The potential of artificial intelligence to bring equity in health care has spurred significant research efforts. Racial, gender, and socioeconomic disparities have traditionally afflicted health care systems in ways that are difficult to detect and quantify. New AI technologies, however, are providing a platform for change.
Regina Barzilay, the School of Engineering Distinguished Professor of AI and Health and faculty co-lead of AI for the MIT Jameel Clinic; Fotini Christia, professor of political science and director of the MIT Sociotechnical Systems Research Center; and Collin Stultz, professor of electrical engineering and computer science and a cardiologist at Massachusetts General Hospital — discuss here the role of AI in equitable health care, current solutions, and policy implications. The three are co-chairs of the AI for Healthcare Equity Conference, taking place April 12.
Q: How can AI help address racial, gender, and socioeconomic disparities in health-care systems?
Stultz: Many factors contribute to economic disparities in health care systems. For one, there is little doubt that inherent human bias contributes to disparate health outcomes in marginalized populations. Although bias is an inescapable part of the human psyche, it is insidious, pervasive, and hard to detect. Individuals, in fact, are notoriously poor at detecting preexisting bias in their own perception of the world — a fact that has driven the development of implicit association tests that allow one to understand how underlying bias can affect decision-making.
AI provides a platform for the development of methods that can make personalized medicine a reality, thereby ensuring that clinical decisions are made objectively with the goal of minimizing adverse outcomes across different populations. Machine learning, in particular, describes a set of methods that help computers learn from data. In principle, these methods can offer unbiased predictions that are based only on objective analyses of the underlying data.
Unfortunately, however, bias not only affects how individuals perceive the world around them, it also influences the datasets we use to build models. Observational
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