Program Objectives:

• Master advanced AI techniques to enhance clinical data analysis.
• Develop predictive models that improve patient care and clinical outcomes.
• Implement AI-driven decision support systems in real-world healthcare settings.
• Navigate the ethical and regulatory issues surrounding clinical AI.
• Lead interdisciplinary teams in the adoption and integration of AI technologies in healthcare.

What you will learn?

Week 1: Clinical Data Foundations and Modeling Approaches
Module 1: Foundations of Clinical Data Science

• Chapter 1.1: Clinical Data Types – EHRs, Lab Data, Imaging, Claims

• Chapter 1.2: Data Quality, Standardization (FHIR, HL7), and Governance

• Chapter 1.3: Data Preprocessing for AI: Missingness, Imputation, Labeling

• Chapter 1.4: Ethical and Legal Considerations in Clinical Data Use

Module 2: Predictive Modeling in Healthcare

• Chapter 2.1: Risk Prediction Models (Readmission, Mortality, LOS)

• Chapter 2.2: Feature Engineering for Clinical Use Cases

• Chapter 2.3: Handling Longitudinal and Time-Series Clinical Data

• Chapter 2.4: Evaluation Metrics: AUROC, Precision/Recall, Calibration

Week 2: Deep Learning, NLP, and Imaging in Clinical Contexts
Module 3: Advanced Modeling Techniques

• Chapter 3.1: Deep Neural Networks for Structured EHR Data

• Chapter 3.2: Time-Series Models: RNNs, LSTMs, Transformers in Healthcare

• Chapter 3.3: Multi-modal Models: Combining Text, Labs, and Images

• Chapter 3.4: Transfer Learning and Pretrained Models in Clinical Tasks

Module 4: Clinical NLP and Imaging AI

• Chapter 4.1: Information Extraction from Clinical Notes

• Chapter 4.2: Named Entity Recognition and ICD Code Prediction

• Chapter 4.3: Clinical Imaging Models: Radiology, Pathology, Ophthalmology

• Chapter 4.4: Annotating and Validating NLP/Imaging Models

Week 3: Deployment, Fairness, and Real-World Integration
Module 5: AI Deployment in Health Systems

• Chapter 5.1: Integrating Models into Clinical Workflows

• Chapter 5.2: Model Monitoring, Drift Detection, and Retraining

• Chapter 5.3: Clinical Decision Support and User Interface Design

• Chapter 5.4: Validation in Multi-Site and Real-World Settings

Module 6: Fairness, Interpretability, and Governance

• Chapter 6.1: Bias in Clinical AI: Causes, Detection, and Mitigation

• Chapter 6.2: Explainability in Clinical Predictions

• Chapter 6.3: Regulatory Pathways: FDA, HIPAA, and AI Oversight

• Chapter 6.4: Capstone – Design a Clinical AI Model Pipeline with Governance Plan