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I have finalised the demo for the ICH-GCP E6 R3 refresher course. Overall, I liked the content and the interface. I also want to thank Whitehall Train...
AI in Healthcare and Pharmaceuticals Course & Certification
Beginner
Foundations, Validation, and Responsible AI Implementation with QMS Integration and Regulatory Inspection Readiness
4.2
★★★★★
95% Recommended
Updated 2026
6 hours
130 active users
6 points
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Each course includes:
Fully compliant
This course meets the required standard for basic GCP training, allowing learners to work on clinical trials. Learners also receive 6 Continuing Professional Development (CPD) Points awarded by the Faculty of Pharmaceutical Medicine of the Royal College of Physicians of the United Kingdom. These can be used to count towards the distance learning element of any scheme that comes under the umbrella of The Academy of Medical Royal Colleges or any other scheme for which there is mutual recognition.
Certified
All learners who pass the final exam receive a uniquely numbered, personal certificate to demonstrate their subject knowledge. Since the questions are picked randomly from database, re-sitting the exam doesn’t mean taking the same questions again and again.
Affordable
Our cost-effective prices represent excellent value. You can easily pay up to ten-times more for face-to-face training. We can also offer generous group discounts on larger purchases.
Time-saving
Our new administration system makes it incredibly quick and simple to allocate licences to multiple learners. Learners save time too by choosing when and where they complete the training.
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Author
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
The AI in Healthcare and Pharmaceuticals Course is designed to provide a comprehensive understanding of how artificial intelligence is transforming healthcare delivery, clinical research, and pharmaceutical development. The course focuses on practical applications of AI across the life sciences value chain, from drug discovery to patient care and regulatory processes.
This course covers AI fundamentals, machine learning applications, drug discovery and development, clinical trial optimization, healthcare analytics, regulatory considerations, and ethical use of AI. It also emphasizes real-world use cases, data-driven decision-making, and emerging innovations shaping the future of healthcare and pharma. Upon completion, learners receive a certification demonstrating competency in AI applications in life sciences.
Who Should Enrol ?:
- Clinical Research Professionals
- Pharmaceutical and Biotechnology Professionals
- Healthcare Professionals and Clinicians
- Data Science and Analytics Professionals in Life Sciences
- Regulatory Affairs and Compliance Professionals
- Pharmacovigilance and Clinical Operations Teams
- Life Science, Pharmacy, Nursing, and Medical Graduates
What you will learn
Understand the fundamentals of AI in healthcare and pharmaceuticals, including core concepts, applications, and responsible implementation principles.
Learn how AI systems are clinically validated and governed through risk management, post-market surveillance, and regulatory oversight frameworks.
Gain knowledge of responsible AI practices, including bias detection, fairness, and ensuring health equity in healthcare and research applications.
Develop an understanding of AI applications in pharmaceutical R&D, implementation strategies within QMS, and regulatory inspection readiness requirements.
Course Syllabus
- Introduction & AI/ML Fundamentals
- Software Classification – US & EU Frameworks
- Design Controls & Quality Management
- AI-Specific Regulatory Considerations
- Documentation, Case Studies & Wrap-Up
- Introduction & Clinical AI Capabilities / Limitations
- Clinical Validation Requirements – US & EU
- Performance Monitoring & Post-Market Surveillance
- Risk Management & ISO 14971
- Case Studies & Documentation Checklist
- Understanding Algorithmic Bias in Healthcare
- Regulatory Requirements for Fairness and Bias
- Fairness Metrics and Assessment Methods
- Transparency and Human Oversight
- Practical Implementation and Case Discussion
- AI Applications In Drug Discovery And Target Identification
- AI In Clinical Trials And Development
- Fairness Metrics and Assessment Methods
- Computer System Validation For Ai In GxP Environments
- Case Studies And Documentation Requirements
- AI Governance Foundations
- Vendor Assessment and Supplier Management
- Change Management and CAPA for AI Systems
- Continuous Monitoring and Performance Management
- Implementation Roadmap and Case Study
- Understanding the Inspection Landscape for AI Systems
- Documentation Requirements and Organization
- Common Inspection Findings and How to Avoid Them
- The Inspection Process — What to Expect
- Case Studies and Master Readiness Checklist
Our Certified Customers
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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
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