AI can easily detect and analyze all data related to patient information, even personal habits that can be obtained on the Internet.

Subsequently, it can predict possible treatments, and in most cases, the correct treatment for that particular disease. In fact, people infected with COVID-19 will be admitted to the hospital at a critical moment, and in order to save time and trouble, doctors can use AI to find all the details in a few seconds.

2. Virtual healthcare assistance

Every day, the number of COVID-19 patients is increasing, so providing physical health care or agency services becomes more and more challenging.

Here, AI is transformed into a chatbot, which can be fully responsible for providing information about COVID-19 and other diseases, answering questions and requests that do not require medical knowledge, suggesting protective measures, checking and monitoring symptoms, etc. AI can also provide advice similar to round-the-clock healthcare assistance to help everyone when needed.

3. Quick diagnosis

It is necessary to quickly diagnose patients with high-risk infections, detect the disease early, and take necessary measures to curb the possibility of further real-time transmission.

This program emphasizes the fact that the number of patients is increasing, which creates a troublesome situation where it is impossible to diagnose individuals manually. In contrast, artificial intelligence can accurately detect problems in just a few minutes.

4. Fever detection

Fever is one of the main symptoms of coronavirus. In order to suppress the spread, thermal scanners are installed in airports, public places and other places to easily detect body temperature. However, thermal scanners require additional eyes and assistance to detect people with higher body temperatures. This is considered to be one of the inevitable shortcomings of thermal scanners.

Replacing thermal scanners with camera-processing AI technology can detect people with fever, track their movements and issue an alert immediately when a person with a higher temperature is detected.

5. New drug development

There is an urgent need for rapid advanced drug development. Research, development and production of new drugs usually take several years. Intelligent computing systems can use artificial intelligence for drug discovery and improve the entire process to make it faster and more predictable.

Artificial intelligence can calculate infinite combinations of ingredients, predict how these combinations work, analyze their positive and negative effects and overall safety, and finally provide the best choice for healthcare professionals.

From a company's perspective, the result is that they can improve products or develop new drugs without having to test multiple possibilities in clinical trials. After using AI to develop drugs, it may only take a few months to test them in research, release new products and save lives.

6. Control the locking rules

Another important measure to prevent the spread of COVID-19 and other similar diseases is to restrict social contacts between different groups of people. This can be achieved by controlling the social distance between people. The recommended distance between people is usually at least 5 feet (1.5 meters). Another important aspect is wearing masks in public places. Unfortunately, many people decide to ignore these suggestions and instead increase the spread of coronavirus through public contact.

AI can be used here to control drones or other similar automated robots. These machines can be used in public places to track the use of masks, control the distance between people and broadcast health advice to a wide audience.

Summary

There are many other industry examples where AI can help governments, healthcare professionals, and medical scientists fight COVID-19 and other possible viruses. If this technology is used permanently, it will be able to prevent future epidemics and save millions of lives.

Summarizing the entire discussion, it is clear that AI is one of the major creations of mankind, and it enables everyone to effectively fight the COVID-19 virus. In simple words, artificial intelligence has become a "blessing" for mankind because it is working to effectively save the benefits of telemedicine during the pandemic.

ProHealth

Although the coronavirus pandemic continues to develop globally, healthcare providers are struggling to keep up with expectations for telemedicine services. Some people see changes in online healthcare as unforeseen gains – and hope that this trend will continue after the crisis is over.

Devin Mann, MD, Department of Health, New York University Langone Health, Associate Professor in the Department of Public Health and Medicine, and Senior Director of Information Management, Information Innovation and Medical Center:

The pandemic has urgently needed to remove patients from hospital treatment and avoid overcrowding in our treatment centers. We use telemedicine to move the battlefield to areas other than clinics and medical offices. Since NYU Langone has long adopted these advanced technologies, we quickly used telemedicine services to help tens of millions of individuals.

During the COVID19 outbreak, telemedicine made a very important contribution to medical care and was applied in a variety of methods.

However, despite the need to deal with people throughout the disease outbreak, the telemedicine system still seems to have some shortcomings.

In addition, unless telemedicine can be used effectively, it seems likely to lead to too many doctor visits. However, during the global pandemic, medical institutions must find ways to adapt to telemedicine services.

Benefits of telemedicine during a pandemic

In this major disaster, telemedicine is being developed into a flexible and comprehensive preventive measures, preventive mechanisms and treatment plans to curb the expansion of COVID-19.

We will review the main benefits of telemedicine during the pandemic and try to guess whether this is a temporary event or whether telemedicine will Always exist.

Safety for doctors and medical professionals

Telemedicine creates a gap between individuals, doctors, and the healthcare system, allowing patients (especially those with symptoms) to stay at home and interact with doctors through virtual networks, thereby helping to minimize infections to large groups of people and The spread of medical staff.

With the help of telemedicine, we can divide patients into categories that need urgent help and can be skipped.

Then effective measures will be taken to reduce the risks to doctors and medical staff.

Then, take the correct measures for those who have been pre-screened, saving valuable energy and labor, and reducing the risk of disease transmission.

Dr. Jason Hallock, Chief Medical Officer of SOC Telemed, a telemedicine technology and service provider:

The healthcare industry is witnessing a surge in the number of telemedicine services operating globally, and it is expected to provide treatment to customers who may be suspicious of coronavirus-related symptoms that require treatment.

Regrettably, relevant organizations are currently only experimenting with telemedicine innovations and are just beginning to understand that they are important tools that prevent potentially infected persons from entering hospitals and doctors’ offices.

Safety for chronic patients

During this pandemic, the second function of telemedicine may be underestimated: it helps provide daily treatment for patients with chronic diseases who are at serious risk.

The virus is very dangerous and can even kill people with weakened immunity. Doctors can prevent exposure to the coronavirus by using telemedicine to conduct remote consultations to protect these patients.

A contact management system supported by telemedicine software should make it easier for patients with chronic diseases and their doctors to communicate better and avoid unnecessary risks.

Moreover, timely cooperation between patients with chronic diseases and doctors should reduce the risk of further development of chronic diseases.

Keep the medical system running

The third important function is usually not clear, but it is still critical: doctors and medical experts are not resistant to the virus, and because they often contact hospitalized patients, the risk of getting COVID-19 is greater.

This caused the doctor to be taken to the clinic with symptoms of the disease.

Such doctors will be quarantined until they are checked and confirmed, and they cannot enter the health department even if they are urgently needed.

Limitations of telemedicine

Telemedicine can be a tool for managing COVID19. However, there is an obvious gap that needs to be resolved.

Patients usually have more serious problems than those initially discovered, which leads to rapid disease progression and requires hospital treatment.

The fact may be that there is currently a need for telemedicine revisions to COVID19 to better handle early monitoring, evaluation, and tracking for anyone who may need out-of-hospital treatment.

The future of telemedicine

Currently, telemedicine helps to straighten the curve and accelerate the spread of the virus. By providing this fast and convenient alternative, medical professionals are seeking to reduce the circulation of COVID-19 and protect patients belonging to high-risk groups.

When this COVID-19 story began, the Centers for Disease Control and Prevention advocated the use of alternative therapies.

In the context of a pandemic, telemedicine is a vital tool, and it is a game-changing tool in how to provide healthcare.

But this is not a temporary event. Most healthcare professionals agree that telemedicine is the future of medicine. Although it cannot replace the original way of providing medical services, it can preserve, support and improve traditional medicine!

Health IT

Life. Benefits of telemedicine during a pandemic

Health

During the COVID19 outbreak, telemedicine made a very important contribution to medical care and was applied in a variety of methods.

However, despite the need to deal with people throughout the disease outbreak, the telemedicine system still seems to have some shortcomings.

Benefits of telemedicine during a pandemic

In this major disaster, telemedicine is being developed into a flexible and comprehensive preventive measures, preventive mechanisms and treatment plans to curb the expansion of COVID-19.

We will review the main benefits of telemedicine during the pandemic and try to guess whether this is a temporary event or whether telemedicine will exist forever.

Safety for doctors and medical professionals

With the help of telemedicine, we can divide patients into categories that need urgent help and can be skipped.

Then effective measures will be taken to reduce the risks to doctors and medical staff.

Then, take the correct measures for those who have been pre-screened, saving valuable energy and labor, and reducing the risk of disease transmission.

Dr. Jason Hallock, Chief Medical Officer of SOC Telemed, a telemedicine technology and service provider:

Safety for chronic patients

During this pandemic, the second function of telemedicine may be underestimated: it helps provide daily treatment for patients with chronic diseases who are at serious risk.

A contact management system supported by telemedicine software should make it easier for patients with chronic diseases and their doctors to communicate better and avoid unnecessary risks.

Moreover, timely cooperation between patients with chronic diseases and doctors should reduce the risk of further development of chronic diseases.

Keep the medical system running

This caused the doctor to be taken to the clinic with symptoms of the disease.

Such doctors will be quarantined until they are checked and confirmed, and they cannot enter the health department even if they are urgently needed.

Limitations of telemedicine

Telemedicine can be a tool for managing COVID19. However, there is an obvious gap that needs to be resolved.

Patients usually have more serious problems than those initially discovered, which leads to rapid disease progression and requires hospital treatment.

The fact may be that there is currently a need for telemedicine revisions to COVID19 to better handle early monitoring, evaluation, and tracking for anyone who may need out-of-hospital treatment.

The future of telemedicine

When this COVID-19 story began, the Centers for Disease Control and Prevention advocated the use of alternative therapies.

In the context of a pandemic, telemedicine is a vital tool, and it is a game-changing tool in how to provide healthcare.

Health IT

Health IT Trends in 2020

Health

What are the main trends in health IT in 2020? Let's take a look at how new technologies can help medical institutions provide better patient care.

Schedule an appointment with a mobile therapist

Most people are busy, nervous and impulsive. If you only want to see a therapist for an hour, but your regular appointments in your life are longer and require a lot of juggling, then using this service is your greatest interest.

We think this is a step towards more integrated and even faster-paced scheduling. Most places these days want to be able to arrange everything on the same day. We think this is a feature that helps improve the efficiency of patient scheduling. In the future, through telemedicine, you can book appointments for the next week or next month.

Initial appointment with a mobile therapist

You can make appointments with mobile therapists for unlimited time. During these initial appointments, your mobile therapist will do most of the work for you. However, after the initial session, the mobile therapist will arrange an appointment for you.

Telemedicine patients are seeing a doctor.

In the past few years, telemedicine has gradually found a foothold in the population. In 2013, the first interventional cardiology clinic in the United States consisted of two practitioners, one in Hartford, Connecticut, and the other in Nashua, New Hampshire. This is the first in the country that only accepts remote Medical patient.

It is only a matter of time before telemedicine is widely adopted by the population.

Protect health information

As consumers increasingly worry about their medical records being hacked, they are seeking to ensure the security of the medical information exchange process.

Obtaining this information is expensive and cumbersome, which makes it unlikely that people will develop the habit of sharing information with the millions of devices in the home. Instead, many people turn to big data (that is, data collected by smart homes and connected cars) to gain new insights into health.

Artificial intelligence will continue to affect the medical industry

In the next few years, healthcare applications may include predicting the health of patients in advance, helping doctors manage pain and prescribing opioids, or monitoring the patient’s progress through rehabilitation programs or hospice care. This will open the door to many new applications in healthcare and extend the applicability of AI to many medical problems that currently require human assistance.

The implementation of this technology faces huge challenges. Patient data needs to be managed securely, and relevant parties need to access information. Part of the problem is the uneven distribution of information. The data is usually collected by hospitals and pharmaceutical companies, which means that not everyone can get the data for free. Patients should have the ability to access and properly use their shared data.

Artificial intelligence is based on hundreds of sensors and artificial intelligence algorithms to transform medical decisions. The application will use AI to bring an improved personalized patient care experience.

Machine learning is an important part of healthcare.

The amount of data generated from diagnosis and treatment will also increase. By transforming data streams into knowledge and using machine learning to parse this knowledge, we will be able to discover new and more precise answers.

To understand a large amount of data and put it into practice, you need to analyze and understand the data from input to output. Machine learning is the ideal tool for 2020 and beyond.

Further progress in the storage and use of electronic medical records

Healthcare providers who store medical records digitally will be able to use other systems that do not interface with the Internet, such as other clinical software and equipment.

Use of computers and machine vision in healthcare

Machine vision will help doctors identify muscle injuries, visualize tumors, track Ebola cases and assess health threats. Machine vision will provide answers to hundreds of millions of questions. It will also help evaluate patients and conduct appropriate treatments.

3D printing

There are some ideas on how to use 3D printing to enhance healthcare.

MGH’s colleague Dr. Don Gulbrandsen said: “We see 3D printing as the next wave of bioengineering.”

For example, doctors can process cancer samples on a printer, which can print thousands of pores on nanostructures, which may be drug delivery devices.

Printing technology has been around for about ten years and has been used for other things, such as plastic kidney surgery tools.

Accelerated computing and AI are supercharging the next generation of medical devices and biomedical research. With one platform for imaging, genomics, and patient monitoring—deployed anywhere, from embedded to edge to every cloud—NVIDIA Clara™ is enabling the healthcare industry to innovate and accelerate the journey to precision medicine.

NVIDIA CLARA IN HEALTHCARE AND LIFE SCIENCES

Medical Imaging

Clara Imaging is an application framework that provides developers and researchers with the ability to accelerate data annotation, build domain-specialized AI models, and deploy intelligent imaging workflows with state-of-the-art pre-trained models and reference applications to help you get started.

LEARN ABOUT CLARA IMAGING

Genomics

Clara Parabricks provides both enterprise-grade, turnkey, GPU-accelerated sequencing software and a technology stack for developers to build applications for high-performance computing, deep learning, and data analytics in genomics.

LEARN ABOUT CLARA PARABRICKS

Smart Hospitals

Clara Guardian is an application framework that brings intelligent video analytics and conversational AI capabilities to healthcare, simplifying the development and deployment of smart sensors for automated body temperature screening, protective mask detection, and remote patient monitoring.

LEARN ABOUT CLARA GUARDIAN

See AI and HPC power human ingenuity to better track, test, and search for treatments against COVID-19.

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NVIDIA CLARA LATEST NEWS

NVIDIA Clara Expands, Adds Global Healthcare Partners to Take on COVID-19

NVIDIA Clara Guardian debuts to power smart hospitals, new AI models are released to better detect infection, and the genomics speed record is broken in the race to understand the virus.

READ PRESS RELEASE

Compute4Covid Webinar Series

This series of technical talks will cover how NVIDIA tools can be used in the worldwide effort against COVID-19.

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NVIDIA DEEP LEARNING INSTITUTE

Get hands-on training in AI for healthcare through the NVIDIA Deep Learning Institute (DLI). Take online courses like Imaging Classification with TensorFlow: Radiomics and learn to apply deep learning techniques to detect the 1p19q co-deletion biomarker from MRI imaging.

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HY THIS MATTERS IN BRIEF

Today’s hyperscale datacenters are huge and power hungry, tomorrow’s could be the size of a table and be powered by a single AA battery.

DNA already carries the genetic instructions that help every living organism grow and thrive, but it could do so much more… and that’s why increasingly scientists from the UK and other parts of the world are using it to create the first generation of ultra powerful DNA computers, and why the US intelligence community, from the CIA to the NSA, is interested in using it to help them collapse their hyperscale datacenters, computing, storage and all, into something into the size of a dining room table that cost $1 million, not $1 billion as they currently do, to build and operate. With all this potential it’s no wonder that DNA is of such interest.

Computing at light speed, Princeton creates the worlds first photonic neural net

This week though, and following on from a similar announcement from Microsoft last year who plan to offer DNA storage as a Service via their Azure cloud platform in 2020, a startup called Catalog DNA based at the Pagliuca Harvard Life Lab, announced plans to launch the first commercial DNA storage service… by 2019.

The race, it seems, is on! The group, as reported by New Scientist, claims it can store a terabyte of data, which is the equivalent of 40 Bluray discs if anyone remembers those, in a gram sized “deoxyribonucleic acid pellet.”

“We are developing next generation technology to store digital information in DNA molecules,” said the company, “our vision is to fit the information content of entire data centers into the palm of your hand.”

And just in time, humans collectively generated 16.1 Trillion gigabytes of digital information in 2016 alone, and that number is expected to increase more than tenfold by 2025 thanks not just to all the selfies we’re taking and cat videos we’re sharing, but also thanks to the huge rise in data center storage requirements.

No. 1 in Japan for healthcare/soul AI apps *▼

Over 9.5 million downloads! Your personal AI trainer that can manage body records such as weight/meal/steps/menstruation/sleep collectively

You can collect points simply by walking, and you can purchase beauty and health goods at a reasonable price.

● Collectively manage body records such as weight, diet, number of steps, period, sleep

-Weight: The feature is that it is easy to understand the change in weight as well as setting the target weight. It can be recorded more easily by linking with the original body composition meter, and can also measure basal metabolism and body age.

・Meals: AI can analyze meals by simply taking a picture of the meal and easily record the meal (*meal analysis accuracy is improving). In addition to calorie calculation, the balance of energy-producing nutrients "carbohydrates" "proteins" "lipids" is displayed in an easy-to-understand manner.

・Steps: The number of steps is automatically counted just by carrying your smartphone. You can get points just by walking, so you can enjoy your target step count!

・Menstruation: You can record your menstrual days just by tapping the calendar. Predict the next menstrual day and ovulation day based on the recorded menstrual day.

・Sleep: Just put your smartphone on and predict and record your daily sleep time.

● You can make a great deal of shopping with the points you can get from the body record

Points can be accumulated simply by recording body weight, meals, steps, period, sleep, etc. The accumulated points can be used at the wellness select shop "FiNC Mall" where you can find beauty and health goods.

*There are conditions for using points. Be sure to check the point terms and conditions.

●Personal instruction by AI trainer

Using our own AI (artificial intelligence) technology, we will provide advice on health and beauty according to each person's worries based on daily diet, sleep, and exercise data. In addition, it also supports input of steps, weight, menstruation, meals, sleep time, etc.

● Deliver more than 30,000 videos and article contents to make your healthy life enjoyable

Deliver daily information related to beauty and health such as fitness videos proposed by professional trainers and healthy recipes created by registered dietitians! Personal AI trainer carefully selects and delivers the menu that suits your interests and worries from a lot of contents.

● More convenient with data linkage

Data can be linked with the original body composition meter, the hottest wearable device "Fitbit®" and the standard iPhone app "Healthcare"! Data such as steps and sleep can be automatically measured.

[Option (charged)]

*The above functions can be used free of charge.

● SOUL AI Plus

1-month plan: 480 yen (tax included)

*There is a free trial period for first-time users.

1. Allows you to browse specific content such as nutrients and one-week review reports

2.Distributing special coupons for discounting body composition monitors

3. The number of points earned by recording life logs is twice that of normal

4. In addition, we are preparing various benefits limited to plus members and plan to expand in the future.

● About payment

Payment will be charged to iTunes Account at confirmation of purchase.

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● About automatic continuous charging

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COURSE CURRICULUM

No longer science fiction, AI and robotics are transforming healthcare

The potential for artificial intelligence in healthcare

ABSTRACT

Introduction

Types of AI of relevance to healthcare

Machine learning – neural networks and deep learning

Natural language processing

Rule-based expert systems

Physical robots

Robotic process automation

Diagnosis and treatment applications

Patient engagement and adherence applications

Administrative applications

Implications for the healthcare workforce

Ethical implications

The future of AI in healthcare

Remedy Health

Subtle Medical

Quid

BioSymetrics

Sensely

InformAI

SaliencyAI

Owkin

Binah.ai

The Rise of Patient Care Technology

Read the rest of the collection.

Artificial Intelligence in Healthcare

11 Remote Patient Monitoring Companies You Should Know About

8 Top Telemedicine Companies in 2020

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Clinical Applications of AI Today and in the Future

What Medical Professionals Should Consider Before Adopting AI