Can Jack-Of-All-Trades AI Reshape Medicine?
Researchers chart course for the design, testing, and implementation of next-gen AI in medicine
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At a glance:
- Most medical AI models in use today are trained to perform one or two specific tasks and have limited utility.
- Next-generation AI — called generalist medical AI — incorporates various types of data to perform a variety of complex tasks in a range of clinical scenarios.
- Generalist medical AI can reshape medicine by augmenting clinical decision-making, real-time surgical and bedside support, and more.
The vast majority of AI models used in medicine today are “narrow specialists,” trained to perform one or two tasks, such as scanning mammograms for signs of breast cancer or detecting lung disease on chest X-rays.
But the everyday practice of medicine involves an endless array of clinical scenarios, symptom presentations, possible diagnoses, and treatment conundrums. So, if AI is to deliver on its promise to reshape clinical care, it must reflect that complexity of medicine and do so with high fidelity, says Pranav Rajpurkar, assistant professor of biomedical informatics in the Blavatnik Institute at HMS.
Enter generalist medical AI, a more evolved form of machine learning capable of performing complex tasks in a wide range of scenarios.
Akin to general medicine physicians, Rajpurkar explained, generalist medical AI models can integrate multiple data types — such as MRI scans, X-rays, blood test results, medical texts, and genomic testing — to perform a range of tasks, from making complex diagnostic calls to supporting clinical decisions to choosing optimal treatment. And they can be deployed in a variety of settings, from the exam room to the hospital ward to the outpatient GI procedure suite to the cardiac operating room.