Artificial intelligence (AI) in healthcare uses algorithms and software to approximate human cognition in the analysis of complex medical data. The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. 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, among others. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center and National Health Service, multinational technology companies such as IBM and Google and startups such as Welltok and Ayasdi, have created solutions currently used in the industry. Healthcare remains the top area of investment in AI as measured by venture capital deal flow.
Video Artificial intelligence in healthcare
History
Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral. While it was designed for applications in organic chemistry, it provided the basis for the subsequent system MYCIN, considered one of the most significant early uses of artificial intelligence in medicine,. MYCIN and other systems such as INTERNIST-1 and CASNET did not achieve routine use by practitioners however.
The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity, as well as the 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 users. New approaches involving fuzzy set theory, Bayesian networks and artificial neural networks, were created to reflect the evolved needs of 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:
- Improvements in computing power resulting in faster data collection and data processing
- Increased volume and availability of health-related data from personal and healthcare-related devices
- Growth of genomic sequencing databases
- Widespread implementation of electronic health record systems
- Improvements in natural language processing and computer vision, enabling machines to replicate human perceptual processes,
Maps Artificial intelligence in healthcare
Examples
IBM
IBM's Watson Oncology is in development at Memorial Sloan Kettering Cancer Center and Cleveland Clinic. 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.
Microsoft
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. Other projects include medical image analysis of tumor progression and the development of programmable cells.
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. A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
Intel
Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.
Startups
Predictive Medical Technologies uses intensive care unit data to identify patients likely to suffer cardiac incidents. Modernizing Medicine uses knowledge gathered from healthcare professionals as well as patient outcome data to recommend treatments. Nimblr.ai uses an A.I. Chatbot to connect scheduling Electronic health record systems and automate the confirmation and scheduling of patients.
Regulation
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. 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.
Investments from the US government in healthcare initiatives that will rely on AI include its $1B proposed budget for the Cancer Moonshot and $215M proposed investment in the Precision Medicine Initiative.
See also
- Clinical decision support system
- Computer-aided diagnosis
- Computer-aided simple triage
- IBM Watson Healthcare
- DeepMind Healthcare
- Speech recognition software in healthcare
References
Source of article : Wikipedia
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