Program Structure

Module 1: AI in Pharmacy and Healthcare Overview

• Understanding where AI fits into pharmacy and healthcare workflows.
• Common AI use cases in hospital, retail, and pharmaceutical settings.
• Limitations and responsible use of AI in healthcare environments.

Module 2: Pharmacy Data and Digital Foundations

• Types of pharmacy and healthcare data used in AI systems.
• Data quality challenges and preprocessing basics.
• Privacy, consent, and secure data handling practices.

Module 3: AI in Drug Discovery and Research

• AI-supported target identification and molecule prioritization.
• Early-stage prediction concepts for drug properties.
• Reducing research timelines using data-driven approaches.

Module 4: AI in Formulation and Manufacturing

• Formulation optimization and stability analysis concepts.
• Quality monitoring and anomaly detection in manufacturing.
• Process efficiency and consistency improvement strategies.

Module 5: AI in Clinical Trials and Evidence Generation

• Patient recruitment and trial matching strategies.
• Monitoring trial outcomes and performance indicators.
• Use of real-world evidence in post-marketing analysis.

Module 6: Pharmacovigilance and Drug Safety Analytics

• Adverse event monitoring workflows.
• Text analysis concepts for safety reports and literature.
• Safety signal prioritization and risk assessment.

Module 7: AI in Clinical Pharmacy and Patient Support

• Medication adherence monitoring and support tools.
• Clinical decision support concepts for pharmacists.
• Patient engagement and digital education solutions.

Module 8: AI in Pharmacy Operations and Supply Chain

• Inventory optimization and demand forecasting methods.
• Reducing medicine wastage and improving availability.
• Supply chain visibility and operational analytics.

Module 9: Ethics, Privacy, and Responsible AI

• Bias, fairness, and transparency in healthcare AI.
• Regulatory readiness and compliance considerations.
• Validation and safe deployment practices.

Final Project

• Design an AI-based solution for a pharmacy or healthcare challenge.
• Define workflow, data requirements, and evaluation metrics.
• Example projects include inventory forecasting, safety monitoring, or patient adherence support systems.