Best for
- Drafting original research manuscripts from clinical study data
- Writing review articles or narrative reviews from a defined evidence base
- Preparing conference papers or short communications
- Publication support drafting where the medical writer works from a data package and author input
- Creating first-draft sections (Introduction, Discussion) to accelerate the authoring timeline
Inputs
- Study data (CSR, data tables, statistical outputs, or published results)
- Reference library (full text of key citations)
- Author guidance on narrative direction, key messages, and target journal
- Target journal author guidelines (word limits, section structure, reference style)
- Any existing outline or messaging framework
Steps
Confirm the outline and narrative
Start from an approved outline or build one using the Build a Content Outline workflow. The outline should define what each section covers, which data supports it, and the overall narrative arc. Do not start drafting without an agreed structure.
Draft the Methods section
Methods is the most mechanical section and the safest starting point for AI. Provide the study design details and let AI structure them into standard reporting format. Verify against the protocol or CSR, not just the data tables.
Draft the Results section
Provide the statistical outputs and data tables. AI structures the results into text, tables, and figure legends. Verify every number. This section has the highest density of values that can be transposed, rounded, or misattributed.
Draft the Introduction
Provide the key references and context. AI generates a structured introduction that positions the study within the existing evidence. Check that every contextual claim cites a specific reference and that no background information comes from AI training data.
Draft the Discussion
This is the section requiring the most human input. AI can structure the discussion around key findings, but the interpretation, clinical implications, and limitations require author and medical writer judgement. Use AI for a structural first pass, then rewrite substantially.
Compile and verify
Assemble all sections. Run Verify Claims Against References on the full manuscript. Check internal consistency (do the Results match the Abstract? do the Discussion conclusions match the data?). Confirm the manuscript meets the target journal’s guidelines.
Output
A complete manuscript draft in IMRaD format (or journal-specified structure) with all sections populated, data accurately represented, references correctly cited, and internal consistency maintained. The draft is ready for author review and revision, not for submission. No manuscript drafted with AI support should be submitted without thorough author review and sign-off.Prompt pattern
Why this works
AI accelerates the mechanical parts of manuscript drafting: structuring Methods from a protocol, converting statistical tables into Results text, and organising Introduction references into a coherent narrative. These are time-consuming tasks where the content is largely determined by the source data. The human writer and authors handle the parts that require scientific judgement: interpreting results, contextualising findings, acknowledging limitations honestly, and ensuring the manuscript tells a coherent story that the data actually supports.Common mistakes
Unsourced claims in the Introduction
Unsourced claims in the Introduction
AI adds a sentence about disease prevalence or standard of care from its training data, not from a cited reference. Every factual statement in the Introduction must cite a specific source. Insert [REF NEEDED] for any claim that lacks a citation, then find the reference or remove the claim.
Results that do not match the data
Results that do not match the data
AI converts a data table into prose and transposes the treatment and placebo arm results. The Results section has the highest error density in AI-assisted drafts. Verify every number against the original statistical output or data table.
Discussion that overstates findings
Discussion that overstates findings
AI writes “This study demonstrates that Drug X is superior to standard of care” when the study was powered for non-inferiority. The Discussion must reflect exactly what the data shows, using the same language the statistical analysis supports.
Internal inconsistency across sections
Internal inconsistency across sections
The Abstract reports a different p-value than the Results. The Discussion references a secondary endpoint that is not mentioned in the Results. After assembling all sections, read the manuscript end-to-end and cross-check key values across sections.
Skipping author review
Skipping author review
Tool stack
Alternatives: Claude or ChatGPT for section drafting from source materials. Zotero or EndNote for reference management and citation formatting.
Review checklist
Human review checklist
Human review checklist
- Every data point in Results matches the source data exactly
- Every factual claim in the Introduction cites a specific reference
- No background information has been added from AI training data
- The Discussion accurately reflects the strength of the evidence (no overstatement)
- Limitations are discussed honestly and proportionately
- Internal consistency across Abstract, Results, and Discussion is confirmed
- The manuscript meets the target journal’s author guidelines (word count, structure, reference format)
- Author review and input has been obtained on interpretation and conclusions
- All [VERIFY] and [REF NEEDED] flags have been resolved
- The manuscript is ready for author sign-off, not just AI-complete
Next steps: Run Check Document Consistency to catch internal mismatches, then Verify Claims Against References. For regulatory submissions, see Draft a Regulatory Document. Complete Final Human Review before submission.