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The documents address ethics, assessment/evaluation, handling, and regulation of AI for health solutions, covering specific use cases including AI in ophthalmology, histopathology, dentistry, malaria detection, radiology, symptom checker applications, etc. FG-AI4H has established an ad hoc group concerned with digital technologies for health ...
As widespread use of AI in healthcare is relatively new, research is ongoing into its application in various subdisciplines of medicine and related industries. AI programs are applied to practices such as diagnostics, [4] treatment protocol development, [5] drug development, [6] personalized medicine, [7] and patient monitoring and care. [8]
In an effort to examine the efficacy of AI to predict patient outcomes, researchers trained a machine-learning model on data from 51,500 COVID-19 patients, specifically on eight characteristics ...
National Survey on Drug Use and Health Large scale survey on health and drug use in the United States. None. 55,268 Text Classification, regression 2012 [269] United States Department of Health and Human Services: Lung Cancer Dataset Lung cancer dataset without attribute definitions 56 features are given for each case 32 Text Classification 1992
The use of AI in banking began in 1987 when Security Pacific National Bank launched a fraud prevention task-force to counter the unauthorized use of debit cards. [60] Kasisto and Moneystream use AI. Banks use AI to organize operations for bookkeeping, investing in stocks, and managing properties. AI can adapt to changes during non-business ...
In healthcare organizations, AI is able to plan and relocate resources. [37] AI is able to match patients with healthcare providers that meet their needs. AI also helps improve the healthcare experience by using an app to identify patients' anxieties. In medical research, AI helps to analyze and evaluate the patterns and complex data.
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
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