Medical AI LLM Tracker – August 2025
an objective, continuously updated ranking of medical AI models based on real-world performance, integration readiness, and safety with each result tagged by a clear confidence rating.
Beyond the Algorithms: Reclaiming the Human in the Age of AI
Eamonn McCormick The current debate around artificial intelligence is backwards. Much of the discourse, particularly from the Doomers of...
From Promise to Practice: How WAIC 2025 Revealed AI Healthcare's Transition from Experimental to Essential
In this analysis, IDC’s Medical AI Research Manager, Dr. Tang examines six critical categories where AI has crossed the threshold from experimental to essential
Beyond the Hype: Why Human-Centered AI is the Future of Healthcare
Discover why specialized, human-centered AI will transform healthcare. Learn about the critical role of AI literacy and physician training in medical innovation.
Are AI Models Sending “Subliminal Messages” to Each Other? What’s Really Happening?
AI models can inherit hidden biases through innocent data. Our analysis explains the healthcare implications and provides implementation safeguards.
From Overload to Augmentation: How AI Can Support the Clinical Workforce
Eamonn McCormick The healthcare industry is approaching a tipping point. Across hospitals, clinics, and health systems worldwide,...
"Am I Second-Guessing Myself?": How Doctors Are Learning to Navigate Clinical AI Partnership
Lee Akay At nearly every IDC Healthcare AI seminar I conduct with physicians and hospital executives, one question inevitably comes up:...
AI-Enabled Clinical Teams: Driving Better Outcomes, Together
Eamonn McCormick A clinician uses an AI-powered advisor to access diagnostic insights and care plan guidance from multimodal patient...
The First Step in AI Strategy May Not Be Strategy
AI doesn’t fail due to bad models—it fails from missing systems. Learn how China’s scaffold-first strategy delivers faster, scalable AI execution.
AI Systems Under Pressure: The Next Wave of Deployment Challenges
AI doesn’t fail because the models aren’t good enough. It fails because the supporting architecture around the LLM, retrieval, memory, orchestration, and control layers is often incomplete or misapplied. Even state-of-the-art models can under deliver when they lack the intelligent scaffolding needed to translate raw capability into real-world performance.
