Artificial intelligence in healthcare

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X-ray of a hand, with automatic calculation of bone age by a computer software.

Artificial intelligence (AI) in healthcare is the use of algorithms and software to approximate human cognition in the analysis of complex medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input. The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes.[1] AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine,and patient monitoring and care. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center[2], Massachusetts General Hospital[3], and National Health Service,[4] have developed AI algorithms for their departments. Large technology companies such as IBM[5] and Google[4], and startups such as Welltok and Ayasdi,[6] have also developed AI algorithms for healthcare.


Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral.[7] While it was designed for applications in organic chemistry, it provided the basis for the subsequent system MYCIN,[8] considered one of the most significant early uses of artificial intelligence in medicine.[8][9] MYCIN and other systems such as INTERNIST-1 and CASNET did not achieve routine use by practitioners, however.[10]

The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity. During this time there was recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physician.[11] Approaches involving fuzzy set theory,[12] Bayesian networks,[13] and artificial neural networks,[14][15] have been applied to intelligent computing systems in healthcare.

Medical and technological advancements occurring over this half-century period that have simultaneously enabled the growth healthcare-related applications of AI include:

Current Research[edit]

Various specialties in medicine have shown an increase in research regarding AI. However the specialty that has gained the greatest attention is the field of Radiology.[23]


Such an ability to interpret imaging results may allow clinicians to be aided to detect a change in an image that is minute in detail, or something that a clinician may have accidentally missed. Such a study that has incorporated AI in radiology is a study at Stanford which has results presenting that the algorithm that they created can detect Pneumonia better than radiologists[24]. The radiology conference Radiological Society of North America has implemented a large part of its schedule to the use of AI in imaging.


The increase of Telemedicine, has shown the rise of possible AI application.[25] The ability to monitor patients using AI, may allow for the communication of information to physicians if possible disease activity may have occurred. The use of a device such that a person may wear, may allow for constant monitoring of a patient and also for the ability to notice changes that may be less distinguishable by humans.


The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility.[26] Greater health data may allow for more implementation of AI algorithms.[27] The following are examples of large companies that have contributed to AI algorithms for use in healthcare.


IBM's Watson Oncology is in development at Memorial Sloan Kettering Cancer Center and Cleveland Clinic.[28] IBM is also working with CVS Health on AI applications in chronic disease treatment and with Johnson & Johnson on analysis of scientific papers to find new connections for drug development.[29]


Microsoft's Hanover project, in partnership with Oregon Health & Science University's Knight Cancer Institute, analyzes medical research to predict the most effective cancer drug treatment options for patients.[30] Other projects include medical image analysis of tumor progression and the development of programmable cells.[31]


Google's DeepMind platform is being used by the UK National Health Service to detect certain health risks through data collected via a mobile app.[32] A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.[33]


Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.[34]


IDx's first solution, IDx-DR, is the first and only FDA authorized AI system for the autonomous detection of diabetic retinopathy. As an autonomous, AI-based system, IDx-DR is unique in that it makes an assessment without the need for a clinician to also interpret the image or results, making it usable by health care providers who may not normally be involved in eye care. IDx is a leading AI diagnostics company on a mission to transform the quality, accessibility, and affordability of healthcare world-wide.

Medvice provides real time medical advice to clients, who can access and store their Electronic Health Records (EHRs) over a decentralized blockchain[35]. Medvice uses machine learning aided decision making to help physicians predict medical red flags (i.e. medical emergencies which require clinical assistance) before serving them. Predictive Medical Technologies uses intensive care unit data to identify patients likely to suffer cardiac incidents.[28] Modernizing Medicine uses knowledge gathered from healthcare professionals as well as patient outcome data to recommend treatments.[36] "Compassionate AI Lab" uses grid cell, place cell and path integration with machine learning for the navigation of blind people.[37] uses an A.I. Chatbot to connect scheduling EHR systems and automate the confirmation and scheduling of patients.[38]


Digital consultant apps like Babylon in the UK use AI to give medical consultation[39] based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user’s medical history.


While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon potential need for regulations.[40]

Currently no regulations exist specifically for the use of AI in healthcare. In May 2016, the White House announced its plan to host a series of workshops and formation of the National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence.[41] In October 2016, the group published The National Artificial Intelligence Research and Development Strategic Plan, outlining its proposed priorities for Federally-funded AI research and development (within government and academia). The report notes a strategic R&D plan for the subfield of health information technology is in development stages.[42]

The only agency that has expressed concern is the FDA. Bakul Patel, the Associate Center Director for Digital Health of the FDA, is quoted saying in May 2017.

“We're trying to get people who have hands-on development experience with a product's full life cycle. We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”[43]

Government investment[edit]

Investments from the US government in healthcare initiatives that will rely on AI[41] include its $1B proposed budget for the Cancer Moonshot[44] and $215M proposed investment in the Precision Medicine Initiative.[45]

See also[edit]


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  24. ^ Rajpurkar, Pranav; Irvin, Jeremy; Zhu, Kaylie; Yang, Brandon; Mehta, Hershel; Duan, Tony; Ding, Daisy; Bagul, Aarti; Langlotz, Curtis (2017-11-14). "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning". arXiv:1711.05225Freely accessible [cs.CV]. 
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