AI & GenAI for Clinical Trials: Complete 2026 Masterclass

By the end of this module, learners will be able to:

• Understand AI and Generative AI in clinical trials, enhancing workflow efficiency, data quality, and operational decision-making.

• Apply AI for predictive modelling and risk detection, identifying safety, protocol, and compliance issues early.

• Use GenAI for report writing and data summarisation, accelerating documentation, patient engagement, and regulatory submissions.

• Generate and simulate synthetic datasets, including control arms, to support trial design and rare patient populations.

• Develop future-ready clinical research skills, combining human expertise with AI to conduct faster, safer, and more precise clinical trials.

By the end of this module, learners will be able to:

• Understand AI and Generative AI in clinical trials, improving workflow efficiency, data quality, and operational decision-making.

• Apply AI for predictive modelling and risk detection, identifying safety, protocol, and compliance issues early.

• Use GenAI for report writing, summarisation, and decision support, accelerating documentation, patient engagement, and regulatory submissions.

• Generate and simulate synthetic datasets, including control arms, to support trial design, rare disease research, and operational planning.

• Develop future-ready clinical research skills, combining human expertise with AI to conduct faster, safer, and more precise clinical trials.

• Collaborate responsibly with AI, ensuring ethical, transparent, and regulatory-compliant integration in clinical research.

By the end of this module, learners will be able to:

• Understand probabilistic modeling to estimate uncertainties, guide adaptive trials, and generate synthetic patient data.

• Apply maximum likelihood estimation (MLE) to fit predictive models accurately for regression, classification, and generative tasks.

• Use loss functions, such as cross-entropy and MSE, to train AI models and quantify prediction accuracy.

• Implement Bayesian inference to update predictions with prior knowledge and handle adaptive trials or rare events.

• Perform regression, feature modeling, and PCA for dose-response analysis, biomarker validation, dimensionality reduction, and multicollinearity detection.

• Interpret AI outputs responsibly, ensuring clinical relevance, regulatory compliance, and patient safety.

By the end of this module, learners will be able to:

• Understand deep learning architectures for analyzing complex biomarker signals and integrating multi-modal data for improved patient stratification.

• Apply Transformers to extract insights from free-text medical records, enhancing pharmacovigilance, data quality, and regulatory submissions.

• Use Generative AI models (VAEs & GANs) to create synthetic patient data, support rare event modelling, and plan adaptive trials.

• Implement reinforcement learning to optimize treatment decisions, dose titration, and trial strategies safely before real-world implementation.

• Recognize the clinical impact of advanced AI, including reduced timelines, improved safety, and increased predictive accuracy in trials.

By the end of this module, learners will be able to:

• Detect risks early by using AI to continuously monitor patient, site, and operational data.

• Leverage Generative AI to extract insights from unstructured clinical documents for faster safety intelligence.

• Make explainable, data-driven decisions, using AI-generated risk scores to support clinicians and CRAs confidently.

• Improve operational efficiency, predicting site compliance issues and patient dropout to maintain trial timelines and data quality.

• Enhance patient outcomes and trial success, using AI to reduce delays, improve safety, and accelerate therapy delivery.

By the end of this module, learners will be able to:

• Understand hybrid clinical trial designs, combining real participants with GenAI-based simulations for faster, safer, and more equitable research.

• Apply GenAI to simulate control arms, reducing patient exposure to placebo or ineffective treatments.

• Generate synthetic patient data for rare diseases or high-ethics-risk trials, strengthening the evidence base safely.

• Enhance data accuracy and reduce bias using AI-driven simulations while supporting trial validation.

• Ensure regulatory compliance and transparency, documenting algorithms, assumptions, and datasets for ethical GenAI deployment.

By the end of this module, learners will be able to:

• Understand ethical considerations for GenAI in clinical trials, ensuring patient awareness, fairness, and privacy.

• Apply rigorous validation techniques, including benchmarking, stress-testing, and explainability audits for AI models.

• Implement strong governance frameworks, with multidisciplinary oversight, accountability, and human-in-the-loop processes.

• Ensure regulatory compliance, engaging early with authorities and documenting model context, workflows, and assumptions.

• Promote trust in AI-driven clinical decisions, balancing innovation with safety, transparency, and patient protection.

By the end of this module, learners will be able to:

• Understand real-world applications of Generative AI in pharma, including drug discovery and molecule design.

• Use GenAI for bioactivity prediction and candidate optimisation, accelerating early-stage research.

• Apply GenAI to drug repurposing, exploring new indications to speed up development safely.

• Recognise efficiency gains from AI deployment, reducing costs and timelines in pharma R&D.

• Integrate GenAI insights responsibly, ensuring outputs align with regulatory standards and ethical practices.

By the end of this module, learners will be able to:

• Understand future trends in AI-powered clinical trials, including AI-first operations and automation.

• Explore the use of patient digital twins, simulating risk, predicting responses, and designing synthetic control arms.

• Learn how Generative AI can reduce protocol complexity, amendments, and operational burden before patient enrollment.

• Recognize the evolving collaboration between humans and AI, enhancing decision-making, safety, and trial efficiency.

• Prepare for emerging AI applications in clinical research, shaping faster, smarter, and more patient-centric trials.