The potential of artificial intelligence to bring equity in health care
Researchers at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), recently hosted the AI for Health Care Equity Conference to assess current state-of-the-art work in this space, including new machine learning techniques that support fairness, personalization, and inclusiveness; identify key areas of impact in health care delivery; and discuss regulatory and policy implications. Nearly 1,400 people virtually attended the conference to hear from thought leaders in academia, industry, and government who are working to improve health care equity and further understand the technical challenges in this space and paths forward.
During the event, Regina Barzilay, the School of Engineering Distinguished Professor of AI and Health and the AI faculty lead for Jameel Clinic, and Bilal Mateen, clinical technology lead at the Wellcome Trust, announced the Wellcome Fund grant conferred to Jameel Clinic to create a community platform supporting equitable AI tools in health care. The project’s ultimate goal is not to solve an academic question or reach a specific research benchmark, but to actually improve the lives of patients worldwide. Researchers at Jameel Clinic insist that AI tools should not be designed with a single population in mind, but instead be crafted to be reiterative and inclusive, to serve any community or subpopulation. To do this, a given AI tool needs to be studied and validated across many populations, usually in multiple cities and countries. Also on the project wish list is to create open access for the scientific community at large, while honoring patient privacy, to democratize the effort.
This call to action is a response to health care in 2020. At the conference, Collin Stultz, a professor of electrical engineering and computer science and a cardiologist at Massachusetts General Hospital, spoke on how health care providers typically prescribe treatments and why these treatments are often incorrect.
One of the largest questions of the conference, and of AI in general, revolves around policy. Kadija Ferryman, a cultural anthropologist and bioethicist at New York University, points out that AI regulation is in its infancy, which can be a good thing. Even before policy comes into play, there are certain best practices for developers to keep in mind. Najat Khan, chief data science officer at Janssen R&D, encourages researchers to be “extremely systematic” when choosing datasets. Even large, common datasets contain inherent bias. Even more fundamental is opening the door to a diverse group of future researchers.