AI Literacy for Pharma and Life Science Professionals

A structured training programme covering the conceptual, practical, and regulatory foundations of artificial intelligence (AI). The course explains how AI and large language models work, how to use them effectively and safely in day-to-day work, and what current FDA, EMA, and EU regulatory frameworks mean for AI-assisted workflows in a regulated environment.

10 Modules

Approx. 30-35 hours

Certificate on completion

English

Foundation

Self-paced: Start any time Access: 150 days from start date Last updated: Q4 2026

What This Course Covers

AI has moved from a speculative technology to a practical tool already embedded in everyday software used across regulatory affairs, medical writing, clinical operations, pharmacovigilance, and market access. Professionals encounter AI-generated summaries, AI-assisted literature searches, and predictive models embedded in quality systems, often without formal evaluation or training. This course provides a structured foundation in what these systems are, how they work, and how to use them responsibly within a regulated pharmaceutical environment.

Across ten modules, the programme covers the foundational concepts of AI, machine learning, and deep learning; the technical mechanics behind large language models, including tokenization, attention, and hallucination; the landscape of AI tools encountered in pharmaceutical workplaces; prompt engineering for reliable outputs; data privacy and acceptable-use considerations; computer system validation and qualification approaches for AI tools; the regulatory frameworks shaping AI in drug development, including the EU AI Act and FDA/EMA guidance; the role of AI in medical writing and regulatory submissions; structured evaluation of AI-generated content; and the ethical, governance, and professional-skills dimensions of AI adoption. Each module includes two applied case studies and a knowledge check.

Course content references and is informed by current regulatory publications, including the joint FDA-EMA guiding principles of good AI practice in drug development (January 2026), the EMA reflection paper on AI in the medicinal product lifecycle, FDA AI/ML guidance, the EU AI Act (Regulation (EU) 2024/1689), and the NIST AI Risk Management Framework. AI-specific regulatory guidance is still evolving; course content is reviewed and revised as new guidance is published.

Who Is Ready for This Course

No prior technical or data-science background is required. The course does not involve coding, model building, or algorithm development. It is designed for professionals encountering AI concepts for the first time as well as those seeking to formalise practical experience already gained through everyday use of AI tools. Module 1 introduces the necessary conceptual foundations before later modules address practical application, validation, and regulatory frameworks. A working familiarity with the pharmaceutical or life-sciences environment is assumed.

After Completing This Course You Will Be Able To:

01 Explain the relationship between AI, machine learning, and deep learning, and distinguish generative from predictive AI using precise, consistent terminology.

02 Describe how tokenization, embeddings, and the attention mechanism enable large language models to generate text, and explain why this makes hallucination a structural feature rather than a fixable defect.

03 Identify the main categories of AI tools encountered in the pharmaceutical workplace and apply a structured framework to evaluate whether a given tool is appropriate for a specific task.

04 Apply a structured framework to construct, refine, and evaluate prompts, and distinguish tasks where prompt quality improves output reliability from tasks requiring human professional judgment regardless of prompt quality.

05 Distinguish between data categories that are prohibited, context-dependent, and generally permissible for use with commercial AI tools, and apply GDPR and acceptable-use principles to everyday data-sharing decisions.

06 Apply a risk-based approach to determine the appropriate level of validation or qualification for an AI tool under frameworks such as GAMP 5, 21 CFR Part 11, and EU GMP Annex 11.

07 Describe current FDA and EMA positions on AI in drug development and regulatory submissions, and explain the EU AI Act's risk-tier classification and its documentation implications for high-risk applications.

08 Identify medical writing tasks where AI tools can provide legitimate, auditable support, and distinguish these from content requiring unmediated human expert judgment.

09 Apply structured evaluation frameworks to identify hallucination, statistical misrepresentation, and regulatory imprecision in AI-generated scientific and regulatory content.

10 Evaluate the components of an organizational AI governance framework, identify sources of bias in pharmaceutical AI systems, and apply a framework for assessing personal and team AI-readiness.

10 Modules · 20 Applied Case Studies

Module 1: What Is AI? Foundations for Pharma and Life Science Professionals

Defining AI · How AI Learns · Generative vs. Predictive AI · Myths and Misconceptions · Case studies · Knowledge check

Module 2: How AI Tools Work — Under the Hood

Tokens & Embeddings · Attention & Prediction · Hallucination · Context & Temperature · Case studies · Knowledge check

Module 3: AI Tools in the Pharma Workplace

LLM Interfaces · Literature & Search · Productivity Tools · Specialized Platforms · Case studies · Knowledge check

Module 4: Prompt Engineering and Effective AI Use

Prompt Foundations · The SPEC Framework · Iterative Refinement · Pharma-Specific Techniques · Case studies · Knowledge check

Module 5: Data Privacy, Confidentiality, and Acceptable Use

The Data Risk Landscape · GDPR and AI Tools · Acceptable Use Policies · A Practical Decision Framework · Case studies · Knowledge check

Module 6: Computer System Validation and Qualification for AI Tools

CSV Foundations · Regulatory Frameworks · AI Tools and GxP · Fit-for-Purpose Validation · Case studies · Knowledge check

Module 7: Regulatory Frameworks for AI in Drug Development

FDA & EMA Positions · EU AI Act Risk Tiers · Predetermined Change Control · Case studies · Knowledge check

Module 8: AI in Medical Writing and Regulatory Submissions

The AI-Assisted Writing Landscape · Legitimate AI Support Tasks · Where AI Cannot Replace Human Judgment · Agency Expectations and Disclosure · Human Oversight and Accountability · Case studies · Knowledge check

Module 9: Evaluating AI Outputs — Scientific and Regulatory Quality

Failure Modes · Hallucination in Depth · Regulatory Imprecision · Evaluation Frameworks · Verification in Practice · Case studies · Knowledge check

Module 10: Ethics, Accountability, and the Future of AI in Pharma

Accountability · Bias and Fairness · Governance Frameworks · AI Impact on Pharma Workflows · Skills for the AI Era · Case studies · Knowledge check

Who This Course Is For

WELL SUITED FOR

Professionals encountering AI tools in their day-to-day work who want a structured foundation
Professionals responsible for evaluating, qualifying, or governing AI tools used in GxP-relevant workflows
Professionals already using generative AI tools informally who want to understand the underlying mechanics, risks, and regulatory context before relying on them further
Professionals seeking a working knowledge of AI terminology, capabilities, and limitations to evaluate vendor claims and contribute to internal AI governance discussions

NOT DESIGNED FOR

Data scientists or ML engineers seeking technical training in model building, coding, or algorithm development — this course addresses literacy, evaluation, and governance, not technical AI/ML engineering

Certificate of Completion

Bradford Hill Academy — Certificate of Completion

Issued digitally upon completion of all ten modules, including each module's knowledge check. The certificate states: participant name, course title (AI Literacy for Pharma and Life Science Professionals), a summary of course content covered, completion date, and the Bradford Hill Academy seal.

Bradford Hill Academy certificates are professional development certificates. They are not issued by or on behalf of a state-recognised academic institution and do not confer academic degrees or formal qualifications. Whether a certificate qualifies for CPD recognition with a specific professional body depends on that body's policies.

Frequently Asked Questions

What does each module include?

Each of the ten modules combines structured content across several topic tabs with two applied case studies and a knowledge check before continuing to the next module. Module 1 requires no prior AI knowledge; later modules build progressively on earlier concepts.

Do I need a coding or technical AI/ML background?

No. This course does not involve programming, model building, or algorithm development. It focuses on conceptual understanding, safe and effective use, and regulatory context — the knowledge needed to be an informed user and reviewer of AI tools, not to build them.

I have no prior exposure to AI at all — is this course still suitable?

Yes. Module 1 builds the necessary vocabulary and conceptual foundation from first principles, assuming no prior technical background. Subsequent modules progressively introduce practical, regulatory, and governance topics.

Is this course aligned with a specific regulatory framework?

The course references and is informed by current FDA and EMA publications, including the joint FDA-EMA guiding principles of good AI practice in drug development (January 2026), the EMA reflection paper on AI in the medicinal product lifecycle, and the EU AI Act. Course content is reviewed and revised as regulatory guidance in this area continues to develop.

Can this course be licensed for a team or organisation?

Yes. Team and enterprise licensing is available from 5 licences upward. Contact info@bradfordhillacademy.com for more information.