Using an Integrated, Digital Framework to Standardize and Expand a Multisite Lung Cancer Screening Program [0.03%]
使用集成数字框架标准化和扩展多站点肺癌筛查项目
Maria Katerina C Alfaro,Christine S Shusted,Teresa Giamboy et al.
Maria Katerina C Alfaro et al.
Purpose: Lung cancer screening (LCS) is one of the most potentially impactful interventions of the past two decades for reducing lung cancer mortality. However, no current standard exists in the field for comprehensive da...
Machine Learning Model Predicts Abnormal Lymphocytosis Associated With Chronic Lymphocytic Leukemia [0.03%]
机器学习模型预测慢性淋巴细胞白血病相关的异常淋巴细胞增多
Joseph Aoki,Omar Khalid,Cihan Kaya et al.
Joseph Aoki et al.
Purpose: The diagnosis of chronic lymphocytic leukemia (CLL) is often delayed several years in advance of disease. Addressing this care gap would aid in identifying at-risk patients who may benefit from targeted evaluatio...
Observational Study
JCO clinical cancer informatics. 2025 Jun:9:e2400197. DOI:10.1200/CCI-24-00197 2025
Patient Perspectives on Technological Barriers and Implementation Strategies Leveraged During a Real-World Remote Symptom Monitoring Program [0.03%]
远程症状监测计划中的患者视角：技术障碍及实施策略
Tanvi V Padalkar,Nicole L Henderson,D&#x;Ambra N Dent et al.
Tanvi V Padalkar et al.
Purpose: Remote symptom monitoring (RSM) using electronic patient-reported outcomes leverages digital technologies to gather real-time information on patient experiences for symptom management. This study reports a format...
Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy [0.03%]
基于人工智能的数字病理分类器在根治性前列腺切除患者中划分前列腺癌风险的独立盲法验证
Magdalena Fay,Ross S Liao,Zaeem M Lone et al.
Magdalena Fay et al.
Purpose: Artificial intelligence (AI) tools that identify pathologic features from digitized whole-slide images (WSIs) of prostate cancer (CaP) generate data to predict outcomes. The objective of this study was to evaluat...
Reliability of Large Language Model Knowledge Across Brand and Generic Cancer Drug Names [0.03%]
大型语言模型在品牌名和通用名癌症药物名称上的知识可靠性分析
Jack Gallifant,Shan Chen,Sandeep K Jain et al.
Jack Gallifant et al.
Purpose: To evaluate the performance and consistency of large language models (LLMs) across brand and generic oncology drug names in various clinical tasks, addressing concerns about potential fluctuations in LLM performa...
Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing of Clinical Notes [0.03%]
利用自然语言处理技术对立体定向放射外科手术患者的临床资料进行分类研究
Mario Fugal,David Marshall,Alexander V Alekseyenko et al.
Mario Fugal et al.
Purpose: Accurate identification of the primary tumor diagnosis of patients who have undergone stereotactic radiosurgery (SRS) from electronic health records is a critical but challenging task. Traditional methods of iden...
Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes [0.03%]
基于临床影像组学及机器学习的四肢软组织肉瘤术后预后评估模型
Rami Elmorsi,Luis D Camacho,David D Krijgh et al.
Rami Elmorsi et al.
Purpose: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS) resection is fraught with uncertainty. Leveraging machine learning and clinicoradiomic data, we developed Sarcoma Recon...
Enhancing Patient-Trial Matching With Large Language Models: A Scoping Review of Emerging Applications and Approaches [0.03%]
利用大型语言模型增强患者与临床试验匹配：新兴应用和方法的综述
Hongyu Chen,Xiaohan Li,Xing He et al.
Hongyu Chen et al.
Purpose: Patient recruitment remains a major bottleneck in clinical trial execution, with inefficient patient-trial matching often causing delays and failures. Recent advancements in large language models (LLMs) offer a p...
Accuracy and Reproducibility of ChatGPT Responses to Breast Cancer Tumor Board Patients [0.03%]
ChatGPT在乳腺癌肿瘤委员会患者中的准确性和可重复性答复评估
Ning Liao,Cheukfai Li,William J Gradishar et al.
Ning Liao et al.
Purpose: We assessed the accuracy and reproducibility of Chat Generative Pre-Trained Transformer's (ChatGPT) recommendations in response to breast cancer patients by comparing generated outputs with consensus expert opini...
Clinical Trial Design Approach to Auditing Language Models in Health Care Setting [0.03%]
医疗环境下语言模型审计的试验设计方法
Lovedeep Gondara,Jonathan Simkin,Shebnum Devji
Lovedeep Gondara
Purpose: Rapid advancements in natural language processing have led to the development of sophisticated language models. Inspired by their success, these models are now used in health care for tasks such as clinical docum...