Generative AI in Healthcare: Responsible Decision Making

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This course equips you with critical skills to work safely and effectively with generative AI in healthcare. Through realistic scenario-based learning, you'll master effective prompting strategies, evaluate AI outputs for bias and hallucinations, and crucially, learn when to trust AI and when human judgment must override it. Whether you're a healthcare student, clinician, manager, or policy professional, you'll discover how to harness AI as a tool for better decision-making without surrendering clinical expertise or ethical responsibility. Using concrete scenarios in clinical planning, resource allocation, and risk assessment, you'll explore the principles behind different AI tools and recognise their limitations in complex, real-world healthcare contexts. What makes this course unique is its focus on hybrid human-AI decision-making. Rather than viewing AI as a replacement for human judgment, you'll learn to design safer, more equitable workflows that combine AI's analytical power with human insight, accountability, and ethical reasoning. You'll also examine emerging issues: data privacy, AI washing, representation gaps, and responsible use across global contexts. By course completion, you'll have practical frameworks for evaluating AI systems critically and the confidence to advocate for responsible, human-centred AI adoption in healthcare. Why take this course now? Globally, nearly half of clinicians reported using AI for work-related purposes in 2025, with adoption rates in some specialities exceeding 60 per cent in the United States. This rapid adoption means healthcare professionals urgently need critical frameworks to evaluate, oversee, and ethically integrate AI into their practice. Source: Statista. AI in healthcare: statistics & facts, 2026. Available from: https://www.statista.com/topics/10011/ai-in-healthcare/