Trustworthy Medical AI: Predictive Modelling, Imaging & Reliability Assessment
Build clinical prediction models, interpret medical images and evaluate AI with confidence.
About This Course
This three-day hands-on workshop introduces participants to the foundations and practical applications of
Artificial Intelligence in Medicine. The programme progresses from machine learning for healthcare data to
deep learning for medical imaging and finally to Generative AI, Retrieval-Augmented Generation (RAG)
and responsible AI in healthcare.
Each 90-minute session is structured with approximately 30 minutes of focused conceptual learning
followed by 60 minutes of guided hands-on implementation. The theoretical component introduces only the
concepts required to understand and perform the corresponding practical activity.
Participants will work with healthcare datasets, medical images and publicly available medical documents
to build and evaluate AI workflows using Python and open-source tools. The workshop also emphasises
interpretation, limitations, reliability, human oversight and responsible use of AI in healthcare.
Aim
To equip researchers, academicians and industry professionals with practical skills to develop, interpret and critically evaluate AI models for clinical data and medical imaging.
Workshop Objectives
Workshop Structure
Workshop Structure
📅 Day 1: Clinical AI Foundations & Predictive Modelling
Focus: Building reproducible machine-learning workflows for clinical data.
- AI in Medicine; AI vs ML vs DL and healthcare data types.
- Clinical problem formulation, supervised learning and classification workflows.
- Training, validation, testing, overfitting and data leakage prevention.
- Clinical outcome definition, predictor availability and dataset limitations.
- Data exploration, missing-value handling, encoding and scaling.
- Reproducible preprocessing using Pipeline and ColumnTransformer.
- Stratified, patient-level, temporal and site-based validation.
- Logistic Regression, XGBoost, cross-validation and model comparison.
- Evaluation metrics: accuracy, precision, recall, sensitivity, specificity and confusion matrix.
- Clinical decision support and responsible interpretation of predictions.
🛠️ Hands-on: Clinical Prediction Modelling
Prepare the UCI Cleveland Heart Disease dataset, build Logistic Regression and XGBoost pipelines, and compare models using training-only cross-validation. Reserve the test set for Day 3, generate cross-validated predictions, calculate performance metrics, identify important features and discuss clinical suitability and leakage risks.
🧰 Tools Covered: Google Colab, Python, pandas, scikit-learn, XGBoost, matplotlib.
📅 Day 2: Medical Imaging & Explainable Deep Learning
Focus: Applying pretrained deep-learning models to medical images and interpreting their predictions.
- Medical imaging modalities, AI applications, classification, detection and segmentation.
- CNNs, visual feature learning, transfer learning and pretrained models.
- Self-supervised learning and foundation models in medical imaging.
- Challenges: limited datasets, class imbalance, overfitting, data leakage and domain shift.
- False positives, false negatives and explainability in medical diagnosis.
- Grad-CAM heatmaps, pretrained chest X-ray models and pathology scores.
- Limitations of visual explanations and AI-assisted versus autonomous diagnosis.
🛠️ Hands-on: Medical Image Prediction & Explainability
Explore PneumoniaMNIST through a guided classification workflow using a pretrained model. Compare predictions with actual labels and examine misclassifications. Use a separate suitable chest X-ray with TorchXRayVision to generate pathology scores and a Grad-CAM heatmap, interpreting results in light of domain shift and visualization limitations.
🧰 Tools Covered: Google Colab, Python, PyTorch, torchvision, MedMNIST, TorchXRayVision, pytorch-grad-cam.
📅 Day 3: Generative AI, Model Reliability, Bias Assessment & Governance
Focus: Building grounded healthcare AI applications and assessing model reliability, explainability and responsible deployment.
- Healthcare applications of Generative AI and LLMs: documentation, literature, patient information, decision support and research.
- Retrieval-Augmented Generation (RAG), grounding and hallucination risks.
- Probability calibration, validation-based thresholds and Brier score.
- Held-out evaluation, ROC-AUC, precision–recall and performance uncertainty.
- SHAP explanations, correlated features, and association versus causation.
- Subgroup assessment, sample-size limitations and model fairness.
- External validation, pre-market evaluation and post-market monitoring.
- Transparency, human oversight, patient-data privacy, WHO/FDA guidance, ethics and responsible deployment.
🛠️ Hands-on: Healthcare RAG Assistant & Clinical AI Reliability Assessment
Build a mini healthcare RAG assistant using public medical documents, embeddings, a searchable vector knowledge base and grounded answer generation. Identify unsupported answers and potential hallucinations. Calibrate the Day 1 model using development data, evaluate the locked model on the reserved test set, generate SHAP explanations, assess subgroup performance using Fairlearn, and draft a clinical AI governance checklist.
🧰 Tools Covered: Google Colab, Python, open-source embedding/LLM components, scikit-learn, CalibratedClassifierCV, SHAP, Fairlearn, pandas, matplotlib.
Who Should Enrol?
Important Dates
Registration Ends
October 15, 2026
IST 5:00 PM
Workshop Dates
October 15, 2026 – October 17, 2026
IST 5:30 PM
Workshop Outcomes
- By the end of the workshop, participants will be able to:
- Define a clinical prediction task and recognise dataset limitations.
- Prepare clinical data while preventing preprocessing leakage.
- Build and compare Logistic Regression and XGBoost pipelines.
- Apply cross-validation and limited-budget hyperparameter optimisation.
- Generate and interpret pretrained chest X-ray model outputs.
- Explain tabular and image predictions using SHAP and Grad-CAM.
- Evaluate discrimination, calibration and decision-threshold trade-offs.
- Examine subgroup and per-class performance without overstating bias conclusions.
- Document findings, validation needs and governance considerations.
Fee Structure
Student
₹1999 | $65
Ph.D. Scholar / Researcher
₹2999 | $75
Academician / Faculty
₹3999 | $85
Industry Professional
₹4999 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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