Generative AI can draft a standard response document in minutes, screen a thousand abstracts in an afternoon, and summarize a data set before the meeting that requested it ends. It can also invent a citation that does not exist, misstate a contraindication, and leak confidential data into a system you do not control. Both facts are true, and medical affairs teams need a working position that takes both seriously.
Where AI genuinely helps today
- Literature triage. Screening titles and abstracts against inclusion criteria is the most defensible use in evidence work. AI assisted screening, with human review of borderline calls and a validation sample, can cut first pass screening time substantially in systematic literature reviews without changing the final evidence base, because every included study is still verified by a human reader.
- First drafts of structured documents. Standard response documents, plain language summaries, and internal briefing notes have predictable structures that AI drafts competently from verified source material, leaving reviewers to focus on accuracy and nuance rather than blank pages.
- Summarization and reformatting. Converting a clinical study report section into a slide narrative, or a congress abstract into an internal alert, is low risk when the source is supplied and the output is checked against it.
- Medical information workflows. Retrieval systems that answer from an approved content library, and only from it, can speed medical information responses while keeping every claim traceable to approved wording.
Where it fails, and why the failures are dangerous
Large language models generate plausible text, not verified text. In medical content that distinction produces specific failure modes:
- Fabricated references. Models produce authors, titles, and journal names that look real and are not. Any AI drafted document with citations must have every reference retrieved and read by a human before it moves forward.
- Subtle clinical errors. The dangerous mistakes are not absurd; they are a dose off by one step, a contraindication softened, an indication broadened by a single adjective. Reviewers skimming for obvious errors miss exactly these.
- Confidentiality exposure. Pasting unpublished trial data or patient level information into a public AI tool can place it outside your control and outside your data protection commitments. Consumer AI tools and enterprise deployments with contractual data protections are different risk categories and should be treated as such.
- Uniform confidence. Models present their weakest output with the same fluency as their strongest, which defeats the informal cues human reviewers rely on to know where to look harder.
A governance framework that actually works
- Human accountability is non-negotiable. A named, qualified reviewer signs off every externally facing document, and that reviewer verifies content against source data, not against the AI draft. Regulators and journals hold people accountable; AI assistance does not dilute that.
- Approved tools only. Maintain a short list of permitted platforms with appropriate data protection terms, and an explicit prohibition on entering confidential or personal data anywhere else.
- Task level risk tiers. Internal summaries, external scientific content, and regulatory documents carry different consequences. Define which tasks allow AI drafting, which allow AI assistance with mandatory verification, and which exclude it.
- Documented process. Record where AI was used in a document's lifecycle. This protects you in audits and disclosure discussions, and several journals now ask explicitly.
- Validation before scale. Pilot each use case against human only output, measure error rates and time saved, and expand only what survives the comparison.
- Watch the regulatory horizon. The EU AI Act, evolving journal policies on AI disclosure, and data protection law all touch these workflows, and positions taken today should be revisited on a schedule.
A worked example of task tiers
Abstract policies fail at the moment someone has a deadline. Task level rules survive. A tiering that has worked in practice looks like this. Tier one, AI drafting permitted with standard review: internal meeting summaries, first pass literature triage with human verification of inclusions, reformatting of already approved content. Tier two, AI assistance permitted with mandatory source verification and documented human sign off: standard response documents, plain language summaries, congress alert summaries, first drafts of non-promotional educational content. Tier three, AI drafting excluded, assistance limited to language editing of human written text: regulatory submission documents, safety narratives, anything containing unpublished patient level data, and responses to health authority questions. The tiers are debatable at the margins, and that is the point: the debate happens once, in policy, rather than fifty times a week under deadline.
The disclosure landscape is settling
Externally, expectations are converging. Journals following ICMJE guidance require disclosure of AI use in manuscript preparation and prohibit AI authorship. The EU AI Act phases in obligations for providers and deployers of general purpose and higher risk AI systems, and while most medical affairs drafting uses fall outside the high risk categories, transparency obligations and internal governance expectations still apply. Data protection law applies with full force regardless: personal data entered into an AI tool is processing, and it needs the same lawful basis and safeguards as processing anywhere else. Teams that documented their AI use from the start are finding these requirements administrative; teams that did not are reconstructing histories.
The realistic position
The teams getting value from AI in medical affairs are not the ones using it most aggressively or refusing it entirely. They are the ones who matched it to tasks where verification is cheap and the source material is controlled, and kept clinician judgment where the cost of a subtle error is highest. Speed and integrity are not opposites; they are sequenced. AI accelerates the draft, and disciplined human review remains the product.
How EvySaif uses this in practice
EvySaif deliverables are clinician led and human verified regardless of what tools assist the drafting, with every claim traced to source data and every reference read before it is cited. If you are building an AI policy for your medical affairs or medical review function and want a partner who has thought through the failure modes, talk to us.