> ## 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.

# AI Risk Framework for Medical Writing

> A practical framework for deciding where AI can assist, where it needs stricter review, and where human authority must remain primary.

<Info>
  AI can accelerate structured writing tasks. Clinical interpretation and regulatory judgement remain human responsibilities.
</Info>

## Purpose

This framework provides a practical model for deciding how much AI involvement is appropriate for a given task, and what review it requires.

For an overview of *why* AI risk varies across medical writing tasks, common failure modes, and what makes a task higher risk, see [Understanding AI Risk](/principles/risk-levels).

***

## The framework

Four tiers classify AI use by the impact of error and the degree of human judgement required.

| Tier                             | Role of AI                       | Impact if wrong                                             | Human involvement                      |
| -------------------------------- | -------------------------------- | ----------------------------------------------------------- | -------------------------------------- |
| **1. Assistive**                 | Structure, search, organise      | Low — correctable in standard review                        | Writer reviews and refines             |
| **2. Structured transformation** | Adapt, convert, reformat         | Medium — meaning can drift without detection                | Detailed review against source         |
| **3. Evidence-critical**         | Extract, verify, draft from data | High — incorrect data propagates downstream                 | Expert verification of every value     |
| **4. Human authority required**  | Supporting role only             | Very high — regulatory, clinical, or compliance consequence | Human controls the process; AI assists |

### How to read the tiers

AI use becomes higher risk as either:

* **the impact of being wrong increases** (a transposed HR in a CSR vs. a formatting error in an outline)
* **the task requires more interpretation** (reporting a number vs. drawing a clinical conclusion from it)

The same workflow can sit in different tiers depending on how AI is used. Outlining a manuscript (Tier 1) is not the same as drafting the Discussion section's interpretation of results (Tier 3–4).

***

## Workflow mapping

Where each playbook workflow sits in the framework. Ranges indicate that the tier depends on how AI is used within the workflow.

| Workflow                                                            | Tier | Notes                                                                     |
| ------------------------------------------------------------------- | ---- | ------------------------------------------------------------------------- |
| [Find Evidence](/workflows/find-evidence)                           | 1    | AI supports search strategy; human selects sources                        |
| [Summarise a Paper](/workflows/summarise-source-paper)              | 1    | AI structures; human verifies data points                                 |
| [Congress Summary](/workflows/prepare-congress-or-poster-summary)   | 1–2  | Extraction is Tier 1; contextualisation is Tier 2                         |
| [Extract Study Data](/workflows/extract-study-data)                 | 3    | Every extracted value must be verified against source                     |
| [Extract Key Messages](/workflows/extract-key-messages)             | 3    | Message framing and evidence strength require expert judgement            |
| [Build an Outline](/workflows/build-content-outline)                | 1    | Structural task; low consequence if refined                               |
| [Write a Manuscript](/workflows/write-a-manuscript)                 | 2–3  | Methods/Results drafting is Tier 2; Discussion interpretation is Tier 3–4 |
| [Draft a Regulatory Document](/workflows/draft-regulatory-document) | 4    | Regulatory wording and interpretation require human authority             |
| [Convert Stats to Narrative](/workflows/convert-stats-to-narrative) | 2–3  | Mechanical conversion is Tier 2; verifying accuracy is Tier 3             |
| [Create a Slide Deck](/workflows/create-medical-slide-deck)         | 2    | Content reformatting with meaning-drift risk                              |
| [Adapt for Audiences](/workflows/adapt-for-different-audiences)     | 2    | Simplification can change meaning without visible errors                  |
| [Plain Language Summary](/workflows/create-plain-language-summary)  | 2    | Patient-facing; oversimplification risk                                   |
| [Verify Claims](/workflows/verify-claims-against-references)        | 3    | Verification accuracy has direct downstream impact                        |
| [Compliance Check](/workflows/check-promotional-compliance)         | 4    | Compliance judgement cannot be delegated to AI                            |
| [Check Document Consistency](/workflows/check-document-consistency) | 2    | AI flags candidates; human confirms true inconsistencies                  |
| [Repurpose Content](/workflows/repurpose-content-across-channels)   | 1–2  | Reformatting is Tier 1; channel-specific adaptation is Tier 2             |
| [Final Review](/workflows/final-human-review)                       | 4    | The sign-off is a human responsibility                                    |

These mappings are not absolute. Risk depends on the source material, the context of use, the level of human review applied, and the consequences of error for the specific deliverable.

***

## Review expectations by tier

What human review should look like at each level.

### Tier 1 — Assistive

* Confirm relevance and completeness
* Verify key data points against sources
* Check that structure matches the deliverable purpose
* Standard medical writing review is sufficient

### Tier 2 — Structured transformation

* Detailed comparison between source and output
* Check for meaning drift, dropped qualifiers, and shifted emphasis
* Verify that safety information is preserved proportionately
* Cross-check every clinical claim against the original

### Tier 3 — Evidence-critical

* Expert verification of every numerical value against source data
* Confirm analysis populations, endpoint definitions, and statistical measures are correctly attributed
* Verify that no AI-generated interpretation has entered the output
* Spot-check unflagged content as well as flagged items

### Tier 4 — Human authority required

* Human controls the process from the start; AI assists with specific tasks (formatting, structuring, locating information)
* Expert review of all wording, interpretation, and conclusions
* No AI output enters the final deliverable without explicit human approval
* Formal sign-off by a qualified professional

***

## Where AI should not be the deciding layer

Regardless of tier, AI should not serve as the final authority for:

* **Clinical interpretation** — whether a result is clinically meaningful
* **Regulatory conclusions** — whether a document meets applicable guidance
* **Benefit-risk assessment** — weighing efficacy against safety for a specific population
* **Compliance sign-off** — whether content meets the requirements of a promotional code
* **Final approval** — the decision that a deliverable is ready for submission, publication, or external use

These decisions require human expertise, accountability, and professional judgement. AI can provide supporting information, but a human must own the conclusion.

***

## Using this framework in practice

**For individual writers:** Before starting an AI-assisted task, identify which tier it falls into. Match your review effort to the tier. If you are unsure, default to the higher tier.

**For team leads:** Use the tiers to set expectations for AI-assisted work across your team. Define which tiers require senior review, which require source cross-checks, and which require formal sign-off.

**For client or stakeholder conversations:** The framework provides a clear, defensible answer to "how do you ensure AI-assisted content is accurate?" You can point to the tier, the review process, and the accountability structure.

**When a task spans multiple tiers:** Many real-world deliverables involve tasks at different tiers. A manuscript has Tier 1 outlining, Tier 2 Methods drafting, and Tier 3–4 Discussion interpretation. Apply the appropriate review to each component, not a single blanket review to the whole document.

***

## Related principles

* [Human-in-the-Loop Decision Making](/principles/human-in-the-loop) — every deliverable has a named owner
* [Source Grounding](/principles/source-grounding) — every claim traces to a cited source
* [Understanding AI Risk](/principles/risk-levels) — why risk varies across tasks, failure modes, and risk factors
* [Review and Accountability](/principles/review-and-accountability) — sign-off protocols and audit trails

## Related workflows

* [Verify Claims Against References](/workflows/verify-claims-against-references) — Tier 3 verification in practice
* [Draft a Regulatory Document](/workflows/draft-regulatory-document) — Tier 4 drafting with human authority
* [Convert Stats to Narrative](/workflows/convert-stats-to-narrative) — Tier 2–3 mechanical translation
* [Final Human Review](/workflows/final-human-review) — the Tier 4 sign-off process

***

*Last reviewed: 15 April 2026 · 6 min read*