AI is increasingly used for medical diagnosis, patient monitoring, and hospital operations, but raises concerns about privacy and the need for on-premise deployment to protect sensitive data.
Two U.S. cases expose the dark side of clinical automation. In New York, nurses say they were pushed aside by algorithms. In Minnesota, a former Mayo Clinic leader claims the software was not trustworthy. As AI advances into hospital wards, independe...
The iLENS framework combines large language models with a mixture-of-experts to predict Alzheimer’s transparently. It’s a step that makes on-premise deployment unavoidable, where privacy and interpretability are regulatory prerequisites.
Built on Qwen3.5, the new 27-billion-parameter model claims to outperform Google's MedGemma in medical tasks. For hospitals, it opens the door to local processing of sensitive data. But hardware trade-offs and clinical validation remain central.
A study across 7,929 questions finds that Retrieval-Augmented Generation dramatically boosts accuracy of LLMs for public health, allowing smaller open-weight models to match or outperform far larger ones. The research also introduces a LLM-based judg...
