> ## Documentation Index > Fetch the complete documentation index at: https://playbook.pharmatools.ai/llms.txt > Use this file to discover all available pages before exploring further. # Human-in-the-Loop Decision Making > Every deliverable has a named owner. AI produces working material; a qualified professional verifies, edits, and signs off. ## Core principle Every deliverable has a named owner. AI produces working material (structured drafts, candidate summaries, flagged issues) that a qualified professional reviews, edits, and approves. At no point in any workflow does AI output go directly into a deliverable without human verification. This is not a disclaimer appended to make AI use sound responsible. It is a structural requirement embedded in every workflow card: defined review points, specific verification steps, and documented sign-off. *** ## What this means in practice ### What AI handles well in med comms workflows * Producing a structured first-draft summary of a Phase III paper in 2 minutes instead of 45 * Generating a candidate content outline from a briefing document and key message set * Adapting a specialist-level summary for a GP or nurse audience * Scanning a 20-page detail aid for language patterns commonly flagged in MLR review * Extracting study design, endpoints, and results from a congress poster into a structured format ### What AI cannot do — and should never be trusted to do * Confirm that a hazard ratio, p-value, or confidence interval in a summary actually matches the source paper * Determine whether a key message crosses from scientific education into promotional territory * Decide which endpoints to foreground in a slide deck for a specific advisory board audience * Judge whether a plain language summary accurately represents the benefit-risk balance for patients * Bear accountability when a deliverable is signed off and sent to a client, regulator, or MLR committee *** ## Decision points in every workflow Each workflow card in this playbook includes explicit sections: * **Where AI helps** — bounded, specific tasks where AI adds value * **Where human judgement is essential** — points where a trained professional must review, verify, or decide * **Human review checklist** — a practical checklist for the review step These are not optional. Skipping the human review step turns an AI-assisted workflow into an AI-dependent one, which introduces unacceptable risk in medical communications. *** ## Why this matters for medical writing Medical writing operates in a space where: * **Accuracy is non-negotiable.** A misrepresented endpoint, an overstated efficacy claim, or an omitted safety finding can have real consequences — for patients, for prescribers, and for regulatory standing. * **Context is everything.** The same data point can be appropriate in a journal manuscript, misleading in a promotional piece, and incomprehensible in a patient leaflet. Only a trained professional can make that judgement. * **Accountability is personal.** When a document is signed off, a named individual is accountable for its accuracy and compliance. AI cannot bear that accountability. *** ## How to implement this principle 1. **Never submit AI-generated text without expert review.** This applies to every deliverable: an internal summary, a client-facing slide deck, a congress highlights report. No exceptions by risk tier. 2. **Document where AI was used.** Track which sections were AI-assisted in your project files. This is not bureaucracy; it tells the reviewer where to focus verification effort and supports client transparency. 3. **Review against sources, not just for readability.** AI output reads fluently. That is the danger. A summary that sounds authoritative can contain transposed data points, merged study arms, or conclusions the authors did not draw. Always verify claims against the original source materials. 4. **Treat AI output as a working draft.** The value is reaching a reviewable draft faster — getting from a blank page to a structured starting point. The review itself is not shortened; in some cases, it requires more attention, not less. 5. **Maintain clear sign-off protocols.** The person who approves the final deliverable owns its accuracy, compliance, and completeness. The fact that AI was involved in production does not change their accountability. *** ## Common failure modes | Failure | What it looks like in practice | How to prevent it | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Over-trust | A writer accepts an AI-generated summary without checking it against the paper. The summary transposes a primary and secondary endpoint result. The error enters a client slide deck. | Verify every data point against the source. Treat AI output the same way you would treat a junior writer's first draft — it needs line-by-line checking. | | Automation bias | MedCheckr flags no issues on a promotional piece. The writer assumes it is clean. MLR catches an unsubstantiated comparative claim the tool missed. | Automated screening is one input. It catches patterns, not context. The writer's own compliance review still applies. | | Accountability gap | An agency uses AI across multiple writers on a project. No one is clearly responsible for verifying the AI-assisted sections. A hallucinated data point reaches the client. | Assign a named reviewer to every AI-assisted deliverable. Document which sections used AI and who verified them. | | Review fatigue | A medical writer reviews five AI-generated summaries in a row. By the fourth, they are skimming. An incorrect sample size passes through. | Use the structured checklist for every review. Batch AI-assisted QC in manageable sets. Do not review more than three AI-generated documents without a break. | *** ## For agencies and teams If you are implementing AI workflows across a team or agency: * Establish minimum review standards for AI-assisted content at each risk tier and write them into your SOPs * Train writers and reviewers on the specific failure modes of AI in medical writing (hallucinated data, meaning drift, omitted qualifiers), not just generic "AI limitations" * Track AI use in project management systems so reviewers, account leads, and clients have visibility * Brief client services teams on how to discuss AI-assisted workflows with clients. Lead with the review framework and risk tiers, not the speed * Do not position AI as a way to reduce QC time or headcount. Position it as a way to produce more reviewable first drafts, improve consistency across deliverables, and free up writer time for the work that requires expert judgement *** *Last reviewed: 15 April 2026 · 6 min read*