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ABOUT THE COURSE:
Artificial intelligence is being adopted across clinical settings at a rapid pace creating a need for rigorous understanding of the foundations and applications of these methods for clinical practitioners and decision makers.
This course provides a systematic introduction to AI for clinicians, covering the intuitions underlying modern deep learning. It covers the principal model architectures encountered in healthcare, and the data considerations that determine what AI systems can and cannot do.
Topics progress from supervised learning and deep neural networks through generative models and agentic systems, with sustained attention to evaluation methodology, algorithmic bias, explainability, and regulatory frameworks.
The course is intended for clinical practitioners and decision makers seeking both the conceptual literacy to critically appraise AI in the literature and in practice, and the decision-making vocabulary required for institutional roles in AI adoption and governance.
INTENDED AUDIENCE:MBBS students, postgraduate medical trainees, practicing clinicians, clinician-researchers, and healthcare administrators who want a rigorous but non-mathematical introduction to AI in clinical practice, evaluation, adoption, and governance.
PREREQUISITES: Should have clinical background. Ideally, at least a 2nd year medical student. Ideally must have done a biostatistics course
INDUSTRY SUPPORT: Healthcare, AI startups in Healthcare, Medical device companies
Artificial intelligence is being adopted across clinical settings at a rapid pace creating a need for rigorous understanding of the foundations and applications of these methods for clinical practitioners and decision makers.
This course provides a systematic introduction to AI for clinicians, covering the intuitions underlying modern deep learning. It covers the principal model architectures encountered in healthcare, and the data considerations that determine what AI systems can and cannot do.
Topics progress from supervised learning and deep neural networks through generative models and agentic systems, with sustained attention to evaluation methodology, algorithmic bias, explainability, and regulatory frameworks.
The course is intended for clinical practitioners and decision makers seeking both the conceptual literacy to critically appraise AI in the literature and in practice, and the decision-making vocabulary required for institutional roles in AI adoption and governance.
INTENDED AUDIENCE:MBBS students, postgraduate medical trainees, practicing clinicians, clinician-researchers, and healthcare administrators who want a rigorous but non-mathematical introduction to AI in clinical practice, evaluation, adoption, and governance.
PREREQUISITES: Should have clinical background. Ideally, at least a 2nd year medical student. Ideally must have done a biostatistics course
INDUSTRY SUPPORT: Healthcare, AI startups in Healthcare, Medical device companies
