One of the more intriguing capabilities of artificial intelligence in medicine is the possibility that AIs can predict patient outcomes. I’d like to discuss three such recent developments.
My first example is the suggestion by health policy experts Michael Millenson and Jennifer Goldsack that AI systems such as ChatGPT and Google’s Bard may soon be able to collate data from disparate sources and let patients know which doctors and hospitals might yield the highest chances of treatment success for their specific problems
: “You can identify the Chicago surgeon who does the most knee replacements and his infection rate, find the survival figures for breast cancer patients at a renowned Los Angeles medical center, or get recommendations for cardiac surgeons in New York City.” Of course, such recommendations would only be good as the reliability of the underlying data on patient survival rates or physicians’ complication rates. Also, complication rates by themselves do not necessarily reflect the competence of the physician. A physician who sees sicker patients or a surgeon who tackles more challenging cases may have a higher complication rate than others who do not.