AI for BioTech professionals
Last reviewed . AI products change quickly; check the linked sources before relying on details.
What AI can help with
AI assistants are good at language work: structuring a question, drafting, summarising text you give them, reformatting, and spotting inconsistencies. In biotech that covers a lot of admin and thinking-aloud work in research, writing, operations, business development and hiring.
They are not good at being a source of truth. They can state invented citations, wrong figures, outdated guidance and plausible but false scientific claims with complete confidence. Nothing on this site presents AI output as validated, compliant, approved or fit for submission. Treat every output as a draft that a qualified person owns.
Use AI to speed up the first draft and the checking list. Keep people responsible for facts, decisions and records.
General-purpose AI or domain validation?
This table is a starting point for your own policy, not a compliance determination. Check with your QA and regulatory team what your organisation allows.
| Task | Suitability | What must happen |
|---|---|---|
| Brainstorming search terms and question framing | Reasonable with checks | You confirm terms in the real database |
| Summarising a paper you supply | Reasonable with checks | Verify every claim against the source |
| Drafting emails, agendas, meeting actions | Suitable | Remove sensitive data; person approves |
| Consistency editing of your own draft | Reasonable with checks | QC against source data still required |
| Market size, competitor facts, financials | Needs verification | Model may invent figures; use primary sources |
| Interpreting regulations for your product | Needs domain validation | Regulatory professional decides |
| Clinical, safety or efficacy statements | Needs domain validation | Qualified clinical and medical review |
| Data analysis feeding a submission or decision | Needs validated tools | Use validated systems and qualified statisticians |
| Anything entering a GxP record or procedure | Not for general-purpose AI | Follow your quality system and validation rules |
Protect sensitive data
Most biotech risk with AI is about what goes in, not what comes out. Treat these as off limits for any tool your organisation has not approved:
- Protected health information and personal data about patients, trial participants or employees (consider rules such as HIPAA and the EU GDPR, and ask your privacy officer).
- Unblinded, interim or confidential clinical trial data, and site-level subject data.
- Unpublished research, sequences, structures, formulations, process details and other intellectual property. Disclosure before filing can damage patent rights; ask your IP counsel.
- Investor terms, partner contracts and anything under a confidentiality agreement.
Use enterprise tools approved by your IT, security and legal teams, with settings that match your data policy (for example no training on your inputs, defined retention, access controls). Free consumer accounts rarely meet that bar. When in doubt, use public or de-identified material only and ask first.
Traceability and human oversight
- Record what you used AI for, which tool and version, and who reviewed the result. Your quality system may require this, for example where electronic records rules such as FDA 21 CFR Part 11 apply. Ask your QA team.
- Keep the source next to every claim. If you cannot trace a statement to a document, remove it.
- Name a reviewer with the right expertise: a scientist for science, regulatory affairs for regulatory interpretation, counsel for legal wording.
- Never let AI output enter a controlled record, a procedure or a safety-critical decision without your normal approval process.
Start by role
| Role | Where to start | Prompts |
|---|---|---|
| Research analyst | Research and literatureLiterature, evidence tables, question framing | 3 in the Vault |
| Medical writer | Research and literatureSummaries and consistency checks, with source verification | 2 in the Vault |
| Regulatory affairs | Clinical and regulatory awarenessAwareness-level support and the limits that apply | 2 in the Vault |
| Clinical operations | Clinical and regulatory awarenessAdmin support, meeting actions, training outlines | 2 in the Vault |
| Lab manager | Lab operationsSOP readability, inventory admin, workflows | 2 in the Vault |
| BD / fundraising | Business and fundraisingLandscape scaffolds, investor preparation | 2 in the Vault |
| Founder | Business and fundraisingClaims audit, updates, preparation | 2 in the Vault |
| Recruiter | Lab operationsJob descriptions and structured interviews (see prompts) | 2 in the Vault |
BioTech modules
For technical readers
If you are building rather than using these tools, the same cautions show up as engineering patterns: retrieval-augmented generation to ground answers in your documents, evaluation pipelines to measure accuracy before trusting output, and a guardrail layer to block unsafe inputs and outputs.
Frequently asked questions
What can AI help with in biotech?
AI assistants are good at language work: structuring a question, drafting, summarising text you give them, reformatting and spotting inconsistencies. In biotech that covers admin and thinking-aloud work in research, writing, operations, business development and hiring. They are not a source of truth, so treat every output as a draft that a qualified person owns.
Is it safe to put clinical trial data into ChatGPT?
No, not into any tool your organisation has not approved. Unblinded, interim or confidential clinical trial data, site-level subject data and personal data about patients or participants are off limits. Use enterprise tools approved by IT, security and legal teams. Free consumer accounts rarely meet that bar, so ask first.
Can general-purpose AI be used for GxP records?
No. The suitability table marks anything entering a GxP record or procedure as not for general-purpose AI. Follow your quality system and validation rules. The table is a starting point for your own policy, not a compliance determination, so check with your QA and regulatory team.
Which biotech tasks need expert validation when using AI?
Market size, competitor facts and financials need verification because models may invent figures. Interpreting regulations and clinical, safety or efficacy statements need domain validation by a regulatory professional or qualified clinical reviewer. Data analysis feeding a submission or decision needs validated tools and qualified statisticians.
How should I document AI use in a biotech team?
Record what you used AI for, which tool and version, and who reviewed the result. Your quality system may require this, for example where electronic records rules such as FDA 21 CFR Part 11 apply. Keep the source next to every claim, and name a reviewer with the right expertise.