AI Training · Pharma & Life Sciences

AI Training for Pharma & Life Sciences

Scientific literature, medical content, regulatory documents, safety cases, SOPs, and field materials all depend on precise text. Pharma and life-sciences teams use this hands-on training to draft, summarize, and triage with generative AI while keeping evidence checks, approved claims, and qualified human review in every workflow.

modules: 6
hours: 13

The common assumption is that a language model reading a clinical paper is basically doing what a scientist does, just faster. It isn't. A model can summarize fluently and still invent a citation or drop a study limitation. It may also drift past an approved claim without any signal that something is wrong.

Pharma and life-sciences work runs on a long chain of evidence and documents: publications become literature summaries and medical briefs, approved data becomes regulatory drafts and field content, and safety information moves through intake, triage, and reporting. AI can speed up reading and structuring at every step of that chain, including first-draft work. It cannot replace the scientific, medical, safety, or regulatory judgment that decides whether the result is usable.

Biotechnology and R&D scientific-knowledge teams sit inside the same evidence chain. The modules support source-grounded literature triage and evidence tables, along with briefing and report drafts. They also cover knowledge-management work that helps a research group reuse prior material within project-confidential boundaries. AI does not predict molecular properties, run bioinformatics analysis or produce a scientific conclusion in this program. A qualified scientist still verifies methods, figures and citations and owns every finding.

In pharmacovigilance, a qualified professional still assesses and decides on a case. AI may only help organize incoming information or flag items for review. In regulatory work, specialists still own validation, approval, and submission. A drafting assistant does not acquire a vote in any of that. Training sharpens the drafting and checking skills.

None of this changes who signs off. Teams get faster at drafting and sharper at catching the mistake before it ships, and that speed is most of what this program is for. The certification and compliance sign-off still sit with your regulatory and quality functions, alongside safety. Other regulated sectors are covered across the AI training catalog.

The reading queue never empties

Journal articles, congress materials, internal reports, and evidence updates create a reading queue that never empties. AI can help summarize and organize that material, but every scientific statement in the output still needs to be traced back to its source and checked.

The same information gets rewritten into a new format constantly

Medical, regulatory, quality, and training teams turn one piece of approved information into a dozen different documents, and AI-assisted drafting speeds up that first version while the document owner keeps control of review and approval.

One inconsistent term causes more rework than a slow draft

Medical responses and training material need terminology shared by SOP support documents. Field content needs the same boundaries, no matter which team wrote it. Structured prompts and a controlled source pack make that consistency achievable without a slower process.

This data carries more risk than marketing copy

Safety narratives, research records, professional contact details, and health information can carry confidential, personal, or special-category data without anyone noticing at first glance. Teams need a plain rule for what is allowed into which tool before anyone starts experimenting.

Verification is the skill

A fluent answer can misstate a study, invent a citation, or drift past an approved claim, and it will read exactly as confidently either way. Organizations that get value teach people to check sources and write down their assumptions. They escalate uncertainty instead of trusting the output at face value.

01
Generative AI foundations for pharma and life sciences
120 minBeginner
What large language models do well, where they quietly fail, and why scientific, medical, regulatory, and safety work needs tighter controls than most industries. Teams build a common vocabulary here and learn to tell drafting support apart from an actual clinical or regulatory decision.
What a language model is doing when it drafts an answer
Citation errors and hallucinations behind unsupported claims
Task lines: where drafting support ends and an expert decision begins
Where AI already shows up in the office tools everyone already has
A first experiment worth trying because the risk is genuinely low
A model for reviewing and escalating anything higher-stakes
02
Prompt craft grounded in sources and validation
120 minBeginner
A repeatable structure for life-sciences documents keeps every draft traceable to its basis, with purpose, audience, approved sources, task, format, and checks all made explicit before participants verify each material statement and move it forward.
A prompt template built for scientific and operational documents
Supplying approved context without exposing anything confidential
A source-linked summary, always
Checking a quotation, reference, figure, or study detail against its source
Telling a source fact apart from a model suggestion and a reviewer's conclusion
Building a controlled prompt and source library as a team
03
Scientific literature and medical affairs content
150 minIntermediate
Literature monitoring and scientific summarization support medical affairs content workflows under one rule: everything stays traceable. AI screens, structures, and drafts, while a qualified reviewer still decides relevance, scientific accuracy, balance, and whether something is fit to use.
Summarizing a publication with every claim linked to its source passage
Literature monitoring with relevance tags and a working review queue
Evidence tables, briefing notes
A first draft of medical information or educational content
Reshaping one approved evidence base for several professional audiences
Scientific review for missing context or overstatement, including invented citations
04
Regulatory, quality, SOP, and training documents
150 minIntermediate
Regulatory affairs, quality, and learning all produce document-heavy work: structured first drafts, controlled rewrites, comparisons, and consistency checks. AI supports the document preparation here, while accountable specialists keep authorship, validation, approval, and submission decisions.
Drafting a regulatory-document section from approved structured input
Comparing versions and flagging what's missing or inconsistent
Turning an approved procedure into SOP support and training material
Learning aids and FAQs
Keeping defined terminology the same across an entire document set
Ownership, validation, approval, and an audit trail for every review gate
05
Pharmacovigilance support within responsible data boundaries
120 minIntermediate
Clear boundaries for confidential, personal, health, and special-category data sit alongside a careful pharmacovigilance use case here. Participants learn where AI can help organize or prioritize information. A qualified safety professional still reviews and assesses every case before deciding it.
Naming confidential, personal, health, and special-category data correctly
What separates a consumer tool from an enterprise one, prompt by prompt
De-identification and minimization through synthetic examples in exercises
Pharmacovigilance intake and triage support that stops short of a case decision
Qualified human review with escalation and a documented rationale
Logging AI-assisted work and flagging uncertain output
06
Approved commercial content and governed rollout
120 minAdvanced
Commercial and field-team content gets created here inside approved claims, followed by a practical adoption plan. Teams design controlled pilots, review paths, reusable assets, and measurement that rewards reliable use over raw output volume.
Drafting field and commercial content from an approved claim library
Adjusting tone and format without expanding or changing the claim
Medical, legal, regulatory checkpoints
Choosing a pilot workflow by value and sensitivity against review effort
A champions model spanning medical, regulatory, safety, quality, and commercial teams
A 90-day rollout plan with named owners and a feedback loop

What you will learn

Summarize scientific text, source-linked, then verify against the original
A literature flow, reviewer-ready
Draft medical, regulatory, SOP, or training content from approved inputs
Create commercial and field-team drafts that never leave the approved claim or its review path
Use AI for pharmacovigilance intake and triage support, while safety assessment and case decisions stay with a qualified person
Handle confidential, personal, health, or special-category data inside clear boundaries
Catch errors before they ship
Plan a governed path from pilot to rollout, with named owners, review gates, and reusable prompt assets

Who should attend

Medical affairs and medical information teams
Regulatory affairs and submissions teams
Pharmacovigilance and drug-safety teams
Quality and learning teams responsible for SOPs
Commercial excellence and marketing teams supporting the field force
Digital, data, legal, compliance, and transformation leaders

Life-sciences functions share two days, usually split into half-day sessions so scientific, safety, regulatory, and commercial reviewers can each join the workshops most relevant to them. Onsite and live-online formats cover the same syllabus.

Format: Onsite or live online
Duration: 2 days (about 13 hours, can be split into half-day sessions)
Group size: Up to 20 participants per group
Language: English or Turkish
Materials: Prompt library, validation checklists, document templates, and exercise workbook
Certificate: Certificate of completion

Zeo started in 2011 and now works out of San Francisco, Istanbul, Ankara, and Lisbon. We run Copilot Academy and organize Digitalzone, an international digital marketing conference. This program draws on the 10+ years of consulting and training work behind that, applied to corporate AI adoption.

2011founded in Istanbul
10+years of consulting and training experience
3offices: San Francisco, Istanbul, Ankara, Lisbon

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Tell us which medical and regulatory teams need this, along with safety, quality, commercial or digital teams. Name the document workflows that matter most. Those answers shape the modules and examples, with review boundaries built around your organization.

Illustrated figure reviewing a workflow board while a small robot assistant holds up a card

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AI Training for Pharma & Life Sciences