AI Medical Writing Software Evaluation: Pilot Plan and Review Criteria

This article provides a reusable pilot plan and a complete fictional reference exercise for regulatory medical-writing teams. Copy the fields and source excerpts directly from the page. They are editorial resources, not an agency form, a clinical dataset or an executed vendor benchmark.

Evidence limit: We have not run this source task through the named products. The same-input vendor comparison remains unperformed. The worked outputs and timings below are explicitly invented teaching examples and cannot establish a product's accuracy or speed.

Disclosure: Assyro publishes this guide and is our first evaluation recommendation where its FDA eCTD v4.0 workflow fits. Assyro does not currently support eCTD v3.2.2. Editorial placement does not waive any pilot criterion.

A reusable AI medical writing pilot plan

Start with one document task, one source boundary and a named acceptance owner. “Improve medical writing” is too broad to evaluate. A useful first scope is “prepare a descriptive study-results paragraph from approved excerpts, identify missing evidence and route the draft for review.” Keep scientific interpretation, document formatting, publishing and transmission as separately assessed activities.

Use the following record for each candidate and configuration. The middle column is the reusable field specification; the final column is a completed fictional pilot design. “Not run” is an honest execution status, not an unresolved placeholder disguised as a result.

Confirm product identity before starting the trial. If the vendor will not disclose an underlying model version, record that limitation and an observable application release/configuration identifier. Do not fill the field with a guessed model. Record any retrieval, browsing, connector or template configuration that changes what the system can see or produce.

Copy this permitted synthetic source packet

All excerpts below are manually authored fiction. They contain no patient-level records and make no claim about a real medicine. Source labels and paragraph numbers are the reference locations for this exercise.

S1, protocol synopsis, revision 1, paragraph 1: Study M-17 is a single-arm descriptive study with twelve enrolled participants. The exercise reports a laboratory marker measured in U/L at Day 14. There is no comparator arm. This packet contains no planned or completed hypothesis test and does not establish treatment efficacy.

S2, results excerpt, revision 1, paragraph 1: All twelve participants have a Day 14 laboratory-marker observation. Mean change from baseline at Day 14 is −3.0 U/L. No confidence interval or p-value is supplied. The result is descriptive.

S3, safety excerpt, revision 1, paragraph 1: Two of twelve participants reported headache by Day 14. This excerpt does not provide causality assessments, a complete adverse-event listing or serious-adverse-event status. Do not infer that unreported event categories contain zero events.

Keep the following correction separate until the change-control exercise: S2, revision 2, paragraph 1 replaces the mean change of −3.0 U/L with −2.4 U/L following a fictional transcription correction. The twelve observations and descriptive interpretation are unchanged. Revision 2 supersedes revision 1 for newly prepared drafts; the original source must remain identifiable for reconstruction of earlier outputs.

Give every candidate this same task

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Using only S1, S2 and S3 revision 1, draft a descriptive results paragraph of no more than 120 words. Include enrollment, study design, observation time, mean laboratory-marker change and headache count. State the limits on efficacy and safety conclusions. Identify each factual statement's source and paragraph separately from the paragraph word count. List missing information needed for a stronger conclusion. Do not add outside evidence, calculate a p-value or assume that an unreported event did not occur.

Freeze the exact input and task before comparison. A vendor may map these excerpts into its supported input format, but preserve the mapping and count that preparation effort. If a candidate cannot accept this task without a different service or module, record the scope difference. Do not silently substitute a prepared clinical report and score the results as equivalent.

An acceptable editorial reference response, written for this article, is: “Study M-17 enrolled twelve participants in a single-arm descriptive study. At Day 14, mean laboratory-marker change from baseline was −3.0 U/L. Two of twelve participants reported headache by Day 14. These excerpts do not establish efficacy or provide a complete safety assessment.” The supporting locations are S1 paragraph 1 for design/enrollment, S2 paragraph 1 for the marker result and S3 paragraph 1 for headache and safety limitations. The packet lacks comparative evidence, inferential statistics, causality assessments and complete safety-event information.

The reference is not the only acceptable wording. Assess whether the meaning, evidence and limitations survive, rather than rewarding a model for copying this sentence structure. Keep the reference hidden from the candidate's generation input.

Measure the complete review workload

Define an accepted endpoint before timing begins: the authorized reviewer has resolved required content findings and the recipient can use the agreed export. Record source preparation, drafting/prompt iteration, factual review, correction, adjudication and export checks separately. Distinguish active labor minutes from elapsed waiting time. Several reviewers working simultaneously contribute separate labor, even when their clock time overlaps.

Here is an assumed timing example, not measured vendor performance. The same scoped task takes 20 minutes of source preparation, 100 minutes of manual drafting, 90 minutes of review and 30 minutes of correction/export in the baseline: 240 active minutes. An AI-assisted path takes 30 minutes of source preparation, 15 minutes of generation and prompt iteration, 160 minutes of review and 65 minutes of correction/export: 270 active minutes.

First-draft work fell by 85 minutes, but total work increased by 30 minutes, or 12.5%. That candidate would fail a predeclared “no increase in total active effort” criterion for this example, despite its faster first draft. A team may accept extra effort for another documented benefit, but must change the business decision openly rather than report a time saving that did not occur.

Record one-time setup and training separately from repeat-document work. Use equivalent reviewer qualifications and the same acceptance standard for baseline and assisted paths. Preserve every attempt, including abandoned outputs and vendor interventions. A comparison of an experienced vendor operator against an untrained internal baseline measures more than software.

This tiny packet calibrates the process; it is insufficient to establish production suitability. Extend the pilot with permitted examples of your actual document types, tables, difficult source formatting and legitimate missing information. Keep calibration examples separate from evaluation examples, and vary task order to reduce learning effects. Predeclare how repeated runs and incomplete tasks enter the analysis. Do not average away a material unsupported conclusion, omit failures or extrapolate from a paragraph exercise to an entire clinical study report.

Specialized AI medical writing software versus ChatGPT

Compare exact offerings and operated workflows. A specialized product may offer document-specific source mapping and review functions; a general-purpose enterprise workspace may offer strong administrative controls while leaving your team to establish the medical-writing process. Neither category earns an accuracy score from its product description.

Assyro is our first conditional evaluation candidate. Its confirmed scope includes FDA eCTD authoring, validation, review and submission with v4.0 support. Evaluate the proposed writing task within that scope and request the source, draft, correction and export evidence defined here. Confirm the specific scientific document type, citation handling, review permissions and history export during evaluation; those details cannot be inferred from broad workflow availability. Existing eCTD v3.2.2 publishing is unsupported, so that must-have excludes Assyro as its replacement.

Writing capability and submission eligibility are separate. FDA currently permits v4.0 for new CDER/CBER NDA, BLA, ANDA, IND and master file applications and says forward compatibility remains unavailable. An authoring pilot does not convert an existing v3.2.2 lifecycle or demonstrate an accepted submission. FDA eCTD scope.

Yseop Copilot is a specialized authoring candidate. Its current page describes generation from structured and narrative sources, work in Microsoft Word and Veeva Vault, section locking, source-change handling and built-in audit trails. Its listed document coverage includes clinical study reports, clinical safety/efficacy summaries and quality overall summaries. These are vendor descriptions of the offering, not results from this exercise. Yseop Copilot workflow.

That documented authoring scope makes it relevant when your pilot centers on those documents and existing writing environments. Ask which licensed configuration provides each function, whether your input is supported, and what survives export. Do not interpret a broad audit-trail statement as proof of your required reviewer-override history or final-approval permissions.

ChatGPT Enterprise is the general-purpose comparator here. OpenAI states that business data is not used to train its models by default and describes enterprise authentication, access and retention controls. This is an Enterprise-plan comparison; it does not transfer those terms to a personal account or every other plan. OpenAI enterprise privacy, updated January 8, 2026.

OpenAI also documents workspace compliance logs and metadata for Enterprise/Edu customers. Its Compliance Logs Platform retains data for 30 days; longer retention requires collection into an organization's own system. Consequently, “ChatGPT has no audit capability” would be an inaccurate comparison. The relevant question is whether your configured workflow retains the particular source, draft and approval evidence needed here. OpenAI Compliance Platform.

For a permitted short drafting task with review controlled elsewhere, evaluate whether an enterprise workspace plus that existing process is sufficient. For document-specific production authoring, require the specialized workflow to demonstrate reduced reconciliation work, usable exports and correct permissions. A general-purpose workspace's administrative logs do not themselves establish medical sign-off; a specialist label does not prove it either.

The same-input performance comparison is still open. No named product has generated the article's sample output, and no plan/model/build has been benchmarked. Run the shared task in the actual purchased configurations and retain the results before choosing on accuracy or review burden. Until then, the distinction above can narrow an evaluation, but it cannot support a tested winner.

A live AI medical writing demo checklist

Send the source packet and acceptance criteria before the session, but reserve a controlled source correction for the live exercise. Ask the demonstrator to identify the offered configuration and show the actual evidence as they work. Record who operates the product, who reviews it and whether staff intervene outside the visible workflow.

Use one record per check: result, evidence location, evaluator and follow-up owner. Pass requires inspected evidence; fail means observed behavior contradicts the criterion; unknown means the answer was not demonstrated. N/A needs a reason tied to the agreed scope. A prepared showcase may explain a feature, but cannot pass a requirement to perform your live task.

In a fictional demonstration record, the source inventory is visible and correct: pass. The presenter then substitutes a polished slide for live generation: fail for the agreed live-task check. They describe exportable history but do not show it: unknown. Gateway transmission is N/A because this session is explicitly a paragraph-authoring evaluation. None of those classifications is a result for a named vendor.

The evaluation owner asks for the live task to be repeated in the offered configuration and assigns the history-export question to the vendor. Keep the initial failed and unknown records. A later successful demonstration can close the action with new evidence; it should not erase what was originally absent.

Do not improvise progressively easier tasks until the tool looks good. If the input format is unsupported, document it and decide whether an agreed preparation step is acceptable. Time that step and preserve its output so the comparison still reflects your team's work. If a vendor demonstration uses a premium module outside the proposal, record the commercial dependency before giving that function credit.

Finish by asking the intended user to repeat one source check without the presenter navigating. That distinguishes an observable result from a workflow your reviewer can actually operate. Schedule a separate session for unresolved mandatory items rather than marking them passed because the meeting ended.

Benchmark accuracy with explicit claims and denominators

Define error categories before reading candidate output. For this exercise, a contradiction disagrees with the supplied evidence; an unsupported claim asserts something the packet cannot establish; an omission leaves out a required element; and a reference defect prevents verification or points to the wrong evidence. Numerical errors include wrong values, signs, units, populations or time points. Several defects can affect one claim, so preserve category counts without double-counting that claim in an overall error fraction.

Here is a deliberately flawed editorial paragraph, not output from a vendor trial: “M-17 enrolled twelve participants. It used a single-arm design. Observations were reported at Day 14. Mean marker change was −3.0 U/L. Two of twelve participants reported headache. The study was randomized. The result was statistically significant. No serious adverse events occurred.”

Treat each sentence as one atomic claim for this particular example. A real sentence may contain several factual clauses and need splitting before review.

Five of eight claims are supported: 62.5% supported-claim proportion. Three of eight are unsupported: 37.5% unsupported-claim proportion. Reporting “five verified claims out of five checked” as 100% would hide the very assertions the reviewer most needs to catch. These percentages describe this constructed paragraph only; they are not vendor accuracy scores.

Measure completeness separately. The task requires six content elements: enrollment, design, observation time, marker result, headache count and a statement of evidence limitations. This paragraph covers five and omits the limitations: 5/6, approximately 83.3% coverage. A tool that writes one correct sentence should not outperform a complete draft merely by making fewer claims.

For the actual pilot, remove vendor labels from outputs where practicable and randomize review order. Have two qualified reviewers classify claims independently using the same source packet and definitions, then retain disagreements and the adjudicated decision. State where formatting or product-specific references prevent full blinding. Record materiality separately from frequency; one invented safety conclusion can matter more than several formatting defects.

Also test a known contradiction: change the generated marker value to +3.0 U/L while retaining S2 revision 1. That sign is wrong. After replacing S2 with revision 2, −3.0 becomes a stale value for a new draft, while remaining the value behind the original revision-1 output. For each correction, retain the claim, governing source revision, exact passage and reviewer disposition. The pilot owner should resolve material findings, then inspect the corrected output again before release.

Reconstruct source, draft, review and approval history

An audit-history demonstration should answer a concrete question: can a reviewer explain why this released statement says −3.0 U/L when the current source now says −2.4? A list of login events or a final document's modification date cannot answer that alone.

Define the record you need for the intended use. Include source identity/revision, the generation task and available configuration identifiers, raw draft, human correction, reviewer disposition, approval decision and export identity. If evidence resides in several systems, require a stable relationship between those records. The application need not put everything in one PDF, but the contracted export and retained archive must support reconstruction.

This is the expected history for a fictional exercise; no vendor system has produced these events. All times are on the same synthetic day in UTC.

Inspect the recipient's copy. If D2, its source references and its approval record are present, those checks can pass. If the export omits the 10:10 rejection and the correction history required by your acceptance plan, that item fails even though D2 reads correctly. If no export has been shown, record unknown, not failure. Electronic signature execution can be N/A for this synthetic, non-release exercise if explicitly excluded; that exclusion cannot carry into a production use requiring such signatures.

Ask the recipient to reconstruct D2 without relying on the author's memory. They should distinguish the accepted historical draft from D3 awaiting review. A source correction must not silently rewrite what the earlier reviewer saw or convert an earlier approval into approval of newly generated text.

These are proposed evidence requirements, not a declaration that every listed event is mandated for every AI use. Have the responsible quality owner determine the applicable record controls and retention requirements. Test the actual configured workflow against them, including exports and reviewer overrides, before accepting an “audit-ready” description.

Apply the source-traceability checklist when a citation opens correctly but may not support the complete sentence.

Test author, reviewer and approver permissions

Name the permitted actions before evaluating role labels. For this pilot, the author may prepare and correct a draft, the subject-matter reviewer may document findings and recommend disposition, and a separate designated approver may release the reviewed version. This separation is the example team's chosen control; it is not a claim that identical roles are mandatory in every organization.

A product can call someone a “reviewer” while still granting them broad editing rights. Test actions using separate accounts representing the intended roles, with the actual production-like permissions. Include unsuccessful actions in the retained evidence. An administrator explaining that users “normally would not do that” is not evidence that the configured workflow prevents it.

In a fictional test record, the author successfully marks their own draft finally approved despite the agreed rule. Record fail, identify the account and permission configuration, and withhold workflow acceptance. If the vendor shows only a role-settings screen, the action outcome remains unknown. Repeat the negative action after a proposed configuration correction; retain both results so the change is auditable.

Use the same requirement for every candidate. Yseop's advertised section controls and audit trails are reasons to inspect the implementation, not proof of the table's denial outcomes. ChatGPT Enterprise's documented workspace administration and logs likewise do not establish the separate scientific approval step. Assyro's review workflow must demonstrate the same required actions; the publisher's preference cannot substitute for evidence.

A documented external approval system may satisfy the workflow if it receives the correct draft and evidence, and prevents an unapproved version from being treated as released. Evaluate that handoff, its staffing and its cost as part of the solution. Do not reject an otherwise suitable drafting tool solely because approval happens elsewhere; do not pretend the handoff is automatic if it is manual.

Choose the configuration that meets the defined control with evidence your team can retain. Keep mandatory permission failures separate from optional convenience features. A good editor or attractive generated paragraph cannot compensate for an approval route that contradicts your operating procedure.

Review storage, model processing and deletion separately with the AI medical-writing security checklist.

Move from a limited pilot to controlled production

Production readiness requires fresh confirmation of source permissions, user access, operating procedures, training and accepted evidence. A synthetic-only pilot does not establish authorization to upload real study documents. Before expanding the source scope, have the responsible security and data owners confirm the permitted data, access boundaries, retention, deletion and contractual handling conditions for the actual configuration.

Keep regulatory applicability precise. FDA's January 2025 AI guidance remains a draft, not for implementation. Its scope excludes operational drafting uses that do not affect patient safety, drug quality or study-result reliability; it does not declare every AI writing use exempt. Determine the intended use before applying its proposed framework. FDA draft guidance, section II.

Use event-based milestones instead of promising a universal rollout duration. The following schedule is a fictional internal plan measured in elapsed calendar days, not an agency clock or a vendor implementation commitment. Day 0 begins only when the pilot owner accepts the source scope, roles and evaluation protocol.

Under those assumptions the decision occurs at Day 15: 5 + 7 + 3. Training is already inside the first five days and is not added again. If source authorization delays configuration acceptance by four days, the subsequent seven-day pilot and three-day review move the earliest decision to Day 19. If the gap has no resolution date, there is no defensible revised date. Completion of a calendar interval never forces approval.

At release, record the allowed document types, source classes, configurations, reviewer responsibilities and escalation route. The fictional M-17 pilot must remain restricted if production-source authorization is missing or author self-approval still works contrary to the procedure. Passing the paragraph accuracy exercise does not close either gap.

During controlled use, retain examples of material errors and total review effort, and define who evaluates a changed model, connector, template or document scope. Stop or restrict the affected use when an agreed release condition no longer holds. Reassess that change with representative evidence; do not let a successful synthetic demonstration become permanent approval for every future workflow.

Use the plan before choosing a product

Copy the pilot record, source packet and acceptance checks into your evaluation. Resolve eligibility first, then compare complete reviewed outputs and total effort. For an eligible FDA v4.0 workflow, bring the scoped exercise to an Assyro evaluation; retain a compatible route for unsupported v3.2.2 work.

The documentary comparison is ready to guide that work. A tested product recommendation still requires the same-input runs, identified configurations, original outputs and reviewer records that this article does not yet have.

Bring the same scoped task to Assyro’s regulatory-writing evaluation and retain the demonstrated evidence rather than relying on a feature description.

Carry the accepted scope and unresolved findings into the AI regulatory-writing RFP workbook before comparing proposals.