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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 & Responsible Implementation
4.7
★★★★★
91% Recommended
Updated 2026
3 hours
175 active users
3 points
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1 x AI in Healthcare and Pharmaceuticals
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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 Module 1 – AI in Healthcare & Pharma: Foundations Course is designed to provide a clear introduction to the core concepts of artificial intelligence and its applications in healthcare, pharmaceuticals, and clinical research. The course focuses on building foundational knowledge of AI technologies and how they are transforming drug development, patient care, and healthcare operations.
This course covers AI fundamentals, machine learning concepts, data types in healthcare, real-world applications in clinical trials and drug discovery, regulatory and ethical considerations, and limitations of AI in life sciences. It also emphasizes responsible AI use, data privacy, and emerging trends shaping the future of healthcare innovation. Upon completion, learners receive a certification demonstrating foundational competency in AI for healthcare and pharma.
Who Should Enrol ?:
- Clinical Research Professionals
- Pharmaceutical and Biotechnology Professionals
- Healthcare and Medical Professionals
- Data Science and Analytics Beginners 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, use cases, and its role in modern clinical and operational environments.
Learn the principles of responsible AI implementation, including ethical considerations, governance expectations, and regulatory perspectives.
Gain knowledge of clinical validation approaches for AI systems, including performance evaluation, risk assessment, and lifecycle management.
Develop an understanding of post-market oversight, monitoring, and risk management of AI-enabled healthcare solutions to ensure safety, compliance, and reliability.
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
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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
