A new theory proposes that critical illness arises from loss of physiological coordination, not just organ failure. The Entropic Critical Illness Theory views living organisms as open systems with a continuous need to regulate entropy.
A new set of consensus standards for classification, annotation, and quality control is being implemented for artificial intelligence applications in dry eye imaging. These standards aim to enhance consistency and multicenter collaboration in AI-assisted diagnosis.
Researchers develop dynamics-driven models to identify disease transitions before symptoms appear, transforming real-time care and personalized treatment. AI systems analyze health data to detect
A specialized AI model, AMIR-GPT, has been developed to improve radiology guideline alignment, outperforming general purpose models in 33.3% of test responses. However, the model's performance varied across performance bands, and qualitative review revealed limitations, such as omissions and deviations from standard recommendations.
The journal has been recognized for its significant international citations, with over 3 million documented in the past year. Its editorial leadership and publishing model ensure rigorous peer review and a multidisciplinary perspective, supporting high-quality research on AI and clinical applications.
The expert consensus provides a structured framework for assessing large language models (LLMs) before deployment in clinical workflows. The framework includes retrospective evaluation, six key capability domains, and essential safeguards for patient data protection and bias mitigation.