AI in Healthcare: Navigating the Myths, Biases, and Regulation

Jessica Morley, postdoctoral researcher at the Yale Digital Ethics Center joins Faces of Digital Health to discuss the overlooked aspects of AI, including biases, ethical issues, and common misconceptions. AI is not new, magical, or truly intelligent; its roots in healthcare trace back 30 years. While AI is often seen as infallible, it is merely statistical prediction and lacks semantic understanding, which excludes it from genuinely grasping human concepts like pain. Jessica remains skeptically optimistic about AI, noting its potential in scientific advancements and operational efficiency but doubting its transformative impact on diagnostics and patient care. The conversation also highlights the regulatory landscapes in Europe, the UK, and the US, each with varying approaches to AI regulation. Jessica emphasizes the unavoidable nature of bias in AI, advocating for more thoughtful adoption and regulation to ensure AI benefits everyone without deepening biases or relying excessively on surveillance. The discussion concludes with insights into ongoing research aimed at shifting focus from individual health optimization to maximizing population health. 00:00 Introduction to Digital Health and AI 02:21 Debunking Myths About AI 06:13 The Potential and Limitations of AI in Healthcare 11:10 Global Regulatory Landscape for AI in Healthcare 19:36 The Impact of AI on Personalized Medicine 34:41 Addressing Bias and Ethical Concerns in AI 38:39 Future Research and Conclusion

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