SM
Clara Fargo
EBMT Clinical Research Quality Assurance Coordinator
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
Responsible and Regulatory-Compliant
4.7
★★★★★
97% Recommended
Updated 2025
3 hours
250 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
This course provides a structured and regulator-aligned understanding of artificial intelligence applications in healthcare and pharmaceutical research and development, with a strong emphasis on responsible implementation. It addresses both the ethical and technical challenges of AI adoption, including algorithmic bias, fairness, transparency, and health equity.
Through focused modules, learners explore how AI is applied across the pharmaceutical R&D lifecycle—from drug discovery and clinical trials to GxP-regulated environments—while understanding global regulatory expectations, validation requirements, and documentation standards. The course equips professionals with practical knowledge to assess, govern, and deploy AI systems that are innovative, compliant, and trustworthy in healthcare and pharmaceutical settings.
Course Syllabus
- 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
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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
