A bioethicist and a professor of medicine on regulating AI in health care

THE ARTIFICIAL INTELLIGENCE (AI) sensation ChatGPT, and rivals such as BLOOM and Stable Diffusion, are large language models for consumers. ChatGPT has caused particular delight since it first appeared in November. But more specialised AI is already used widely in medical settings, including in radiology, cardiology and ophthalmology. Major developments are in the pipeline. Med-PaLM, developed by DeepMind, the AI firm 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. Such technology means our societies now need to consider the best ways for doctors and AI to best work together, and how medical roles will change as a consequence.

The benefits of health AI could be vast. Examples include more precise diagnosis using imaging technology, the automated early diagnosis of diseases through analysis of health and non-health data (such as a person’s online-search history or phone-handling data) and the immediate generation of clinical plans for a patient. AI could make care cheaper as it enables new ways to assess diabetes or heart-disease risk, such as by scanning retinas rather than administering numerous blood tests, for example. AI has the potential to alleviate some of the challenges left by covid-19. These include drooping productivity in health services and backlogs in testing and care, among many other problems plaguing health systems around the world.

For all the promise of AI in medicine, a clear regime is badly needed to regulate it and the liabilities it presents. Patients must be protected from the risks of incorrect diagnoses, the unacceptable use of personal data and biased algorithms. They should also prepare themselves for the possible depersonalisation of health care if machines are unable to offer the sort of empathy and compassion found at the core of good medical practice. At the same time, regulators everywhere face thorny issues. Legislation will have to keep pace with ongoing technological developments—which is not happening at present. It will also need to take account of the dynamic nature of algorithms, which learn and change over time. To help, regulators should keep three principles in mind: co-ordination, adaptation and accountability.