Program Structure

Module 1: Introduction to AI in Pharmacy

• Overview of AI concepts relevant to pharmacy and healthcare.
• Understanding pharmacy data sources and digital workflows.
• Real-world examples of AI adoption in pharmacy settings.

Module 2: Data Foundations for Pharmacy AI

• Types of healthcare and pharmaceutical data.
• Data quality, preprocessing, and privacy considerations.
• Handling structured and unstructured pharmacy data.

Module 3: AI in Drug Discovery and Research

• AI-supported target identification and molecule screening.
• Prediction of drug properties and early risk assessment.
• Case examples from pharmaceutical research.

Module 4: AI in Formulation and Manufacturing

• Using AI to optimize drug formulations.
• Quality monitoring and process optimization concepts.
• Predictive maintenance in pharmaceutical manufacturing.

Module 5: AI in Clinical Trials and Evidence Generation

• Patient recruitment and trial optimization using AI.
• Analyzing trial data and real-world evidence.
• Post-marketing data insights for decision-making.

Module 6: Pharmacovigilance and Drug Safety Analytics

• AI-supported adverse event detection workflows.
• Text analysis for safety reports and literature review.
• Signal detection and risk prioritization concepts.

Module 7: AI in Clinical Pharmacy and Patient Support

• Medication adherence monitoring and support tools.
• Clinical decision support concepts for pharmacists.
• Patient engagement and education through AI systems.

Module 8: AI in Pharmacy Operations and Supply Chain

• Inventory optimization and demand forecasting.
• Reducing wastage and improving supply reliability.
• Monitoring logistics and distribution efficiency.

Module 9: Ethics, Privacy, and Responsible AI

• Patient data protection and regulatory compliance.
• Bias, fairness, and transparency in healthcare AI.
• Safe deployment and validation practices.

Final Project

• Design an AI-driven solution for a pharmacy-related challenge.
• Define problem scope, data requirements, and evaluation metrics.
• Example projects include inventory forecasting systems, drug safety monitoring workflows, or patient adherence support tools.