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AKTU Ethical Considerations and Translational Applications of AI in Pharmacy (Theory) (BP801T) Syllabus PDF – B.Pharm 8th Semester
Ethical Considerations and Translational Applications of AI in Pharmacy (Theory) BP801T
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Official AKTU syllabus of Ethical Considerations and Translational Applications of AI in Pharmacy (Theory) (BP801T) for B.Pharm 8th Semester – 2 credits, L-T-P 2-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Ethical Considerations and Translational Applications of AI in Pharmacy (Theory)
Download PDFBP801T · 2 credits · L-T-P 2-0-0
Course Objectives
The objectives of this course are:
- Introduce the lifecycle of artificial intelligence systems used in pharmaceutical and healthcare applications, including data management, model development, validation, and deployment.
- Provide an understanding of model validation, auditing procedures, and documentation practices required for reliable and reproducible AI systems.
- Familiarize students with regulatory, governance, and ethical frameworks guiding the implementation of AI in pharmaceutical and healthcare environments.
- Explore applications of artificial intelligence in pharmacy automation, supply chain management, and public health data analytics.
- Develop practical understanding of AI implementation in pharmaceutical and healthcare domains through case studies and guided project work.
Course Outcomes
| CO | Upon successful completion of this course, the students will be able to: |
| No. | |
| 1 | Explain the lifecycle of AI systems in pharmaceutical settings. |
| 2 | Evaluate model performance, bias, and validation requirements. |
| 3 | Discuss regulatory and governance frameworks applicable to AI in pharmacy. |
| 4 | Analyze AI applications in automation and public health analytics. |
| 5 | Evaluate ethical considerations in AI applications for pharmaceutical sciences and healthcare |
Course Content
| Unit No. | Topics Lectures | No. of |
| I | 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 | 6 hours |
| II | 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 | 6 hours |
| III | 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. | 6 hours |
| IV | 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.) | 6 |
| V | 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 topics (not exhaustive): ADR prediction system, stability forecasting, demand prediction, QC failure model, disease risk prediction, medication adherence | 6 |
| Recommended Readings: 1. Germanakos, P. Human-Centered AI: An Illustrated Scientific Quest. 2. Matheny, M., et al. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. National Academy of Medicine. 3. Mittal, M. and Bhushan, B. Generative AI in Healthcare: Concepts, Methodologies, Tools, and Applications. 4. Steyerberg, E. W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer. 5. Steyerberg, E. W. Practical Predictive Analytics and Decisioning Systems for Medicine. Academic Pres | ||
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