Acceptable accuracy for medical AI: a survey of physicians and the general population in Sweden [0.03%]
瑞典医务人员和普通民众对医疗AI的接受度及其准确性的调查研究
Rasmus Arvidsson,Jonathan Widén,Lina Al-Naasan et al.
Rasmus Arvidsson et al.
Objectives: To identify the lowest sensitivity and specificity that physicians and the general population consider acceptable for medical artificial intelligence (AI), relative to current human performance. ...
Biomarkers associated with future suicide risk enhance predictive performance in psychiatric inpatients [0.03%]
与未来自杀风险相关的生物标志物可提高精神病住院患者的预测性能
Zheya Cai,Enzhao Zhu,Jianmeng Dai et al.
Zheya Cai et al.
Objectives: Suicide risk assessments currently rely on subjective clinical judgement, lacking objective measures. This study aimed to evaluate the association between biomarkers and suicide risk and to explore their predi...
Novel two-stage deep learning framework for automated pressure injury classification [0.03%]
一种新的两阶段深度学习压力性损伤分类框架
Ting-Yu Lai,Yi-Jiun Chou,Chun-You Liu et al.
Ting-Yu Lai et al.
Objective: The study aims to develop an artificial intelligence (AI) framework for automatic pressure injury (PI) staging directly from raw clinical images, without requiring manual lesion localisation. By integrating a t...
Introduction to secure data sharing in primary care using the federated causal learning models [0.03%]
基于联合因果学习模型的初级保健安全数据共享概述
Miaoshuang Chen,Zongqi Chang,Peng Gong et al.
Miaoshuang Chen et al.
Objectives: In primary healthcare research, there are core challenges such as data silos and missing data. Furthermore, the current high technical barriers severely limit effective cross-regional data analysis. ...
Engineering framework for curiosity-driven and humble AI in clinical decision support [0.03%]
一种用于临床决策支持的好奇心驱动和谨慎的人工智能的工程框架
Janan Arslan,Kurt Benke,Sebastian Andres Cajas Ordones et al.
Janan Arslan et al.
We present BODHI (Balanced, Open-minded, Diagnostic, Humble, and Inquisitive), an engineering framework for curiosity driven and humble clinical decision support artificial intelligence (AI) systems. Despite growing capabilities, large lang...
Practical adaptability of a pre-hospital prognostic prediction model for patients following out-of-hospital cardiac arrest during the COVID-19 pandemic [0.03%]
新冠肺炎疫情期间院前心脏骤停患者的预后预测模型的实用性适应性研究
Masahiro Nishi,Akira Shikuma,Eiichiro Uchino et al.
Masahiro Nishi et al.
Objectives: The overwhelmed situation under the COVID-19 pandemic has worsened the quality of emergency medical care and the mortality rate due to out-of-hospital cardiac arrest (OHCA). However, there has been no research...
Virtual reality-based mindfulness applications: a commercial health app review [0.03%]
基于虚拟现实的正念应用程序：商业健康应用评价
Shraboni Ghosal,Mengying Zhang,Angeliki Bogosian et al.
Shraboni Ghosal et al.
Background: Mindfulness can positively impact physical and mental health, but face-to-face programmes are limited by poor accessibility, availability and cost. Virtual reality (VR) offers immersive audiovisual environment...
Unlocking digital health: inequalities in the adoption of a patient portal [0.03%]
解开数字医疗：患者门户采用的不平等现象
Richard David Barker,Refik Gökmen,Daisy Naylor et al.
Richard David Barker et al.
Objective: Digital health apps and patient portals are proposed as part of the drive from 'analogue to digital' care for the National Health Service (NHS) 10-Year Plan. Without mitigation strategies, digital inequalities ...
Impact of the Federated Data Platform's digital surgery scheduling system on elective theatre utilisation at an NHS Trust: an interrupted time series analysis [0.03%]
联邦数据平台的数字手术排程系统对一家NHS信托医院择期手术室利用的影响：一项中断时间序列分析
Elena Lammila-Escalera,Gabriele Kerr,Geva Greenfield et al.
Elena Lammila-Escalera et al.
Objectives: To evaluate the National Health Service (NHS) Federated Data Platform (FDP) Inpatient (IP) Care Coordination Solution (CCS) digital scheduling tool on elective theatre utilisation. ...
Comparison of large language models and expert multidisciplinary team decisions in colorectal cancer [0.03%]
比较大型语言模型与专家多学科团队在结直肠癌诊疗中的决策差异
Boyang Qu,Longhao Cao,Chen Wu et al.
Boyang Qu et al.
Objectives: To evaluate the ability of large language models (LLMs) to simulate multidisciplinary team (MDT) decision-making in colorectal cancer, a malignancy that often requires complex treatment planning. ...
Comparative Study
BMJ health & care informatics. 2026 Mar 10;33(1):e101780. DOI:10.1136/bmjhci-2025-101780 2026