Unsupervised Large Language Models to Identify Topics in Cancer Center Patient Portal Messages [0.03%]
无监督大型语言模型在癌症中心患者门户网站消息中识别主题
Ji Hyun Chang,Amir Ashraf-Ganjouei,Isabel Friesner et al.
Ji Hyun Chang et al.
Purpose: The increasing use of patient portal messages has enhanced patient-provider communication. However, the high volume of these messages has also contributed to physician burnout. ...
Artificial Intelligence-Based Model Exploiting Hematoxylin and Eosin Images to Predict Rare Gene Mutations in Patients With Lung Adenocarcinoma [0.03%]
基于人工智能的模型利用苏木精和伊红图像预测肺腺癌患者的罕见基因突变
Peiling Yu,Weixing Chen,Nan Liu et al.
Peiling Yu et al.
Purpose: Accurately identifying gene mutations in lung cancer is crucial for treatment, while molecular diagnostic methods are time-consuming and complex. This study aims to develop an advanced deep learning model to addr...
Development of Machine Learning Systems to Predict Cancer-Related Symptoms With Validation Across a Health Care System [0.03%]
在一个医疗系统内开发并验证用于预测癌症相关症状的机器学习模型
Baijiang Yuan,Muammar Kabir,Jiang Chen He et al.
Baijiang Yuan et al.
Purpose: Cancer and its treatment cause symptoms. In this study, we aimed to develop machine learning (ML) systems that predict future symptom deterioration among people receiving treatment for cancer and then validate th...
Mitigating Ethical Issues for Large Language Models in Oncology: A Systematic Review [0.03%]
肿瘤学中大型语言模型的伦理问题及系统性审查方法
Shuang Zhou,Xingyi Liu,Zidu Xu et al.
Shuang Zhou et al.
Purpose: Large language models (LLMs) have demonstrated remarkable versatility in oncology applications, such as cancer staging and survival analysis. Despite their potential, ethical concerns such as data privacy breache...
Hybrid ReGex and Natural Language Inference Model as a Zero-Shot Classifier for Extracting Data From Medical Reports [0.03%]
基于医学报告的混合正则表达式和自然语言推理模型的零样本数据提取方法
Nicolas Wagneur,Olivier Capitain,Stéphane Supiot et al.
Nicolas Wagneur et al.
Purpose: This study presents a new method based on regular expressions (ReGex) and artificial intelligence for extracting relevant medical data from clinical reports. This hybrid approach is designed to address the limita...
Clinician's Artificial Intelligence Checklist and Evaluation Questionnaire: Tools for Oncologists to Assess Artificial Intelligence and Machine Learning Models [0.03%]
临床医生的人工智能检查表和评估问卷：肿瘤学家评估人工智能和机器学习模型的工具
Nadia S Siddiqui,Yazan Bouchi,Syed Jawad Hussain Shah et al.
Nadia S Siddiqui et al.
Advancements in oncology are accelerating in the fields of artificial intelligence (AI) and machine learning. The complexity and multidisciplinary nature of oncology necessitate a cautious approach to evaluating AI models. The surge in deve...
Development and Validation of an Ipsilateral Breast Tumor Recurrence Risk Estimation Tool Incorporating Real-World Data and Evidence From Meta-Analyses: A Retrospective Multicenter Cohort Study [0.03%]
基于真实世界数据和荟萃分析证据的同侧乳腺肿瘤复发风险评估工具的开发与验证：一项回顾性多中心队列研究
Yasuaki Sagara,Atsushi Yoshida,Yuri Kimura et al.
Yasuaki Sagara et al.
Purpose: Ipsilateral breast tumor recurrence (IBTR) remains a critical concern for patients undergoing breast-conserving surgery (BCS). Reliable risk estimation tools for IBTR risk can support personalized surgical and ad...
Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer [0.03%]
利用贝叶斯网络预测非小细胞肺癌全身治疗患者的急诊就诊次数
Brian D Gonzalez,Xiaoyin Li,Lisa M Gudenkauf et al.
Brian D Gonzalez et al.
Purpose: Patients receiving systemic therapy (ST) for non-small cell lung cancer (NSCLC) experience toxicities that negatively affect patient outcomes. This study aimed to test an approach for prospectively collecting pat...
Enhancing Readability of Lay Abstracts and Summaries for Urologic Oncology Literature Using Generative Artificial Intelligence: BRIDGE-AI 6 Randomized Controlled Trial [0.03%]
利用生成式人工智能改进泌尿生殖系统肿瘤文献非专业读者摘要可读性的BRIDGE-AI 6随机对照试验
Conner Ganjavi,Ethan Layne,Francesco Cei et al.
Conner Ganjavi et al.
Purpose: To evaluate a generative artificial intelligence (GAI) framework for creating readable lay abstracts and summaries (LASs) of urologic oncology research, while maintaining accuracy, completeness, and clarity, for ...
Randomized Controlled Trial
JCO clinical cancer informatics. 2025 Sep:9:e2500042. DOI:10.1200/CCI-25-00042 2025