AI for Life Sciences & Pharma: Advanced

Pharma, biotech, and life-sciences teams learn to apply AI in regulated workflows with rigor. The course covers literature review, regulatory writing, data analysis, validation, and GxP and compliance considerations. Participants finish able to use AI where it's allowed, carefully and defensibly.

What this course covers

The course runs across 10 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

GxP Compliance and AI Validation Frameworks — Establish the regulatory foundations for using AI in life sciences. Learn how GxP, FDA guidelines, and EU AI Act principles apply to algorithm validation, data integrity, and maintaining a defensible audit trail.
AI-Assisted Literature Review and Evidence Synthesis — Perform rigorous, reproducible literature searches using AI tools. Practice constructing verifiable prompts, cross-referencing AI outputs with primary sources, and screening studies with zero hallucination tolerance.
Scenario: Navigating Hallucinations in Regulatory Writing — An interactive exercise where you draft sections of a Clinical Study Report (CSR) or Common Technical Document (CTD) using AI, identify subtle factual errors introduced by the model, and apply mitigation protocols.
Hands-On: AI-Driven Clinical Trial Data Analysis — Load a mock clinical dataset into a secure AI environment. Write and execute code-based prompts to clean data, generate safety summaries, and perform exploratory analysis while preserving data privacy.
Automating Pharmacovigilance and Adverse Event Detection — Explore how NLP models parse unstructured safety reports. Learn to set up human-in-the-loop validation pipelines to ensure high-sensitivity detection of adverse events without missing critical signals.
Scenario: Defending AI Workflows to Regulatory Auditors — Navigate a simulated FDA or EMA audit. Make critical decisions on how to present your AI validation documentation, explain model drift, and justify your human-in-the-loop oversight mechanisms under pressure.
Secure Proprietary Data: Sandboxes and Local LLMs — Technical setup and best practices for deploying AI within secure enterprise boundaries. Learn to prevent IP leakage, configure private API endpoints, and evaluate when to use local vs. commercial models.
Bias Mitigation and Diversity in Clinical Cohort Selection — Use AI to optimize patient recruitment criteria. Learn to audit AI algorithms for demographic bias and ensure synthetic control groups or recruitment models do not skew clinical trial outcomes.
Scenario: Resolving Conflicting AI Insights in Drug Discovery — Manage a high-stakes decision point in early-stage target identification. Choose how to validate conflicting predictive outputs from two different machine learning models using wet-lab constraints.
Establishing Institutional AI Governance and SOPs — Draft standard operating procedures (SOPs) for AI use in your organization. Define roles (e.g., AI Validator), establish continuous monitoring schedules, and create a roadmap for compliant scaling.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to AI Ethics, Data Analysis, Content Strategy, Generative AI Concepts, Critical Thinking.

AI Ethics
Data Analysis
Content Strategy
Generative AI Concepts
Critical Thinking

Who it is for

Pharma, biotech, life sciences. The material is pitched at advanced level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Industry: Life Sciences track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, branching scenario, code lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

AI for Life Sciences & Pharma: Core — The working rung between safe everyday use and regulated-workflow expertise. Learners apply AI to the real middle of pharma work - literature summaries, internal reports, meeting-heavy coordination - while learning the documentation habits regulated environments expect. They finish using AI confidently in daily work with an auditable trail.
AI for Life Sciences & Pharma Pathway — Grow from safe everyday use into applying AI across regulated life sciences workflows.

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code IND-PHARMA.