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This Perspective paper argues that large language models are not yet safe for autonomous clinical decision support, particularly in triage of undifferentiated patients, due to lack of robust evaluation under incomplete information and asymmetric costs of missed diagnoses.
Michael Antonov, co-founder of Oculus, discusses his pivot to drug discovery through Deep Origin, emphasizing the integration of AI with physics-based simulations and experimental validation for safer therapies, and the role of VR in medical training.
This paper presents an evidence-grounded AI framework for diagnosing and treating musculoskeletal conditions, leveraging clinical data and orthopedic knowledge.
The author reflects that the hospital is a good place to think about the value of AI, but the practical application of AI in healthcare has not yet brought universal breakthroughs; for example, AI tools like Fable 5 and Doubao have limited effectiveness.
The author uses Claude Code with Opus 4.8 to analyze an MRI scan, finding discrepancies with the initial diagnosis, and discusses the potential and limitations of AI in medical imaging interpretation.
AI in hospitals aims to improve operational workflows and support clinical decisions rather than replace doctors, emphasizing integration into existing systems for tasks like triage, lab review, and patient monitoring.
AI is transforming U.S. pharmaceutical R&D by accelerating drug discovery, preclinical testing, clinical trials, and regulatory review, with early evidence suggesting it could cut development times by roughly half and reduce costs.
A new study proposes viewing tumors as organized ecosystems rather than random mutations, using AI to analyze spatial organization, immune localization, and signaling environments in oncology.