Is there a medical chatbot in the house?

ChatGPT has shown promise in summarizing patient records, making diagnoses for certain diseases, and providing initial treatment recommendations. And the use of AI “doctors” has also begun to support mental health care. But much more testing and refinement is needed before the medical community could consider rolling it out at a large scale, particularly in under-resourced countries where some researchers believe its use could close gaps in access to the diagnosis and treatment of common diseases.

NYU data scientist Ruopeng An is a leading researcher on how AI could address public health disparities, and he and colleagues recently put ChatGPT-as-doctor through a battery of tests. Feeding it with straight-forward input from a series of mock patients, they set out to determine whether it could detect diabetes, hypertension, and tuberculosis and other illnesses accurately, and if it could suggest appropriate meds or not.

“The future,” he says, “holds exciting possibilities, with AI improving access to care, especially in underserved regions, enhancing accuracy in diagnostics, and supporting overburdened healthcare providers by automating routine tasks. Yet for this transformation to be truly revolutionary, we must address challenges around accuracy, safety, and ethical use, especially in low-resource settings.”

In a recent Journal of Medical Internet Research study, An and research collaborators found ChatGPT has promise as a way to provide a doctor’s or mental health counselor’s patient with an initial diagnosis. In the experiment, ChatGPT showed a strong success rate, providing appropriate medical recommendations in a majority of the instances when a patient’s symptoms were inputted.

The testing was repeated for nine different disease types in all, a total of 27 patient/Chatbot interactions. But one caveat was notable: even when it offered up a correct diagnosis, ChatGPT recommended medications that were unnecessary or potentially harmful. This happened in more than half the cases.

An, who studies AI’s potential to improve public access to advice and care in places of the world where treatment is hard to get, said more work clearly needs to be done. Until then, AI’s dependability in important areas of health care are likely to remain uncertain.

An’s growing body of work on the future uses for AI to reduce social disparities has, in part, made him a leading expert in the epidemiology of obesity. His previous study explored the potential of machine learning and deep neural networks to predict and assess a patient’s obesity progression from short audio recordings. As a faculty member at NYU’s Silver School of Social Work, he holds a Martin Silver Endowed Professorship in Data Science and Prevention and directs the Constance and Martin Silver Center on Data Science and Social Equity.

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