Use AI to shape questions, speed database searches, triage studies, and draft summaries—then verify every claim against trusted medical sources.
Medical teams want faster reviews without losing rigor. AI can help with phrasing questions, drafting search strings, sorting results, and building clean summaries. Human judgment still leads every step. Journal rules also apply: disclose tool use, keep data private, and accept full responsibility for the text and citations. This guide lays out a repeatable path you can run for scoping, narrative, rapid, or systematic work.
Project Scope And Review Type
Pick a review format that fits your purpose and timebox. A scoping review maps what exists. A narrative review builds a reasoned synthesis by topic. A rapid review uses shortcuts yet keeps transparent records. A full systematic review follows a protocol, runs structured searches across multiple sources, and reports selection with a flow diagram. The choice shapes searching, screening, extraction, and how you present results.
Review Types And When To Use Them
| Review Type | Best For | Typical Output |
|---|---|---|
| Scoping | Field mapping, concepts, gaps | Topic map, tables, minimal critique |
| Narrative | Clinical overview or teaching aim | Themes, landmark studies, plain-language takeaways |
| Rapid | Time-bound advice for policy or service | Abbreviated methods with staged limits noted |
| Systematic | Decision-grade synthesis | Protocol, multi-database search, PRISMA flow |
| Umbrella | Synthesizing multiple reviews | Comparison across reviews, high-level patterns |
| Living | Topics with frequent new trials | Scheduled updates and versioned changes |
For systematic reports, align language and structure with the PRISMA 2020 checklist. It lists items for title, abstract, methods, results, and flow.
Steps For Doing A Literature Review With AI In Medicine
1) Define The Clinical Question With PICO
State the population, intervention, comparator, and outcomes. Add setting or study design if needed. Ask an AI assistant to propose synonyms for each element, then cross-check terms against MeSH and trial registries. Keep a running sheet of chosen terms and reasons for inclusion or exclusion.
Use the PubMed search builder to test phrases, field tags, and MeSH headings. Review the “Details” area to see how the engine parsed your string. Adjust phrasing until the first pages bring back the right signal.
2) Pick Data Sources
Plan a mix that fits your topic: PubMed or MEDLINE, Embase, Cochrane Central, CINAHL, PsycINFO when mind-health matters, Web of Science for forward citation chains, and ClinicalTrials.gov or WHO ICTRP for trials. Grey sources can include guidelines, theses, preprints, and regulatory summaries. List where you will search and why each source adds reach.
3) Draft Search Strings With And Without AI
Ask AI to generate an initial boolean string for each database, then refine manually. Replace vague phrases, add proximity operators where supported, and clip false-positive terms. Save the exact strings you run, with date and database field notes. That log feeds your methods section later.
4) Import, Deduplicate, And Label
Export results in RIS or XML. Pull them into a manager such as Zotero, EndNote, or a screening app. Run deduping, then tag records by source. Keep counts per source so the flow chart is trivial to fill. Store the raw exports as read-only files for audit.
5) Title And Abstract Screening
Write clear inclusion and exclusion rules before you view the pile. Two human screeners make calls; an AI triage can score relevance but never replaces the second screener. Disagreements go to a third reviewer. Record reasons for every exclusion at the full-text stage.
6) Full-Text Review And Extraction
Build a pilot extraction sheet with fields for study ID, design, setting, sample, exposures or interventions, comparators, outcomes, follow-up, effect size units, and notes. Ask AI to auto-fill a draft table from PDFs, then verify every cell against the text. Flag doubtful numbers for a second human pass.
7) Appraise Study Quality And Bias
Pick tools that match the designs you included: ROB 2 for randomized trials, ROBINS-I for non-randomized studies, QUADAS-2 for diagnostics, AMSTAR 2 for review-of-reviews. Capture judgments with quoted evidence. AI can suggest which domain a quote fits, yet the call is yours.
8) Synthesize Findings
Choose a narrative path when designs, outcomes, or measures vary. Meta-analysis needs consistent metrics and low heterogeneity or a plan for random-effects models. AI can cluster themes, draft forest plot notes, and surface conflicts across studies. Verify estimates, and cite the exact tables or figures where numbers came from.
9) Report Methods And Results With Structure
Mirror the item order from PRISMA 2020. Include databases, dates, full strings in an appendix, selection counts with a flow chart, risk-of-bias judgments, and any sensitivity checks. You can download a flow template from the PRISMA site and tailor labels to your sources.
Many journals follow the ICMJE Recommendations. They state that AI tools are not authors and that any tool use should be named in the text. Keep a short “Use of AI tools” note in your methods and acknowledgements.
10) Keep Reproducible Files
Store exports, search logs, screening decisions, extraction sheets, and analysis scripts in a versioned folder. Name files with date stamps. If your topic suits registration, post a protocol so readers can see any changes made during the work.
Doing A Medical Literature Review With AI: Common Pitfalls
Hallucinated Citations
Language models may produce fake DOIs or made-up trials. Never ask AI to create a reference list from scratch. Always pull citations from databases and managers you control, then ask AI to format them to a style.
Over-trusting Abstracts
Abstracts can sell a message that the full text does not back. Verify effect sizes, subgroup rules, and outcome timing inside the methods and results sections, not just the abstract.
Scope Creep
When new themes appear mid-stream, record whether they fit your aim. If you change the aim, state the change in the write-up. Keep a changelog so the path stays clear to readers.
Privacy Risks
Do not paste protected health information into any external tool. Strip direct identifiers from notes. Use local or enterprise models when handling internal documents.
One-sided Evidence
AI suggestions can mirror popular views. Balance that with backward and forward citation chasing and a scan of trial registries for unpublished work.
Prompts That Work For Medical Reviews
Search Planning
You are helping with a cardiology review on SGLT2 inhibitors for heart failure with preserved EF.
List synonyms and MeSH terms for:
Population, Intervention, Comparator, Outcomes.
Return a two-column table: term and rationale.
Exclude brand names.Screening Support
Read this abstract and rate fit to the inclusion rules (0-100).
Rules:
• adults with HFpEF
• RCTs or well-designed cohorts
• outcomes: hospitalization, mortality, NYHA class
Give a one-sentence reason for the score.Data Extraction
From this PDF, draft a row for the extraction sheet with:
study ID, design, setting, sample size, follow-up,
intervention dose, comparator, primary outcome measure,
effect size with unit, and notes that cite the page.Risk Of Bias Notes
Map each quoted statement to ROB 2 domains.
Do not assign a rating. Just suggest the domain and quote location.Write-Up Assistance
Using these verified numbers and quotes, draft a methods paragraph
that lists databases, years, and main search fields.
Keep it under 120 words and match PRISMA item names.Structured Reporting And Audit Trail
Readers expect a clean trail from question to answer. Keep a one-page diagram of your process: search dates, sources, counts at each gate, and reasons for exclusion. A PRISMA flow diagram works for most designs. Pair it with a short table that lists every database and the last run date.
For transparency, post the search strings and extraction sheet as supplements. If you used AI at any stage, add a short note that names the tool, version, settings, and human checks. That small note prevents confusion during peer review.
AI Tasks And Human Checks
| AI Can Help With | What You Still Do | Proof You Keep |
|---|---|---|
| PICO synonyms | Confirm MeSH, remove noise | Term list with sources |
| Draft boolean strings | Tune fields and operators | Exact strings per database |
| Deduping and tagging | Spot-check pairs and clusters | Dedupe log and counts |
| Triage scoring | Second human screen | Decision file with reasons |
| Table drafts | Verify every data point | Extraction sheet with page cites |
| Theme clustering | Resolve conflicts across studies | Summary notes with study IDs |
| Language polishing | Retain meaning and nuance | Versioned text and change notes |
Ethics, Safety, And Journal Rules
Disclose any tool that shaped text, tables, or figures. State what the tool did and how you verified the output. Per ICMJE policy, AI systems are not authors, and people remain accountable for accuracy and integrity. See the ICMJE Recommendations for wording and scope.
Protect patient privacy and confidential data. Avoid pasting sensitive material into external services. Use institution-approved platforms when you need to process internal PDFs. Log every dataset you used and who approved access.
Template Timeline For A Lean Team
Week 1: Aim And Setup
Confirm the question, pick review type, and draft inclusion rules. Build a project folder with subfolders for searches, screening, extraction, analysis, and drafts. Assign roles and create a shared changelog.
Week 2: Search And Logs
Generate term sets with AI, refine with MeSH, and run pilot strings. Finalize the strings per database. Export and store raw files and search notes.
Week 3: Screening
Run deduping, tag by source, and start title and abstract screening with two people. Use AI triage to rank items, then record human decisions and reasons.
Week 4: Full Text And Extraction
Fetch PDFs, test the extraction sheet on five papers, adjust fields, then process the rest. Ask AI to draft rows only after you mark the target fields, then verify line by line.
Week 5: Appraisal And Synthesis
Apply bias tools that match study designs. Build summary tables, pool numbers if methods allow, and write the main message in plain language.
Week 6: Write-Up And Checks
Assemble methods in PRISMA order, fill the flow chart, and paste the final strings into an appendix. Add the AI use note, run citation checks, and circulate for internal review.
Final Notes For Clinicians And Researchers
AI speeds grunt work; it does not grant authority. Keep people in the loop at every gate that affects inclusion, extraction, and interpretation. Link your steps to PRISMA items and cite databases you actually queried. Point readers to the search strings, the screening counts, and the tables that back the claims. With that discipline, AI becomes a safe accelerator for careful medical reviews.
Database-Specific Tips That Save Time
PubMed thrives on MeSH when articles have been indexed. Pair MeSH with title/abstract terms to catch new records that are not yet indexed. Use field tags such as [tiab] for phrases you want in the text. Test narrower versus broader MeSH trees to see which mix retrieves the right set.
Embase adds Emtree terms and proximity operators. If your topic relies on drug names, list both generic and brand names, then trim noise terms that pull device ads or conference pages. Cochrane Central is lean but rich for trials; keep filters off during the first pass so you do not hide relevant items.
Google Scholar can help with citation chasing. Paste a sentinel paper and harvest the “Cited by” tree to find studies that did not carry the same keywords. When you add preprints from medRxiv or bioRxiv, label them so readers can tell which findings had not gone through peer review at the time of your search.
Set alerts for new records on your terms list. Schedule reruns before submission to catch late indexing and registry updates and preprints.