Further Reading — Chapter 15: AI in Healthcare

The Equity Challenge

Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S. "Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations." Science, 366(6464), 447–453, 2019. The landmark study documenting how a widely used healthcare algorithm systematically disadvantaged Black patients by using healthcare costs as a proxy for healthcare needs. Essential reading for understanding proxy variable bias. Tier: Primary — the most important single paper for this chapter.

Adamson, A. S. and Smith, A. "Machine Learning and Health Care Disparities in Dermatology." JAMA Dermatology, 154(11), 1247–1248, 2018. A concise analysis of how the underrepresentation of darker skin tones in dermatological training datasets creates performance disparities in AI skin analysis systems. Tier: Recommended — directly addresses a core equity concern.

Vyas, D. A., Eisenstein, L. G., and Jones, D. S. "Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms." New England Journal of Medicine, 383(9), 874–882, 2020. Examines how race-based corrections in clinical algorithms — beyond AI specifically — can perpetuate health disparities. Provides essential context for understanding the broader landscape of algorithmic bias in medicine. Tier: Recommended — broadens the frame beyond AI-specific bias.

Evidence and Effectiveness

Liu, X., et al. "A Comparison of Deep Learning Performance Against Health-Care Professionals in Detecting Diseases from Medical Imaging: A Systematic Review and Meta-Analysis." The Lancet Digital Health, 1(6), e271–e297, 2019. The systematic review discussed in Section 15.2, which found that most diagnostic AI studies are retrospective and few compare AI to clinicians under real-world conditions. Essential for calibrating expectations about healthcare AI evidence. Tier: Primary — grounds the evidence discussion.

Topol, E. J. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2019. A leading cardiologist's argument that AI's greatest value in medicine may be in freeing physicians from administrative burden, giving them more time for the human connection that is central to good care. Optimistic but thoughtful. Tier: Recommended — accessible book-length treatment of healthcare AI.

Rajpurkar, P., et al. "AI in Health and Medicine." Nature Medicine, 28, 31–38, 2022. A comprehensive review of the state of AI in healthcare, covering diagnostics, therapeutics, and health system management. Balanced assessment of progress and challenges. Tier: Recommended — excellent overview for readers wanting broader context.

Trust, Transparency, and Clinical Integration

Gaube, S., et al. "Do As AI Say: Susceptibility in Deployment of Clinical Decision-Making." npj Digital Medicine, 4, 31, 2021. Research on automation bias in clinical settings, documenting how physicians' reliance on AI recommendations can sometimes degrade rather than improve diagnostic accuracy. Tier: Recommended — empirical evidence for a key concern.

Amann, J., et al. "Explainability for Artificial Intelligence in Healthcare: A Multidisciplinary Perspective." BMC Medical Informatics and Decision Making, 20, 310, 2020. A multi-stakeholder analysis of the explainability challenge in healthcare AI, including perspectives from clinicians, patients, developers, and ethicists. Tier: Supplementary — deepens the transparency discussion.

Regulation

Muehlematter, U. J., Daniore, P., and Vokinger, K. N. "Approval of Artificial Intelligence and Machine Learning-Based Medical Devices in the USA and Europe (2015–20): A Comparative Analysis." The Lancet Digital Health, 3(3), e195–e203, 2021. A comparative analysis of FDA and European regulatory approaches to AI medical devices, including the types of devices cleared and the evidence required. Tier: Recommended — essential for understanding the regulatory landscape.

U.S. Food and Drug Administration. "Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices." FDA website, updated regularly. The FDA's own resource page listing all cleared AI/ML medical devices and the agency's evolving regulatory framework. Useful as a primary source for understanding current policy. Tier: Supplementary — reference resource.

Mental Health and AI

Fitzpatrick, K. K., Darcy, A., and Vierhile, M. "Delivering Cognitive Behavior Therapy to Young Adults with Symptoms of Depression via a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial." JMIR Mental Health, 4(2), e19, 2017. One of the first randomized controlled trials of a therapeutic chatbot, finding significant symptom reduction in college students using Woebot. Small and short-term, but methodologically important. Tier: Supplementary — for readers interested in the mental health AI discussion.

Books for Deeper Exploration

Benjamin, R. Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press, 2019. While not focused exclusively on healthcare, Benjamin's analysis of how technology encodes racial inequality provides essential theoretical grounding for understanding the equity challenges in medical AI. Tier: Recommended — powerful framework for the equity discussion.

Mukherjee, S. The Song of the Cell: An Exploration of Medicine and the New Human. Scribner, 2022. A Pulitzer Prize-winning physician's exploration of the frontier of medicine, including AI's role. Provides humanistic context for the technological discussion. Tier: Supplementary — broader medical context.

World Health Organization. Ethics and Governance of Artificial Intelligence for Health. WHO, 2021. The WHO's comprehensive guidance on healthcare AI, with a global perspective that extends beyond the U.S. and European focus of much of the literature. Freely available online. Tier: Recommended — global governance perspective.