AI for Medical Professionals 2026
AI for Medical Professionals – 2026
A 4-module program designed for medical students, clinicians, and faculty to understand, evaluate, and apply AI/ML in healthcare settings.
Program Details
Objectives
- Providing a foundational understanding of AI and its relevance to healthcare.
- Familiarizing participants with the applications of AI techniques in clinical practice.
- Fostering awareness of ethical and governance issues related to AI in medicine.
Target Group
- Postgraduate medical students (MD/MS/MDS/Equivalent).
- Faculty and medical professionals (MD/MS/MCH/DM/MDS/MBBS/BDS).
Eligibility
- Basic knowledge of healthcare processes and clinical practice.
- Curiosity for understanding the impact of modern technology in healthcare.
Expected Outcomes
- Understanding of AI concepts and their applications in healthcare.
- Ability to evaluate AI tools in clinical settings.
- Hands-on experience with AI tools and basic model development.
- Improved ability to critically assess AI research and technologies.
- Enhanced ability to work collaboratively on AI projects in healthcare.
Previous Iterations
- This announcement is for the third improvised iteration for Medical Professionals.
- The first iteration in June 2025, had participants from 110 academic and clinical institutions in India.
- The second iteration in December 2025, saw participants from 141 academic and clinical institutions in India.
Course Content
Comprehensive coverage from AI basics to clinical applications and ethics.
Module 1: Introduction to Artificial Intelligence (AI) in Healthcare
- Clinical data sets: Data sources and types (structured, unstructured); Standards in data acquisition and management; Opportunities and challenges in data handling
- The role of smart and intelligent systems in clinical workflow: Traditional vs. intelligent systems in healthcare; concepts of intelligence and smartness
- Recent Inroads / Trends of AI in Healthcare: Landmark applications in healthcare
Module 2: Basics of Machine and Deep Learning (ML & DL)
- Introduction to AI: Definition, history, evolution, and applications
- Learning paradigms: Supervised (classification, regression), Unsupervised (clustering), Reinforcement Learning
- The ML Pipeline: Feature extraction, selection, dimensionality reduction; Model building, validation, and evaluation metrics
- Recent Trends: Generative AI, Foundational Models, ChatGPT
- ML & DL Algorithms: Tree-based methods (decision trees, random forests), Neural Networks (MLP, SVM), Hierarchical Clustering, Deep Neural Networks (CNNs, RNNs, Transformers)
Module 3: Clinical Applications
- Case Study Framework: Clinical presentation & AI/ML role; Data prep & feature processing; Model building & evaluation; Results & inference
- Domains (any 4): Screening, Diagnosis, Prognosis, Treatment, Patient Management, Hospital Resource Management
- Specific Case Studies: Different data types (biosignals, images, tabular, molecular, textual) with relevant algorithms (tree-based, NN, DNN)
- Signal-based – ECG (cardiovascular diseases), EEG (sleep staging)
- Applications by Data Type:
- Image-based – Retinal Scans (Diabetic Retinopathy), Chest X-ray (Pneumonia/Covid)
- Tabular – EHRs (Covid, Sepsis)
- Molecular – Genomics (Cancer), Structural Biology (Drug Discovery)
- Text – Medical Q&A systems, ChatGPT Chatbots for Physical & Mental Health
Module 4: Ethics and Governance of AI
- Data protection, privacy, anonymity, biases – Regulations and governance frameworks for AI as a medical device
Hands-on AI Projects (Optional)
- Building a basic machine learning model
- Simple AI projects relevant to healthcare
Course Delivery & Certification
Lectures
Pre-recorded lectures (~1 hour, delivered as 4 videos of 15 minutes each) released weekly for flexible self-paced learning.
Online Contact Sessions
Weekly 1-hour live sessions covering lecture summaries, tutorials, Q&A, and demonstration of case studies.
Demo of AI Applications
Simple AI applications relevant to healthcare demonstrated during live sessions.
Study-time
Participants are expected to dedicate around 3 hours per week.
Assessment
Short quizzes/assignments after each video module and a post-course assessment to measure learning outcomes.
Project Work
Participants gain a unique opportunity to apply their learning through hands-on projects using real-world medical datasets. Guided by expert faculty and dedicated TAs, they design, build, and evaluate AI/ML models in clinically meaningful scenarios, gaining practical experience, technical confidence, and a portfolio-ready project.
Certification
Certificate of course completion will be jointly issued by IIIT-H, IHub-Data, & NAMS.
Pre-Registration
Secure your seat for the AI for Medical Professionals 2026 program.
Pre-Registration
Those who are interested in pre-booking their seat for the course can register with an advance payment.
- Pre-booking Amount: ₹3,000/-
- Seat confirmation prior to full enrollment
- Balance course fee payable before course commencement
Registered Participants
Coordinators
Course Instructors
Teaching Assistants
