Society needs to “consider the best ways for doctors and AI to work together, and how medical roles will change as a consequence”, argue EffyVayena and ProfADMorris in a guest essay
has caused particular delight since it first appeared in November. But more specialisedis already used widely in medical settings, including in radiology, cardiology and ophthalmology. Major developments are in the pipeline. Med-Pafirm owned by Alphabet, is another large language model. Its 540bn parameters have been trained on data sets spanning professional medical exams, medical research and consumer health-care queries.
First, there is an urgent need to co-ordinate expertise internationally to fill the governance vacuum.tools will be used in more and more countries, so regulators should start co-operating with each other now. Regulators proved during the pandemic that they can move together and at pace.
Second, governance approaches must be adaptable. In the pre-licensing phase, regulatory sandboxes would help to develop needed agility. They can be used to determine what can and ought to be done to ensure product safety, for example. But a variety of concerns, including uncertainty about the legal responsibilities of businesses that participate in sandboxes, means this approach is not used as often as it should be.
Third, new business and investment models are needed for co-operation between technology providers and health-care systems. The former want to develop products, the latter manage and analyse troves of high-resolution data. Partnerships are inevitable and have been tried in the past, with some notable failures.Watson, a computing system launched with great fanfare as a “moonshot” to help improve medical care and support doctors in making more accurate diagnoses, has come and gone.
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