AI helps nurses stay one step ahead in chronic disease care, new review finds
Peer-Reviewed Publication
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 3-Nov-2026 13:17 ET (3-Nov-2026 18:17 GMT/UTC)
Home visits are a core part of community nursing. For nursing students, they are not simply a checklist of health assessments. A home visit asks students to observe the family environment, recognize safety risks, communicate with patients and relatives, and turn classroom knowledge into practical care decisions. Yet such situations are hard to recreate in a classroom. A real home may be cluttered. An older adult may hesitate, question the visitor, become impatient, or respond in unexpected ways. Traditional role-play can help students rehearse the process, but scripted scenarios may not fully capture the uncertainty of entering someone’s home as a nurse. A study published in the Chinese Journal of Medical Education Research explored whether a virtual–physical integrated scenario simulation could bring students closer to that reality. Researchers from Peking Union Medical College School of Nursing, Beijing Xicheng District Desheng Community Health Center, and Beijing Fengtai District Fangzhuang Community Health Center tested the approach in undergraduate nursing education. They found that the new simulation model increased students’ learning immersion compared with conventional scenario simulation. However, the study did not show a clear short-term advantage in improving students’ home visit attitudes or skills.
From disease surveillance to clinical care, artificial intelligence is transforming health. As AI becomes more deeply embedded in decisions affecting population health, a new editorial asserts that realizing AI’s full potential requires public health leadership alongside technical innovation.
Prof. Shiyuan Liu’s team from Huazhong University of Science and Technology has reported a full-chip EUV curvilinear mask optimization framework that integrates deep learning with gradient-based inverse optimization. This framework achieves an estimated speedup of four orders of magnitude over conventional FDTD methods while preserving accuracy, thereby enabling full-chip EUV mask optimization within a practical runtime. Doctoral student Pinxuan He is the first author of this paper, and Prof. Shiyuan Liu and Dr. Jiamin Liu are the co-corresponding authors. Prof. Honggang Gu, Dr. Song Zhang, Prof. Qi Xia, and Prof. Hao Jiang also contributed to this research.
A case report in Chinese Neurosurgical Journal describes a novel salvage surgical approach for refractory multiple sclerosis-related trigeminal neuralgia. In a single patient who had failed medical, percutaneous, and radiosurgical treatments, the procedure combined internal neurolysis (nerve combing) with targeted glycerol delivery to the proximal trigeminal root. The patient experienced immediate pain relief and remained recurrence-free on the treated side for 43 months, with preserved sensation and reduced medication use.