Foundations of Agentic AI for Life Sciences

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.

Course

Intermediate
12 hours
Updated: Feb 24, 2026

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Skills you'll learn

13 skills

Persona-Based Prompt Design
CoT and ReAct
AI Transparency
Iterative Prompt Development
Agentic Reasoning Frameworks

Prerequisites

3 prerequisites

Prior to enrolling, you should have the following knowledge:

You will also need to be able to communicate fluently and professionally in written and spoken English.

Course Outline

16 lessons
1 project

Foundations of Agentic AI for Life Sciences

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.

Program Instructors

3 instructors

Unlike typical professors, our instructors come from Fortune 500 and Global 2000 companies and have demonstrated leadership and expertise in their professions:

Tamas Madl

Healthcare & life sciences AI leader

Ahmad Abboud

Lead Gen AI Engineer and AI/ML Architect at Algentics

Brian Cruz

Head of AI Engineering, Advocate

Tamas Madl

Healthcare & life sciences AI leader

Ahmad Abboud

Lead Gen AI Engineer and AI/ML Architect at Algentics

Brian Cruz

Head of AI Engineering, Advocate

About this program

Build compliant, safe AI agents for life sciences. Apply effective prompting techniques that adhere to HIPAA, GDPR, and FDA frameworks. Create audit-ready validation artifacts.

Subscription · Monthly

Cancel Anytime
Unlimited access to hundreds of top-rated courses
Hands-on projects with expert feedback
Personalized career coaching and interview prep
Program Certificates

Foundations of Agentic AI for Life Sciences

Skills you'll learn

Prerequisites

Course Outline

Program Instructors

About this program

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