Overview

This course introduces the integration of artificial intelligence in the life sciences. It covers regulatory pathways and assurance strategies, emphasizing risk management from development to clinical applications. Through lessons on governance and ethics, students will learn to assemble a comprehensive dossier. The course also incorporates practical elements of prompting techniques, including role-based prompting, chain-of-thought (COT), and ReACT prompting using Python. Finally, it explores feedback loops for continuous improvement and a detailed approach to adaptive clinical trial feasibility.

Syllabus

Introduction to Foundations of Agentic AI in Life Sciences

Explore core concepts of agentic AI in life sciences, meet your instructors, and set up Vocareum OpenAI API keys for hands-on learning.

Protecting Sensitive Data

Learn to protect sensitive health and genomic data by understanding privacy risks, key regulations (HIPAA, GDPR), and security measures essential for trust in life sciences AI.

Compliance & Lean Assurance

Explore how to ensure healthcare AI is safe and effective, covering regulatory compliance, risk analysis, SaMD, lean assurance, bias mitigation, and ongoing post-market vigilance.

Building Trust & Accountability

Learn essential practices for building trust and accountability in AI within the life sciences: documentation, traceability, good governance, and ethical standards in high-stakes, regulated fields.

Introduction to Prompting for Effective LLM Reasoning and Planning

Introduces the core concepts of Agentic AI, the course structure, prerequisites, and learning environment.

Role-Based Prompting

Explains the theory of using roles or personas to control the tone, style, and expertise of an LLM's output.

Implementing Role-Based Prompting with Python

Learn to create effective role-based prompts in Python, guiding AI to emulate expert personas like pathologists or genetic counselors for structured, safe, and professional outputs.

Chain-of-Thought and ReACT Prompting

Explains the conceptual frameworks for Chain-of-Thought (CoT) for guided reasoning and ReAct (Reason+Act) for enabling agents to plan and take actions.

Applying COT and ReACT Prompting with Python

Learn to implement Chain-of-Thought (CoT) and ReAct prompting in Python to enable structured agent reasoning and tool-using workflows for biomedical tasks.

Prompt Instruction Refinement

Explains the theory of systematically refining prompt instructions by modifying components like Role, Task, Context, Examples, and Output Format.

Applying Prompt Instruction Refinement with Python

Learn to iteratively refine Python prompts for regulated, auditable, and machine-validated LLM outputs, using role, task, and format adjustments in real-world health data scenarios.

Chaining Prompts for Agentic Reasoning

Explains the conceptual framework for building multi-step AI workflows by linking the output of one prompt to the input of the next, and the importance of validation.

Chaining Prompts with Python

Learn to implement robust multi-step prompt pipelines in Python using LangChain, ensuring validated, error-free outputs for pharmacovigilance signal reporting workflows.

LLM Feedback Loops

Explains the conceptual framework for building self-improving systems where an agent uses feedback from its own actions to iteratively refine its output.

Implementing LLM Feedback Loops with Python

Learn to build self-correcting feedback loops for LLMs in Python, enabling programmatic evaluation and revision for reliable, audit-friendly outputs in life sciences workflows.

Adaptive Clinical Trial Feasibility Agent

You will act as an AI Engineer configuring an agentic AI system for drug safety monitoring. You’ll define expert AI roles, assign tools, and enable the agents to collaborate on a safety signal report.