How MEDLEY Works
Diversity drives accuracy. By combining perspectives from 31 AI models trained on different datasets across various cultures and regions, MEDLEY provides comprehensive diagnostic insights with evidence-based reasoning.
Input Case
Submit comprehensive patient information including symptoms, history, labs, and imaging findings.
Diverse AI Analysis
31 diverse AI models from USA, Europe, China, and more analyze independently, reducing single-model bias.
Evidence Synthesis
Orchestrator builds consensus while preserving all differential diagnoses with supporting evidence.
Physician Decision
Human-in-the-loop: Physician reviews all evidence and alternatives to make the final clinical decision.
Why Diversity Matters in Medical AI
Reduces Bias
Models trained on different populations catch diagnoses others might miss. Geographic and cultural diversity ensures comprehensive coverage.
Evidence-Based
Every diagnosis comes with supporting evidence and reasoning. Models must justify their conclusions with clinical data.
Preserves Alternatives
All differential diagnoses are retained, even minority opinions. Rare conditions aren't overlooked by consensus averaging.
Evidence-Based Consensus Building with Human-in-the-Loop
30+ AI Models from Around the World
Geographic and cultural diversity reduces diagnostic blind spots
🇺🇸 OpenAI (5 models)
🇺🇸 Anthropic (3 models)
🇺🇸 Google (6 models)
🇺🇸 Meta (2 models)
🇨🇳 Chinese AI (6 models)
🇫🇷 European AI (3 models)
🌍 Other Origins (6 models)
info Why Model Diversity Matters
Each model carries inherent biases from its training data and origin. Models trained in different regions may better recognize conditions prevalent in their geographic areas. By combining perspectives from USA, Europe, China, and other regions, MEDLEY reduces the risk of missing diagnoses due to geographic or cultural blind spots.
Explore Medical Cases
Select from our library of pre-analyzed complex medical cases