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2026/3/20
While Clinical AI Booms, Reliability Becomes the Bottleneck
Clinical AI is advancing rapidly, but LLM reliability has emerged as a key bottleneck. Recent studies show performance varies with user interaction and disease context, raising safety concerns. While progress is being made (e.g., AMIE, OpenScholar), adoption in routine care depends on consistent, trustworthy performance. Achieving this requires system-level solutions, not just better models. The Learning Health System (LHS) provides a path by enabling continuous evaluation and real-world evidence generation. Reliability will determine whether GenAI is trusted and adopted in clinical care.
2025/12/15
At the Chen Institute’s AI Accelerated Science Symposium on October 28, 2025, ELHS Institute founder Dr. AJ Chen proposed a new vision for Open Clinical AI Science (OCAIS) to accelerate the clinical impact of generative AI. The framework delivers free GenAI-based disease prediction services to clinical teams worldwide, including low-resource settings, enabling large-scale participation in clinical evidence generation. By converging GenAI with task-specific Learning Health System units, this approach aims to shorten evidence-generation timelines from decades to years and help prevent GenAI from repeating past failures in health care innovation.
2025/10/25
Dr. AJ Chen delivered a keynote at the Tsinghua Health AI Summit on converging generative AI (GenAI) and Learning Health Systems (LHS) to improve clinical diagnosis and reduce global health disparities. He presented the ELHS Institute’s ML-enabled LHS framework, supported by Nature- and JAMIA-published studies, showing how GenAI embedded in LHS units can enable scalable, responsible evidence generation and democratize high-standard predictive care worldwide.
