Course12710600626 lectures
AI for Clinicians
Prof. Balaji Srinivasan and Prof. Ganapathy Krishnamurthi · IIT Madras
About
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
Lectures
Week 1
- What Is AI? An overview
- The AI Continuum I: Rules to Statistics
- The AI Continuum II: Machine Learning to Foundation Models
- What Does the Model See? Encodings and representations
Week 2
- Mathematical Functions and Learning from Data
- Regression, Classification, and Types of Learning
- Generalisation: Learning Beyond the Training Data
- Neural Networks as Flexible Learned Functtions
Week 3
- Learning Features and Representations
- Convolutional Neural Networks for Medical Images
- Medical Image Tasks: Classification, Detection, Segmentation and Regression
Week 4
- Modeling Sequences with RNNs
- Tokens and Embeddings
- The Attention Mechanism: Deciding What Context Matters
- The Transformer Architecture and Large Language Models
Week 5
Week 6
Week 7
Week 8
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Madras
- Department
- Multidisciplinary
- Taught by
- Prof. Balaji Srinivasan, Prof. Ganapathy Krishnamurthi
- Level
- Graduate
- Language
- English
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed