AI for Document Review in Pharma

In most industries document review is an administrative step. In yours it is a regulated activity with a name attached. Someone reviewed, someone approved, and the audit trail says when. That signature is the product of the review, and it is why the work cannot be handed to software.

It is also why the backlog is stubborn. The number of controlled documents overdue for periodic review is not high because people are slow. It is high because the reading is enormous, the cross referencing is manual, and the only people qualified to sign are the same people running everything else.

Four activities hiding inside one review cycle

Pull apart any review cycle and you find four activities in different proportions.

• Finding the documents in scope: a specification, three work instructions and a supplier agreement all touching one change.
• Comparing a document against a source of truth: approved label text, a protocol, a master formula, a commitment made in a submission.
• Drafting what moves the cycle on: the review record, the response, the redline, the impact assessment.
• Judging. Is this acceptable, is it defensible to an inspector, does it go out under my name.

The first three are where the time goes. The fourth is where the accountability sits. An agent that does the first three well, with its sources visible, leaves the reviewer a decision rather than a pile. An agent allowed to drift into the fourth creates a problem you will not enjoy explaining.

Controlled documents whose review date has passed

Controlled documents carry review dates. Standard operating procedures, work instructions, specifications and quality agreements come round on a cycle, and the honest state of most electronic document management systems is that a meaningful share of those dates have passed. The cycle slips because the default action, confirming a document is still fit for purpose, still requires somebody to read it and mean it.

Most of that reading is checking for drift rather than reassessing the science: a department reorganised last year, a superseded reference, replaced equipment, a change implemented without anyone updating the procedures describing the old way.

An agent can prepare each overdue document for its reviewer: the current effective version, every change control closed against the affected process since the last review, every deviation and CAPA that cited the document, every cross reference that no longer resolves, and each inconsistency with its paragraph and conflicting source. The reviewer then does the part that needs them, deciding whether no change is genuinely the right answer. Triage of this kind also surfaces duplicated procedures and orphaned work instructions nobody owns.

What else does this change touch

The hardest question in a change control packet is the simple one: what else does this affect. A supplier changes a component, a method is updated, a line is requalified. A dozen documents somewhere describe the old state, and the assessment is only as good as the memory of the people in the room.

This is the clearest agent use case in quality, because the search is wide, repetitive and verifiable. An agent can read the change description and return every controlled document referencing the affected material, equipment, method, specification or supplier, with the referencing text quoted so the assessor judges relevance in seconds. It can separate documents that merely mention the item from documents whose instructions become wrong once the change is live.

That output is a candidate list with evidence, not a conclusion: the assessor still decides scope, still decides whether validation or stability work is needed, and still signs. The list simply starts complete rather than with whatever was recalled on the day.

Dossier modules and health authority questions

Submission work has a different failure mode. Nothing is overdue, everything is urgent, and inconsistency across thousands of pages earns you a question you did not want. An agent is useful in two places before filing. The first is internal consistency: the same batch numbers, specifications, limits, study identifiers and manufacturing descriptions appearing the same way in every module and supporting report. The second is traceability: for a statement in a summary, pointing to the report, table or record that supports it, and flagging where that link cannot be made.

When questions arrive, the agent's job is retrieval and assembly, not authorship. It can locate every prior statement your organisation has made on the subject, across modules, previous responses, commitments and inspection correspondence, so the response does not contradict something filed four years ago. It should not write the final answer, and it should not be trusted to characterise what a regulator is asking for. That is the last thing to attempt here, not the first.

Clinical documents and consistency checking

Protocols, amendments, statistical analysis plans, clinical study reports and investigator brochures have to agree with one another, and they are written by different people at different times under schedule pressure. Endpoint definitions drift between protocol and report, and an amendment changes an eligibility criterion while the brochure keeps the old one.

Consistency checking at volume is mechanical work an agent does the same way every time: numbers against source tables, definitions against their first authoritative statement, cross references against the current version of what they point to. Medical writing keeps the narrative and the interpretation.

Executed records, deviations and supplier files

For executed records and deviation reports, an agent is most valuable finding discrepancies against other systems of record and checking the narrative contains what the procedure requires, with both sides of each discrepancy shown so the reviewer decides rather than hunts for context. Supplier files are quieter and often worse: certificates expire, audit reports sit unassessed, quality agreements name obligations nobody tracks. An agent can hold a live view of what is on file, what has lapsed, and where a supplier's process no longer matches what you approved.

Promotional, medical and label review

The MLR cycle

Medical, legal and regulatory review behaves like a queue, and most of its iterations are not about judgement. They are about claims not substantiated by the referenced source, references that do not say what the slide claims, outdated safety information, fair balance that has thinned across versions, and local requirements applied inconsistently between markets.

An agent can run that pass before the material reaches the reviewers: every claim mapped to its cited reference with the supporting sentence quoted, every departure from the approved product information flagged, every reused asset checked against the label in force now rather than when it was made.

Label and artwork proofing

Artwork proofing against approved text is close to the ideal task for machine comparison. It is character level, high consequence, and human attention degrades where errors hide. An agent can compare proof against approved text across every market variant, flag every difference including punctuation and strength expression, and track which approved version each variant was built from. The sign off stays with the person who always held it.

Screening the literature without discarding a case

Literature screening is a volume problem with an unbalanced cost: thousands of abstracts, a small number of hits, and an obligation to recognise a case report. An agent can prioritise and summarise the queue, identify probable individual case safety reports, and extract the fields a case needs. It must not discard. Treat it as triage over a review that still sees everything.

Validation, audit trail and the agent itself

The agent is a computerised system inside your quality system, not a tool outside it. It inherits the expectations you already apply: intended use defined, risk assessed, qualified in your environment, change controlled, access controlled, and producing records that are attributable, legible, contemporaneous, original and accurate.

Three design choices make that tractable. Keep the agent inside your existing systems, so the record of the review lives where it always lived. Make every assertion traceable to a source document and version, because an output a reviewer cannot verify is one they have to redo. And treat the agent's own evidence as a primary output: which checks ran, against which versions, with what result.

Where AI fails here

The agent is in scope. Your prompts, retrieval configuration, document index and model version become controlled items with change control and periodic review of their own. That is a recurring cost, and on a small document estate it will exceed the benefit.

A confident wrong citation is worse than a slow correct one. In a response to a health authority, a cross reference to a report that does not say what the response claims invites a deeper look at everything else. Fluent output is what makes a wrong answer survive review, so require the quoted source next to every claim.

No agent output carries a signature. Approval is a human act tied to a qualified person's accountability. Watch for the slower version of that problem: once the agent's pass is usually right, it quietly becomes the review rather than the preparation for it.

Anything an agent writes into a controlled record carries the full weight of data integrity expectations. If it cannot be attributed, if its sources cannot be reconstructed, if a draft can be regenerated without a trail, you have made the record worse.

In safety literature screening the asymmetry is the whole problem. A false positive costs a reviewer a minute. A false negative is a missed case report. Any configuration that lets the agent exclude material without human sight of it is wrong, whatever the measured accuracy says.

Finally, an agent cannot repair a document hierarchy nobody maintains. If ownership is unclear, if superseded versions circulate, if half the estate is scanned attachments without usable text, the agent will reflect that mess faithfully and at speed. Showing the problem is not fixing it.

Where to start

Pick one process that touches no submission. Periodic review triage is the usual honest first deployment: the documents are already overdue, the agent prepares rather than decides, the reviewer's judgement is unchanged, and you can measure before and after in cycle time and in findings the reviewer would have missed. Change control impact search is the other good candidate, and assessors feel the difference at once.

Run either in shadow first against cycles already closed. Two numbers matter: what the agent found that the reviewer did not, and what the reviewer found that the agent did not. They tell you whether to widen, and they start your qualification evidence.

Adopt AI Partners builds these agents inside your own quality and regulatory systems, with your document control, your approval routing and your audit trail, and stays until your team owns them. If document review is the step your cycles wait on, describe the process to Adopt AI Partners at hello@adoptaipartners.com and we will give you a straight read on which parts an agent could carry and which should never leave your reviewers.