Rethinking fairness in unsupervised healthcare AI: A methodological scoping review [0.03%]
从方法论角度审视医疗保健中无监督AI的公平性：系统综述
Malek Adouani,Djillali Annane,Zaineb Chelly Dagdia
Malek Adouani
Objective: Fairness in machine learning has been extensively studied in supervised settings, where labeled outcomes allow direct assessment of bias. In contrast, fairness in unsupervised learning-particularly in healthcar...
Diagnostic performance of AI-based EEG interpretation versus human clinical experts for epilepsy detection: systematic review and meta-analysis [0.03%]
基于人工智能的EEG解读与人类临床专家在癫痫检测中的诊断性能：系统评价和meta分析
Fábio Augusto Dos Reis,Felipe de Araújo Santana Merique,Marcos Zanetich Filho et al.
Fábio Augusto Dos Reis et al.
Background: Electroencephalography (EEG) interpretation for epilepsy diagnosis faces persistent challenges including specialist shortages, variable interpretation accuracy, and limited accessibility. Artificial intelligen...
Response to Tahir et al.'s commentary on our BSRS causal framework for longitudinal well-being data [0.03%]
对Tahir等人关于我们BSRS纵向幸福感数据因果框架的评论的回应
Pingfan Wang
Pingfan Wang
We thank Tahir et al. for their respectful and constructive commentary on our article proposing the Behaviour Self-Regulation Score (BSRS) and a causally-informed workflow for longitudinal self-report well-being data (Wang et al., 2026). We...
Exploring the role of reinforcement learning in vision-language models for cardiovascular disease decision support [0.03%]
强化学习在心血管疾病决策支持中视觉语言模型的作用探究
Pengze Li,Jianfu Li,Shuteng Niu et al.
Pengze Li et al.
Objective: To explore the role of reinforcement learning (RL) in vision-language models (VLMs) for cardiovascular disease (CVD) decision support and assess whether RL-enhanced multimodal reasoning improves clinical classi...
Extraction of distant recurrence sites for breast cancer patients from free-text clinical notes using large language models [0.03%]
使用大型语言模型从自由文本临床记录中提取乳腺癌患者的远处复发部位
Madhu Babu Sikha,Amara Tariq,Allison W Kurian et al.
Madhu Babu Sikha et al.
Objectives: Accurate documentation of distant recurrence sites in breast cancer is essential for evaluating treatment effectiveness and outcomes research. However, such information is embedded in unstructured clinical not...
A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning [0.03%]
基于大规模语言模型的患者信息抽取研究：模型架构、微调策略及多任务指令调优
Cheng Peng,Xinyu Dong,Mengxian Lyu et al.
Cheng Peng et al.
Background: Natural language processing (NLP) is a key technology to extract patient information from clinical narratives to support healthcare applications. The rapid development of large language models (LLMs) has revol...
Biomedical multimodal large language models: From model-centric development to clinically grounded evaluation and integration [0.03%]
生物医学多模态大型语言模型：从以模型为中心的开发到以临床为基础的评估和整合
Jiang Bian,Yifan Peng,Eneida Mendonca et al.
Jiang Bian et al.
DeepSTFSynergy: A multi-scale structural information fusion method for personalized drug combination prediction [0.03%]
深度药物组合推荐的多尺度结构信息融合方法
Yiran Huang,Linyang Guo,Cuiyu Huang et al.
Yiran Huang et al.
Efficiently predicting drug synergy is crucial for developing personalized cancer combination therapy regimens. However, existing methods primarily focus on single-scale structural information and fail to explicitly model the interactions b...
A heterogeneous graph neural network for candidate gene prediction in endometriosis [0.03%]
用于子宫内膜异位症候选基因预测的异构图神经网络
Zelia Soo,Hua Lin,Mengjia Wu et al.
Zelia Soo et al.
Objective: Precision medicine applications depend on elucidating the underlying molecular mechanisms of disease. However, many disorders, such as endometriosis, remain poorly characterised genetically due to data scarcity...
DynaMamba: Multi-scale dynamic interacting Mamba network for irregular clinical time series classification [0.03%]
多尺度动态交互Mamba网络：不规则临床时间序列分类方法
Hao Chen,Junjie Zhang,Xiaowei Yan et al.
Hao Chen et al.
Irregular clinical time series, composed of patient data from various physiological indicators, are essential for clinical decision-making. Effectively modeling these sequences for tasks like disease diagnosis and mortality prediction is a ...