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Semester VIII · PCI syllabus 2026
Ethical Considerations and Translational Applications of AI in Pharmacy
BP801T
2 credits
Theory
Coming soon
Unit 1: AI Lifecycle, Validation & Model Auditing
- Overview of AI system lifecycle: data collection, preprocessing, modeling, validation, deployment, and monitoring
- Importance of data quality in healthcare datasets
- Training vs testing vs validation datasets
- Cross-validation (conceptual understanding)
- Model drift and performance degradation, data leakage in modeling
- Documentation practices: Documentation and reproducibility
- Basics of model auditing
Unit 2: Regulatory Framework & Explainable AI
- Overview of AI in regulatory submissions
- Explainable AI (XAI) concept
- Transparency and interpretability
- Overview of regulatory guidance on AI (EU AI Act, FDA/ CDSCO frameworks)
- Accountability in automated decision systems
- Risk-based classification of AI systems
Unit 3: AI in in Pharmacy Automation & Supply Chain
- Overview of AI in automated dispensing systems
- Inventory prediction models
- Case studies on regression-based demand forecasting, different forecasting methods
- Medication adherence monitoring systems
- AI in supply chain risk prediction
- Predictive analytics in pharmaceutical logistics
- Advantages, limitations, legal & privacy considerations of AI in pharmacy automation
Unit 4: AI in in Public Health & Real-World Data Analytics
- Overview of real-world data sources (EHR, claims, surveillance systems)
- Conceptual knowledge of AI in outbreak prediction, Population-level risk modeling
- Regression models in epidemiology
- AI in vaccination forecasting
- Case studies: AI applications in epidemiological trend analysis (e.g. COVID-19 vaccination hesitancy, diabetes prevalence forecasting, antibiotic resistance trends etc.)
Unit 5: Guided Project - Translational AI in Pharmacy
- Students implement a supervised ML model (regression, logistic regression, or others) using real-world pharmacy data from domains like formulation, pharmacokinetics (PK), ADR detection, quality control (QC), automation, or public health. They validate the model, analyze regulatory implications, identify ethical risks (e.g., bias, privacy), and present a structured AI implementation plan for peer/faculty review. Suggested (not exhaustive): ADR prediction system, stability forecasting, demand prediction, QC failure model, disease risk prediction, medication adherence
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