Beyond keyword matching — three AI specialists 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 AI specialists with different ages, experience, and practice settings rate each paper on a 5-point scale
thomas.w
38 / University Medical Center
Latest evidence focus
“Robust RCT design. High potential for clinical application.”
sebastian.w
42 / Municipal Hospital
Real-world applicability
“Sample size somewhat small, but practical insights.”
lars.w
44 / National Center
Patient communication
“Useful reference for explaining to patients.”
Consensus Result
Average of 3 expert scores → Overall rating
4.3
/ 5.0
Papers rated low (≤2) by 2 or more experts 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 specialists 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.
Expert evaluations automatically feed back into classification prompts. As daily evaluations accumulate, paper classification accuracy and importance scoring continuously improve.
3
AI Experts / Subspecialty
384
Total AI Experts
29
Specialties
24h
Auto-eval Cycle
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