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Clinical Practice
Pharmacovigilance
Manufacturing
Laboratory Practice
Distribution
Medical Devices
AI Clinical

Author

Dr. Chirag Shah

Dr. Chirag Shah

Professor at the University of Washington (UW), Seattle, with research and teaching expertise in artificial intelligence, data science, machine learning, and search and recommender systems. A TEDx Speaker and ACM Distinguished Member.
Research focuses on task-based and conversational search and recommendation, user experience, multi-objective optimization, cold-start challenges, and agentic systems. Actively engaged in generative AI research, particularly in information access and image classification, with a strong emphasis on improving fairness and reducing bias in ML/AI systems.
Teaches undergraduate and graduate courses in Information Science and Data Science, and collaborates closely with leading industrial research labs as a visiting researcher. Recent industry engagements include Spotify, Amazon, Microsoft Research AI, Getty Images, and TikTok.
Has worked on real-world problems such as zero-intent and zero-query recommendations, marketplace fairness, and task-, journey-, and mission-based ranking systems, contributing to solutions and products that impact hundreds of millions of users across global markets.

About

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into healthcare and pharmaceutical products, influencing clinical decision-making, drug discovery and development, diagnostics, and patient outcomes. As these technologies move from innovation to real-world deployment, they introduce critical challenges related to regulatory compliance, quality management, clinical safety, data integrity, and ethical use. This course addresses the full AI lifecycle—covering foundational concepts, validation and implementation practices, Quality Management System (QMS) integration, and regulatory inspection readiness—to support the safe, effective, transparent, and responsible use of AI in regulated healthcare and pharmaceutical environments.

Course Syllabus

  1. Introduction & AI/ML Fundamentals
  2. Software Classification – US & EU Frameworks
  3. Design Controls & Quality Management
  4. AI-Specific Regulatory Considerations
  5. Documentation, Case Studies & Wrap-Up

  1. Introduction & Clinical AI Capabilities / Limitations
  2. Clinical Validation Requirements – US & EU
  3. Performance Monitoring & Post-Market Surveillance
  4. Risk Management & ISO 14971
  5. Case Studies & Documentation Checklist

  1. Understanding Algorithmic Bias in Healthcare​
  2. Regulatory Requirements for Fairness and Bias​
  3. Fairness Metrics and Assessment Methods​
  4. Transparency and Human Oversight​
  5. Practical Implementation and Case Discussion​

  1. AI Applications In Drug Discovery And Target Identification
  2. AI In Clinical Trials And Development​
  3. Fairness Metrics and Assessment Methods​
  4. Computer System Validation For Ai In GxP Environments
  5. Case Studies And Documentation Requirements

  1. AI Governance Foundations
  2. Vendor Assessment and Supplier Management​
  3. Change Management and CAPA for AI Systems
  4. Continuous Monitoring and Performance Management​
  5. Implementation Roadmap and Case Study

  1. Understanding the Inspection Landscape for AI Systems​
  2. Documentation Requirements and Organization​
  3. Common Inspection Findings and How to Avoid Them​
  4. The Inspection Process — What to Expect​
  5. Case Studies and Master Readiness Checklist​

Our Certified Customers

novartis
NHS
takeda
roche
baxter

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Frequently Asked Questions

  • Regulatory Affairs & Compliance Professionals
  • Clinical Research & Medical Affairs Teams
  • Quality Assurance & Quality Management Professionals
  • Digital Health, AI & Software Development Teams
  • Pharmaceutical & Medical Device Professionals
  • CROs, Sponsors, and Healthcare Innovators

This module builds the foundational understanding required to design and manage AI systems within regulated healthcare environments. This module focuses on clinical validation, performance monitoring, and risk management to ensure AI systems remain safe, effective, and compliant throughout their lifecycle.

  • Developed by regulatory and industry experts
  • Practical, real-world focus aligned with global regulatory expectations
  • Covers emerging AI regulatory guidance alongside established standards
  • Suitable for professionals across pharma, medical devices, and digital health
  • Designed to support audit readiness and compliant AI implementation