AI-assisted clinical evidence scoping tool for physiotherapy.
Turn a patient profile into a curated, quality-rated, meta-analyzed evidence brief in under 3 minutes — powered by a 6-agent pipeline that follows PRISMA methodology across 7+ free scientific databases.
⚠️ PhysioFlow is a decision-support tool, not a substitute for a formal systematic review. All outputs are labeled and time-stamped.
Evidence-based physiotherapy is stuck in a bottleneck: a clinician has ~5 minutes between patients, but a proper literature search takes hours. Existing tools (PubMed, Google Scholar) return raw lists — no screening, no quality rating, no synthesis. PhysioFlow closes that gap by running the same PRISMA workflow a research assistant would, in the time a clinician actually has.
Input a patient profile (age, condition, comorbidities, goals). The pipeline:
- Translates the profile into a structured PICOS query
- Searches 7+ databases in parallel (PubMed, OpenAlex, Europe PMC, SciELO, ClinicalTrials.gov, medRxiv, Semantic Scholar)
- Screens results through a 3-level PRISMA pipeline with dual reviewer logic
- Rates quality using Cochrane RoB 2, PEDro, and Oxford CEBM levels
- Extracts quantitative outcomes from included studies
- Meta-analyzes using random-effects DerSimonian-Laird with Hedges' g — outputs SVG forest plots
- Rates confidence with GRADE and delivers a plain-language brief with citations
Every step is logged and traceable back to the source paper.
Six independent agents coordinated by a pipeline orchestrator:
| Agent | Responsibility |
|---|---|
picos_translator |
Patient profile → structured PICOS |
search |
Parallel query across 7+ databases |
screener |
3-level PRISMA screening (title/abstract/full-text) |
quality |
RoB 2 / PEDro / Oxford CEBM rating |
extractor |
Quantitative outcome extraction |
synthesizer |
Random-effects meta-analysis + GRADE + brief |
Each agent returns strict JSON and is independently testable.
- Framework: Next.js 16 (App Router, Turbopack)
- Language: TypeScript
- UI: Tailwind CSS + shadcn/ui
- LLM: DeepSeek API (OpenAI-compatible) for agent reasoning
- Statistics: Custom random-effects meta-analysis implementation (DerSimonian-Laird, Hedges' g)
- Visualization: SVG forest plots (no charting library dependency)
- Deployment: Vercel
# 1. Clone
git clone https://github.com/victorgomezadapty/physioflow.git
cd physioflow
# 2. Install
npm install
# 3. Configure
cp .env.example .env.local
# Fill in DEEPSEEK_API_KEY and optional PUBMED_API_KEY / SEMANTIC_SCHOLAR_API_KEY
# 4. Run
npm run dev
# Open http://localhost:3000The app works without a PubMed API key — the key only raises rate limits.
MVP functional — end-to-end pipeline running with real searches and real papers. Under active development toward:
- Public demo deployment
- Editable inclusion/exclusion criteria per query
- Session persistence and shareable briefs
- Additional condition-specific PICOS templates
- User-facing quality rating explanations
See EXECUTION_PLAN.md for the detailed build plan.
- Transparency over convenience — every claim in the output is linked to its source
- Honest labeling — this is a scoping tool, not a systematic review, and it says so
- Bilingual by default — Spanish patient-facing labels, English technical terms
- Zero patient data storage — queries are stateless; nothing is retained server-side
Built by Víctor Andrés Gómez López — physiotherapist, doctoral researcher in physical activity and sport, founder of ADAPTY. Currently Head Physiotherapist at Optimo Gym (Riyadh) and building clinical AI tools full time.
Contact: LinkedIn
Source available for review and evaluation. Contact the author for commercial or clinical deployment inquiries.