Yes, ChatGPT can help with a medical literature review, but expert control, source checks, and full disclosure are non-negotiable.
Researchers want speed without losing rigor. A large language model can draft text, shape outlines, and point to gaps. Still, clinical claims live and die by evidence. The right way blends human method with smart tooling. This guide shows a safe, repeatable workflow that keeps accuracy, transparency, and audit trails tight from start to finish.
What ChatGPT Can And Cannot Do In A Medical Review
Use the model for pattern spotting, summarizing, and drafting plain-language prose. Keep judgment calls, inclusion decisions, and final interpretations with trained reviewers. The table below maps tasks to safe uses and common pitfalls so you can plan your workload.
| Task | Helpful Use | Watch-outs |
|---|---|---|
| Scope & question shaping | Brainstorm PICO elements and variant terms | Leading prompts can bias the frame |
| Search string drafting | Create seed Boolean lines and MeSH ideas | Hallucinated terms and missed synonyms |
| Screening support | Generate draft criteria templates | False positives; must keep dual human screen |
| Data extraction notes | Draft field lists and codebooks | Inconsistent schema unless locked by humans |
| Summaries | Lay summaries of included studies | Fabricated quotes or numbers if unchecked |
| Writing | Plain-language first drafts and transitions | Source drift; needs strict citation control |
| Editing | Clarity edits, tone, and de-jargon | Over-smoothing that blurs nuance |
| Tables & figures | Draft layouts and labels | Wrong units or footnotes if unsupervised |
Writing A Medical Literature Review With ChatGPT: What Works
This section lays out a stepwise plan. Each step keeps the human in charge and pins every claim to a verifiable record.
Plan The Question And Outcomes
Lock the clinical question before any prompts. Write the PICO. List primary and secondary outcomes. Note subgroups. Save this prereg plan in your project folder. Use the model only to suggest alternative phrasing or missing synonyms.
Build Searches In Real Databases
Draft seed strings with the model, then run real searches in PubMed, CENTRAL, Embase, and subject indexes. Turn every final line into a saved strategy with dates, limits, and database names. Keep a copy of the raw export. If you use Clinical Queries or LitSense filters, record the exact settings.
Screen With Human Oversight
Two reviewers should screen titles and abstracts in duplicate. The model can suggest reasons for exclusion, but humans confirm. Keep a log that shows counts at each stage so your PRISMA diagram stays exact.
Extract And Check Data
Create a codebook before extraction. Ask the model to propose field labels or units. Then freeze the template. Extract data in pairs for a sample and spot-check the rest. Run a logic pass to catch impossible values.
Synthesize And Write
Use the model to draft neutral prose that mirrors your tables. Keep numbers only from your extraction sheet. Ask for plain-language blurbs that explain effect size direction, study designs, and risk of bias. Do not let the tool insert new citations on its own.
Disclose, Attribute, And Keep Records
State how the tool was used in your methods. Keep prompts, versions, and settings in your repository. Add a short disclaimer about AI assistance per journal rules. Credit only humans as authors.
Ethics, Policy, And Journal Rules You Need To Meet
Medical journals expect transparency. Many follow ICMJE rules that require disclosure of any use of AI tools in writing or analysis. They also forbid listing a model as an author. Reporting standards for reviews expect complete methods and exact search details. Link your statements to public checklists and handbooks accepted by editors.
Two anchors to keep near your desk: the ICMJE guidance on AI-assisted technology and the PRISMA 2020 checklist. Those pages spell out disclosure, authorship, and reporting items in plain terms.
Prompt Patterns That Produce Verifiable Output
Good prompts are concrete and auditable. Tie each request to inputs you control, and ask for outputs that cite only your corpus.
For Seed Search Lines
“Suggest Boolean synonyms for these PICO elements. Limit to MeSH where possible. Output a line for sensitivity and one for precision. Do not invent new terms.” Paste your terms and require a table with fields for concept, synonyms, and MeSH.
For Structured Summaries
“Summarize this abstract into design, population, intervention, comparator, outcomes, follow-up, and notable limits. Keep numbers only from the text. No new references.” Feed one abstract at a time to keep traceability.
For Draft Paragraphs
“Write two short paragraphs that paraphrase rows 3–7 of Table 1 in my sheet. Keep effect size labels and units. Do not add sources.” Always point the model at your vetted table, not the open web.
Quality Controls That Keep You Out Of Trouble
Every model draft needs checks. Build a checklist that catches the usual failure modes. Run it before peer review and again after revisions.
Citation And Fact Checks
Confirm that each numeric claim maps to a row in your data. Ban free-text citations from the model. Use your reference manager to insert DOIs and PMIDs. If a sentence has no supporting row or PDF, delete it.
Bias And Balance
Ask the tool to flag absolute language and over-confident verbs. Add both positive and negative trials when present. Write limits in plain terms. State when evidence is sparse or indirect.
Reproducibility
Save prompt files, model versions, and temperature settings. Export the chat as a PDF and place it next to your code and data. That archive supports audits and journal queries.
Common Pitfalls With LLM-Assisted Reviews
Three patterns cause most rework. First, letting the tool “search” on its own. That produces unverifiable claims. Second, copy-pasting draft references. That leads to phantom papers. Third, mixing phrasing help with conclusion changes. Keep each request narrow and tied to evidence.
Workflow: Human Steps And AI-Assisted Steps
Use this quick map to set roles before you start. Assign owners by name so tasks do not drift.
| Stage | Human Lead | AI-Assisted Support |
|---|---|---|
| Question & outcomes | Clinician + methodologist | Wordsmith variants and synonyms |
| Search strategy | Librarian | Seed strings and MeSH prompts |
| Screening | Two reviewers | Draft exclusion reasons |
| Risk of bias | Two reviewers | Plain-language notes |
| Extraction | Data team | Template suggestions |
| Synthesis | Statistician | Text smoothing only |
| Writing | Section authors | Draft paragraphs from tables |
| Final checks | Guarantor | Read-aloud and clarity passes |
When You Should Skip AI Assistance
Certain steps need direct human work from start to end. Risk of bias scoring, statistical pooling, and subgroup judgments sit in that list. If your review involves sensitive outcomes or safety signals, keep the prose draft human as well. Err on the side of manual work when a line could sway practice.
Choosing Inputs That Are Safe
Do not paste raw patient records, internal peer review notes, or any scraped publisher PDFs. Feed only citations you have rights to quote and public abstracts you can cite. If you keep a private index of PDFs, use offline tools to extract your own summary tables, then ask the model to rephrase those tables.
Reference Management Setup
Pick one manager and lock the workflow. Export RIS or XML from each database with the same fields. Deduplicate once, not five times. Tag records by stage: screened, included, excluded with reason. Insert citations only from the manager. Avoid quick copy-paste links from a draft chat window.
PRISMA Items That Pair Well With AI
Some PRISMA sections map neatly to short model tasks. Methods wording, plain-language descriptions of eligibility criteria, and notes on information sources can start as AI-assisted drafts. The flow diagram still comes from your logs. Keep counts exact, and keep dates and databases named in full.
Peer Review Preparation
Expect questions about search coverage, screening reliability, and data accuracy. Prepare a short appendix with your prompt archive, the locked codebook, and a snapshot of the model settings. Add track-changes files that show human edits to AI-drafted passages. That bundle answers most editor emails before they arrive.
Audit Trail And Data Sharing
Store registered protocols, search strategies, deduplication settings, and extraction templates in a versioned repository. Name files by date and step. If journal policy supports it, share an anonymized prompt list and the exact instructions used for summaries. That level of clarity builds trust and makes updates easier later.
Tools And Settings That Keep Output Tidy
Keep temperature near zero when you want consistent phrasing. Raise it only for early outline drafts. Ask for JSON or CSV when you need tables that load cleanly into your sheet. When you need prose, request short paragraphs with one idea per sentence. Avoid open-ended questions that invite speculation.
Limits Of Model Knowledge
A language model reflects its training window and the prompts you feed it. It cannot guarantee coverage of the latest trials, and it cannot judge risk of bias without structured inputs. Treat it like a writing and summarizing aide, not a search engine or a statistician. When in doubt, read the PDF and quote the exact numbers.
Bottom Line
A language model can speed parts of medical review writing when the team stays in charge. Keep searches in real databases, keep extraction human, and keep every claim tied to a verifiable row or PDF. Disclose tool use per journal policy and track your prompts. That mix gives you speed without losing trust.