Explainable artificial intelligence for personalized prognosis in pancreatic cancer: A nationwide study from Taiwan [0.03%]
台湾全国胰腺癌个性化预后预测的解释型人工智能模型研究
Dai-Rong Tsai,Chun-Ju Chiang,Pei-Chun Hsieh et al.
Dai-Rong Tsai et al.
Pancreatic cancer is highly aggressive with poor outcomes; current artificial intelligence (AI) prognostic models often lack interpretability and underutilize large-scale data. This study develops an explainable AI prognostic model for panc...
Correction: A feature-based qualitative assessment of smoking cessation mobile applications [0.03%]
关于一种基于特征的戒烟手机应用程序定性评价的更正通知
Lydia Tesfaye,Michael Wakeman,Gunnar Baskin et al.
Lydia Tesfaye et al.
[This corrects the article DOI: 10.1371/journal.pdig.0000658.]. Copyright: © 2026 Tesfaye et al. This is an open access article distributed under the te...
Published Erratum
PLOS digital health. 2026 Mar 19;5(3):e0001307. DOI:10.1371/journal.pdig.0001307 2026
Using supermarket loyalty card data to investigate seasonal variation in laxative purchases in the UK [0.03%]
利用超市忠诚卡数据调查英国泻药购买的季节性变化
Romana Burgess,Neo Poon,Edward Sloan et al.
Romana Burgess et al.
While laxatives are designed to manage the symptoms of constipation, they are also known to be misused for weight management, particularly by individuals with eating disorders. This study investigates the relationship between laxative purch...
Evaluating the impact of discordant and missing demographic information on population health assessments using linked electronic health records and Census Bureau microdata [0.03%]
评估种族和社会经济地位信息不一致和缺失对人口健康状况评价的影响：利用电子健康记录与人口普查微观数据进行匹配的分析
Derek Ouyang,Aubrey Limburg,David H Rehkopf et al.
Derek Ouyang et al.
Administrative records are increasingly being used to study population-level outcomes, despite high rates of missingness and discrepancies (i.e., discordance) in demographic identifiers across different sources of data, which could reduce t...
"It could bring a lot of help to people that aren't getting help right now": A qualitative analysis of the impact of virtual care on access to primary care for people with opioid use disorder [0.03%]
“它可能为目前没有得到帮助的人带来很多帮助”：虚拟护理对阿片类药物业障碍初级保健获取影响的定性分析
Shawna Narayan,Sarah Spencer,Lindsay Hedden et al.
Shawna Narayan et al.
Primary care plays a vital role for people with opioid use disorder and the COVID-19 pandemic introduced new challenges for this population to access primary care. While the expansion of virtual care was intended to support access to essent...
Interpretable machine learning for predicting delays in seeking abortion among reproductive-aged women in Ethiopia: A study using EDHS 2016 data [0.03%]
基于埃塞俄比亚2016年 DHS 数据的可解释机器学习预测生殖年龄女性流产延迟问题的研究
Meron Asmamaw Alemayehu,Almaw Genet Yeshiwas,Abebaw Molla Kebede et al.
Meron Asmamaw Alemayehu et al.
Delayed access to abortion care in Ethiopia poses significant public health risks, yet it has not been studied using advanced machine learning models with interpretable techniques. This study aims to identify its key predictors through Shap...
Cardiology knowledge assessment of retrieval-augmented open versus proprietary large language models [0.03%]
检索增强型开源语言模型与专有大型语言模型的心脏病学知识评估
Constantine Tarabanis,Shaan Khurshid,Areti Karamanou et al.
Constantine Tarabanis et al.
To evaluate the performance of open-weight and proprietary LLMs, with and without Retrieval-Augmented Generation (RAG), on cardiology board-style questions and benchmark them against the human average. We tested 14 LLMs (6 open-weight, 8 pr...
Advancing the science of qualitative patient preference assessment using large language models [0.03%]
利用大型语言模型推进定性患者偏好评估的科学
Ted Grover,Emanuel Krebs,Deirdre Weymann et al.
Ted Grover et al.
Patient experiences and perspectives are essential for shaping patient-centered healthcare. While large language models (LLMs) in healthcare are typically applied to specific clinical or patient-facing tasks, they have not been used for qua...
Real World Human-LLM Interactions - Prospective blinded versus unblinded expert physician assessments of LLM responses to complex medical dilemmas [0.03%]
真实世界的人类与大语言模型互动：前瞻性盲法与非盲法专家医师对大语言模型复杂医疗困境回应的评估
Itamar Ben Shitrit,Daphna Idan,Mark Volevich et al.
Itamar Ben Shitrit et al.
Current evaluations of large language models (LLMs) in healthcare have largely emphasized theoretical benchmarks and clinician oversight, with limited exploration of real-world physician-AI interaction. In this two-stage prospective study, ...
Beyond translation: A review into preserving human connection in AI-mediated health language interpretation [0.03%]
超越翻译：审查人工智能介导的健康语言解读中的人文关怀保护措施
Kristina M Kokorelias,Kateryna Metersky,Christina Reppas-Rindlisbacher et al.
Kristina M Kokorelias et al.