Artificial Intelligence in Healthcare - A Comprehensive Account
Improving Healthcare Data Security with AI
In a perspective piece, Stanford researchers discuss the ethical implications of using machine-learning tools in making health care decisions for patients. Artificial intelligence is hard at work crunching health data to improve diagnostics and help doctors make better decisions for their patients. But researchers at the Stanford University School of Medicine say the furious pace of growth in the development of machine-learning tools calls for physicians and scientists to carefully examine the ethical risks of incorporating them into decision-making. In a perspective piece published March 15 in The New England Journal of Medicine , the authors acknowledged the tremendous benefit that machine learning can have on patient health. David Magnus , PhD, senior author of the piece and director of the Stanford Center for Biomedical Ethics , said bias can play into health data in three ways: human bias; bias that is introduced by design; and bias in the ways health care systems use the data.
Case Study 8: The use of Artificial Intelligence in Healthcare
Management and Leadership. The course explores types of AI technology, its applications, limitations, and industry opportunities. The potential of artificial intelligence AI to transform health care — through the work of both organizational leaders and medical professionals — is increasingly evident as more real-world clinical applications emerge. As patient data sets become larger, manual analysis is becoming less feasible. AI has the power to efficiently process data far beyond our own capacity, and has already enabled innovation in areas including chemotherapy regimens, patient care, breast cancer risk, and even ICU death prediction.
Skip to main content. Essential reports and perspectives. Future-proofing AI: Embrace machine learning now because healthcare adoption is picking up speed. Hospitals are proving the merits of machine learning and cognitive computing, but deep learning and