Ethical Considerations and Translational Applications of AI in Pharmacy
Ethics, bias, data privacy and governance in AI; translating AI research into pharmacy practice; responsible and regulatory aspects of AI in healthcare.
Shree Sai Prakashan is writing a full set of B.Pharm titles to this new NEP 2020 syllabus. Get notified the moment the book for this subject is released, or pre-book sets for your college.
Course contents — unit by unit · PCI NEP 2020 BP801T
Unit 1 · AI Lifecycle, Validation & Model Auditing 6 hrs
The AI system lifecycle — data collection, preprocessing, modelling, validation, deployment and monitoring; the importance of data quality in healthcare datasets; training, testing and validation datasets and cross-validation; model drift, performance degradation and data leakage; documentation and reproducibility; and the basics of model auditing.
Unit 2 · Regulatory Framework & Explainable AI 6 hrs
AI in regulatory submissions; Explainable AI (XAI), transparency and interpretability; regulatory guidance on AI (the EU AI Act, FDA and CDSCO frameworks); accountability in automated decision systems; and risk-based classification of AI systems.
Unit 3 · AI in Pharmacy Automation & Supply Chain 6 hrs
AI in automated dispensing systems; inventory-prediction and demand-forecasting models; medication-adherence monitoring; AI in supply-chain risk prediction and pharmaceutical logistics; and the advantages, limitations, legal and privacy considerations of automation.
Unit 4 · AI in Public Health & Real-World Data Analytics 6 hrs
Real-world data sources (EHR, claims, surveillance systems); AI in outbreak prediction and population-level risk modelling; regression models in epidemiology; AI in vaccination forecasting; and case studies in epidemiological trend analysis (COVID-19 vaccine hesitancy, diabetes-prevalence and antibiotic-resistance forecasting).
Unit 5 · Guided Project — Translational AI in Pharmacy 6 hrs
Implementing a supervised ML model on real-world pharmacy data (formulation, pharmacokinetics, ADR detection, quality control, automation or public health), validating it, analysing regulatory implications, identifying ethical risks such as bias and privacy, and presenting a structured AI-implementation plan.
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