Beyond keyword matching — three AI personas with diverse clinical backgrounds evaluate each paper from multiple angles. Articles with low consensus scores are automatically excluded from delivery.
How It Works
Hundreds of papers across 29 specialties collected daily, auto-classified by AI for importance and specialty
Each paper receives an importance score (1-3). Pediatric misclassifications are auto-detected at code level
Must Read
Recommended
Reference
Three virtual physicians with different ages, experience, and practice settings rate each paper on a 5-point scale
Dr. Sato
35 / University Hospital
Latest evidence focus
“Robust RCT design. High potential for clinical application.”
Dr. Tanaka
52 / Regional Hospital
Real-world applicability
“Sample size somewhat small, but practical insights.”
Dr. Suzuki
44 / Private Clinic
Patient communication
“Useful reference for explaining to patients.”
Consensus Result
Average of 3 persona scores → Overall rating
4.3
/ 5.0
Papers rated low (≤2) by 2 or more personas are automatically excluded from email delivery
Only high-quality papers that passed AI consensus reach your inbox every morning
Why Consensus
A single AI tends to be biased. Three personas with different ages, experience, and practice environments evaluate from their own perspectives. A young academic, a veteran at a regional hospital, a private practitioner — diverse viewpoints cover blind spots.
Keyword matching alone causes pediatric papers to slip into adult cardiology. Consensus review determines 'Is this truly useful for this specialty's physicians?' from multiple angles.
Persona evaluations automatically feed back into classification prompts. As daily evaluations accumulate, paper classification accuracy and importance scoring continuously improve.
3
AI Personas / Subspecialty
384
Total Personas
29
Specialties
24h
Auto-eval Cycle
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