Building and Evaluating LLM-Powered Generative AI Solutions for Clinical Trials

Master the design, development, and rigorous evaluation of LLM-powered Generative AI solutions tailored for clinical trials, ensuring ethical compliance and optimal performance.
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Introduction to LLMs in Clinical Trials & Regulatory Landscape

Unit 1: LLMs in Clinical Trials: An Overview

Unit 2: Challenges and Ethical Considerations

Unit 3: Regulatory Landscape & Compliance

Prompt Engineering for Clinical Trial Use Cases

Unit 1: Prompting Fundamentals for Clinical Trials

Unit 2: Advanced Prompting Techniques

Unit 3: Prompting for Clinical Trial Applications

Building Retrieval-Augmented Generation (RAG) Systems for Clinical Data

Unit 1: RAG Fundamentals for Clinical Trials

Unit 2: Building the Retrieval Component

Unit 3: Integrating RAG with LLMs

Fine-tuning LLMs with Clinical Trial-Specific Datasets

Unit 1: Fine-tuning Fundamentals for LLMs

Unit 2: Data Preparation for Clinical Fine-tuning

Unit 3: Fine-tuning Implementation & Optimization

Evaluating Performance, Safety, and Reliability of LLM Solutions

Unit 1: Foundations of LLM Evaluation

Unit 2: Performance & Accuracy Metrics

Unit 3: Safety & Reliability Evaluation

Unit 4: Advanced Evaluation Frameworks

Mitigating Bias, Ensuring Explainability, and Operationalizing LLMs in Clinical Trials

Unit 1: Understanding and Mitigating Bias in LLMs

Unit 2: Ensuring Explainability and Interpretability

Unit 3: Operationalizing LLMs in Clinical Trials