What This Guide Covers
Here you’ll learn a practical way to plan, search, screen, and write a medical literature review with help from AI. The steps fit resident projects, quality improvement work, and formal systematic reviews. You can adapt each step for scoping or rapid reviews as well.
The plan favors traceable work. You’ll see where AI speeds grunt tasks and where human judgment stays in charge. Every step leaves an audit trail you can show to peers, reviewers, and editors.
How To Do A Literature Review With AI In Medicine: Step-By-Step
1) Frame a precise question
Pick a structure that suits your goal. PICO works for treatment and prevention questions. PEO fits etiology. DIET or SPIDER can help for qualitative topics. Write one sentence that names the population, the exposure or intervention, the comparator if any, and the outcomes that matter to clinicians or patients. Keep a short list of synonyms for each concept. Those synonyms feed your search strings and your AI prompts later.
Add scope limits now. State settings you care about, such as inpatient versus outpatient, adult versus pediatric, and minimum follow-up. Note any must-have outcome scale or time window. Tight scoping saves hours downstream.
2) Set up a simple protocol
Outline decisions before you start. State your question, databases, date limits, languages, study designs to include, and primary outcomes. Add rules for screening, data extraction, and bias appraisal. If you plan a full systematic review, register the protocol in PROSPERO to make your plan public and to avoid duplicate work. Save your protocol alongside a version history so your team can track changes.
Decide team roles early. One person owns searches, two people screen, one resolves conflicts, and at least one senior reviewer signs off on bias calls. List response times so the work keeps moving during busy weeks.
Map the evidence sources
Medical topics rarely live in one database. Use a mix of bibliographic databases, trial registries, guideline portals, and grey literature. Record every source you search and the date. The table below shows a handy mix and how AI can help you move faster while staying transparent.
| Source | Best use in reviews | AI support tip |
|---|---|---|
| PubMed/MEDLINE | Peer-reviewed biomedical studies with MeSH indexing and strong filters | Use AI to draft synonym lists, then refine MeSH and field tags by hand |
| Embase | Drug and device coverage beyond MEDLINE; conference abstracts | Have AI suggest Emtree analogs of your MeSH terms, then verify |
| Cochrane Library | Systematic reviews and controlled trials curated for evidence-based care | Ask AI to summarize included trials to spot gaps quickly |
| ClinicalTrials.gov / WHO ICTRP | Ongoing and completed trials that may be unpublished | Let AI extract trial arms and outcomes into a table for screening |
| Guideline portals | Practice guidelines and evidence profiles from specialty groups | Use AI to pull out recommendations and certainty ratings |
| Preprints (medRxiv/bioRxiv) | Early signals on fast-moving topics; appraise with care | Prompt AI to flag limitations and missing peer review markers |
| Google Scholar | Forward and backward citation chasing | Ask AI to mine reference lists and build a screening backlog |
3) Build a reproducible search
Turn your question into concepts, then into search blocks. Combine subject headings with text words, link blocks with Boolean operators, and use proximity or field tags when the platform supports them. Search each database on its native platform. Export results with full citation data and abstracts. Keep a log of search dates, limits, and exact strings in a living document stored with your project.
Run a pilot search and skim the first 200 hits. Check whether your sample contains the landmark trials or syntheses you expected. If not, adjust one concept at a time. Add spelling variants, acronyms, and brand names where relevant.
Use MeSH and free text together
Subject headings boost recall when terms vary. Free text catches new terms that may not be indexed yet. Pair both to reduce missed studies. Build quick pilots, check what you retrieve, then tune your strings. When recall looks thin, expand one block at a time and watch the noise.
Keep a clean audit trail
Save every search string, platform, and date. Note any filters. Record how you deduplicated results. The log supports a PRISMA flow diagram and helps others repeat your work. It also helps you revisit the search months later without guesswork.
4) Import and deduplicate
Pull all records into a reference manager or a screening tool. Run a first pass to remove exact duplicates. Then scan for near-duplicates that differ by conference versus journal versions. Keep a copy of the raw export and a copy of the clean set. That simple habit prevents hours of rework if files go missing.
Give files predictable names. Include the source, date, and stage. Put your project ID at the start so sorting keeps the set together across folders.
5) Screen titles and abstracts with AI help
Start with a short training batch screened by two people. Teach the tool what counts as include versus exclude. Then let AI rank the rest by predicted relevance. Keep humans in the loop for final include decisions. Blind dual screening raises reliability. Resolve conflicts with a third reviewer or a short huddle with reasons logged in plain language.
Revisit your rules after the first 300 records. Tighten phrasing, add one or two clarifiers, and share an updated copy inside the tool so everyone sees the same instructions.
Write tight include/exclude rules
Make rules short and test them on a sample. List the study designs you accept, the required population features, and any must-have outcomes. Add common false positives you expect, such as animal studies or editorials. Share the list inside your screening tool so every reviewer sees the same guide at the point of use.
6) Extract data and appraise bias
Create a pilot form before you touch the full set. Capture study identifiers, design, setting, sample size, intervention details, follow-up, outcomes, effect estimates, and notes on deviations. Pair extraction with a bias tool that fits the design. Use two independent extractors on a sample, compare entries, and refine the form. Then move to the full run with one extractor and one verifier if your timeline is tight.
When outcomes vary across papers, pre-define a priority list. Name the scale you prefer, the window for follow-up, and rules for units. That one page saves long email threads later.
Where AI fits during extraction
AI can draft tables from PDFs, pull numeric results, and list outcome definitions. Treat those drafts as a starting point. Check every number against the paper. Flag any missing denominators or switched units. Keep a change log when you correct AI output.
7) Synthesize and write
Group studies by design, population, and outcome. Decide whether a meta-analysis makes sense given heterogeneity. If not, write a structured narrative synthesis. Use AI to draft section outlines, not finished prose. Feed it your own notes, tables, and bias calls so the draft reflects your choices. Then rewrite in your voice and cite the evidence directly.
Use figures to clarify the story. Forest plots show effects. Harvest plots show evidence clusters. Simple bubble charts can map dose or device class across outcomes.
Pick the right AI for each task
Each tool shines at a different point in the workflow. Match the task to the tool and keep humans responsible for judgments. The table below maps common tasks to well-known options and a safe way to use them.
| Workflow stage | Helpful AI/tool | Good practice |
|---|---|---|
| Term finding | LLM prompts, MeSH on Demand | Use AI to suggest synonyms, then verify against MeSH or Emtree |
| Screening | Rayyan or ASReview | Start with dual human labels, review AI-ranked lists, keep an audit log |
| Risk of bias | RobotReviewer | Use as a second reader to flag domains; keep human judgment primary |
| Data extraction | LLM table drafting, Elicit | Cross-check all pulled numbers and fix unit mismatches by hand |
| Summarizing | LLM outline support | Feed notes and quotes, not conclusions; rewrite in plain clinical language |
| Reference cleanup | Reference managers with DOI lookups | Validate DOIs and journal names; fix casing for drug and gene names |
Doing a medical literature review with AI: what to avoid
Don’t let AI decide inclusion alone
AI can rank and highlight, yet it can also miss edge cases or over-weight common phrasing. Always keep a human on final include calls. Sample excluded records to make sure no major study slipped through.
Don’t paste patient data
Never paste protected health information or unpublished data into a web tool. Work with public abstracts or de-identified content only. If your institution offers a private model, follow local rules and share files inside secure drives.
Don’t treat AI text as a source
When you write, cite the study, not the chatbot. If you used AI to assist with wording, disclose that use in your methods or acknowledgments per journal rules. Keep authorship to humans who take responsibility for the work.
Document everything for transparency
Record decisions as you go
Keep a short changelog that lists protocol edits, search updates, and screening rule tweaks. Link each change to a reason and a date. Small notes now prevent confusion later.
Track counts for a PRISMA flow
Log the number of records found in each source, how many were deduplicated, how many passed title and abstract screening, how many passed full text, and why papers were excluded. Drop those counts into a PRISMA flow figure and save the underlying spreadsheet with your project files.
Write clear methods readers can repeat
Searching
Report each database, the platform, the date you searched, and the full strings. Attach them as an appendix or a repository link. Include how you handled grey literature and trial registries.
Screening
State who screened, whether masking was used, and how conflicts were resolved. If you used AI ranking, say which tool, how you trained it, the share of records it helped you prioritize, and how you verified its suggestions.
Extraction and bias
Share your extraction form, list the bias tool you used, and describe the judgment rules with examples. If AI drafted tables or suggested bias calls, make that clear and describe how you checked them.
Smart prompting for literature work
Prompts that produce better search blocks
Feed the model your PICO, a few seed papers, and any subject headings you already trust. Ask for candidate synonyms grouped by concept, with notes on likely false positives. Then transfer good ideas into platform-specific strings and test them live.
Prompts that speed screening notes
Paste the abstract and your include rules. Ask for a one-line verdict and a list of phrases that triggered the decision. Use that text as a note inside your screening tool. It keeps team decisions aligned and consistent.
Prompts that draft tables you can verify
Share a PDF or full text when the license allows. Ask the model to extract sample size, arms, follow-up, primary outcomes, effect sizes, and any adverse events into a tidy table. Then check the numbers against the paper and add a footnote when data were imputed or calculated.
Ethics and authorship
Disclose any AI assistance in the methods or acknowledgments based on the journal’s policy. Do not list a tool as an author. Keep raw data, prompts, and outputs archived with your project so editors can ask questions if needed. When unsure, ask your librarian or research office for the journal rules before submission.
Helpful links you can trust
Use the PRISMA 2020 checklist to shape methods and reporting. Search subject headings with the MeSH Browser. If you register a full review, do it at PROSPERO so others can see your plan.
Next steps
Pick a live question from your service or clinic. Draft a one-page protocol. Build a first PubMed string by hand. Then let an AI assistant suggest synonyms and a few missing angles. Test, refine, and log everything. Once your search looks solid, run dual screening with an AI-ranked queue and keep notes inside the tool. Use AI to speed extraction and summaries, and keep every number checked. Finish with clear methods, a PRISMA flow, and a tidy archive of files. That blend of speed and rigor helps your review land with readers and editors alike.