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MedAPI Evidence Synthesis — Clinical Evidence Lookup & LLM Analysis
x402
MedAPI Evidence Synthesis — Clinical Evidence Lookup & LLM Analysis is a paid API for AI agents from mamujamedapi.vercel.app, paid per call via x402, $0.35/call, status unknown (last checked 2026-09-14).
Accepts a medical question and returns synthesized clinical evidence from PubMed/clinical trials, including study count, evidence level, and LLM-powered analysis.
Price / call$0.35
Success—
StatusUnknown
How this service works
Drug data, adverse events, clinical trials, PubMed. AI-powered medical analysis. 17 endpoints. x402 on Base.
Output
Returns a JSON object with a success flag, a data object containing the total number of relevant studies, an array of study objects, and the assessed evidence level (e.g. high/moderate/low), plus an LLM-generated analysis object that synthesizes the evidence into a human-readable summary.
Example use cases
Drug efficacy evidence check
“What does the clinical evidence say about metformin combined with lifestyle intervention for type 2 diabetes prevention? Give me the study count, evidence level, and a synthesis of what the research shows.”
Treatment decision support for clinician
“I'm looking at using low-dose naltrexone for fibromyalgia — can you find what clinical evidence exists, how strong it is, and summarize what the trials actually found?”
Medical research literature overview
“Summarize the clinical evidence on PD-1 inhibitors like pembrolizumab for non-small cell lung cancer — how many studies are out there and what level of evidence do we have?”
Technical details
Endpoint
POSThttps://mamujamedapi.vercel.app/api/evidenceWhen to prefer
Choose this endpoint when you need a synthesized, evidence-graded answer to a clinical or pharmacological question, combining raw study retrieval with LLM analysis in a single call. Prefer it over raw PubMed search when you want an evidence level assessment and a narrative synthesis rather than just citations. Ideal for clinical decision support, medical research summaries, or answering evidence-based medicine questions without building your own retrieval-plus-synthesis pipeline.
Side effects
- Vague or malformed question yields low-quality or empty study arrays
- Evidence level may be 'insufficient' if few studies match the query
- LLM synthesis may hallucinate or over-generalize when source studies are sparse
- Payment failure (x402 on Base) blocks the response entirely
- Rate limits or upstream API outages (PubMed, ClinicalTrials.gov) may cause timeouts
- Very niche or novel queries may return zero studies with no useful synthesis
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| questionrequired | string | question |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| data | object | |
| successrequired | boolean | |
| analysis | object | LLM evidence synthesis |
Tags
x402
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