Get oriented in a medical topic before you read 40 papers
with a structured literature survey
If you ask AI to "summarize the literature on X," you usually get generic paragraphs, weak citation discipline, and vague lines like "more research is needed."
Here's the same question asked two ways—
Try for Free→ Fill in variables and run directly with AI. Free to try.
Fill Variables
e.g., Machine learning in diabetes diagnosis, GLP-1 and cardiovascular outcomes
e.g., 简体中文, Spanish, 日本語, English
Same topic, two ways to ask
The real difference is not whether AI can summarize. It is whether the output is verifiable and actionable.
What you send to AI
Summarize the literature on GLP-1 receptor agonists and cardiovascular protection.
Typical result
- Lists broad themes but does not tell you which papers matter most - Gives no PMID/DOI, so you cannot verify quickly - Says "more research is needed" without naming a concrete gap You finish reading it and still do not know what to read next.
Variables you fill in
Topic: GLP-1 receptor agonists and cardiovascular protection Preferred language: English
Structured output you get back
1. Research hotspots - ASCVD outcome trials - HFpEF and weight-loss mechanisms 2. Representative papers - Title, Journal, Year, PMID/DOI - Why each paper is worth reading first 3. Research opportunities - Each one anchored to a real contradiction or limitation - Includes novelty and feasibility 4. Current limitations - Limited population generalizability - Short follow-up or inconsistent methods
How this tool works
You only fill the topic and output language. Evidence-quality rules and output structure are already built in.
You fill in
- Research topic: Enter the disease, intervention, methodologic problem, or cross-disciplinary topic you want to map quickly.
- Preferred language: The full survey is written in your preferred language, for example English, so you can use it directly for discussion, reporting, or the next round of filtering.
Already built in
- Prioritize PubMed, Cochrane, high-impact journals, and stronger evidence types such as RCTs, systematic reviews, and meta-analyses
- Require Title, Journal, Year, and PMID/DOI for every paper mentioned, with uncertain items clearly marked for verification
- If the topic is too broad, narrow to the most clinically relevant or fastest-growing subareas instead of sweeping the whole field badly
- Force research opportunities to come from real contradictions or concrete limitations in the literature rather than storytelling
- Keep the total response within 500-800 words so it works as fast orientation, not a fake substitute for a full review
What one survey run gives you
These outputs are taken directly from the quick-literature-survey tool structure. They are not after-the-fact marketing claims.
Research hotspots
See the most active subareas from the last 2-3 years so you know where the field is actually moving.
Representative papers
Get a short list of papers worth reading first, each with title, journal, year, and PMID/DOI.
Research opportunities
Move beyond "more research is needed" and identify specific next questions grounded in real contradictions or limitations.
Current limitations
Spot where the evidence base is still weak, so you can decide whether to keep reading or move into study design.
How to use it
Enter topic and language
Describe the topic you want to map and choose your output language. The more specific the topic, the more usable the output.
Click AI Run
AI Run opens a chat. Describe the topic you want to survey — the assistant will map the research landscape and identify key papers and directions.
Use it to decide the next move
Verify the key papers first, then decide whether to read deeper, move into PICO question building, or rethink the topic direction.
Rapidly map research hotspots, representative papers, and real research opportunities in any medical topic. Structured output under 800 words, built for fast orientation before deeper reading.
→ Run directly with AI. Free to try.
FAQ
Are the citations always accurate?
This tool requires AI to provide Title, Journal, Year, and PMID/DOI when possible, and to mark uncertain details clearly. But AI can still be wrong, so verify important citations in PubMed or Google Scholar before using them.
What kinds of topics work best?
Specific clinical or methodological topics work best. “Machine learning in diabetes diagnosis” will give you a much better survey than just “diabetes.” If the topic is broad, the tool narrows the scope to the most active subareas.
Can I use this as my thesis literature review?
Not as a full replacement. It is designed for fast orientation and early-stage mapping. For thesis-level work, treat it as a starting point and then do your own systematic searching and full-text reading.
How reliable are the AI-generated research opportunities?
More reliable than a generic AI summary, because each opportunity must be tied to a documented contradiction or limitation. But it is still a hypothesis until you verify the gap in PubMed.
How do I turn the survey into the next research step?
Pick the 1-2 most interesting opportunities, verify them in PubMed, and then pass them into the platform’s PICO Question Builder to turn them into formal research questions.