Intelligent medicine最新文献

英文中文

Embracing the challenges of digital orthopedics in the age of artificial intelligence 迎接人工智能时代数字骨科的挑战

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2025.06.001

Om Prakash Choudhary

引用次数: 0

Iteration and evaluation of digital interface for clinical surgery design from the usability perspective 可用性视角下临床外科设计数字界面的迭代与评价

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2024.12.003

Bowen Sun , Saisai Li , Dijia Li , Xin Peng , Bowen Song , Yirui Wang , Zixu Zhang , Xia Wang , Xu He

Objective Based on the development background of digital medical technology, this study aimed to establish design guidelines and references in relevant fields to better serve clinical medical treatment using intelligent technology to enhance the usability of the interaction interface of robotic surgical systems and reduce potential human-factor risks during digital surgery.

Methods Considering the robotic liver cancer ablation surgery system as the research object, subjective and objective evaluation indicators were established from 3 dimensions of effectiveness, efficiency, and satisfaction based on the usability theory. Using the hierarchical task analysis method, usability experiments were conducted to collect relevant data. Feedback on issues during the experimental process was obtained through observation and interviews. Failure mode and effect analysis and fault tree analysis were used to assess risk levels and formulate design strategies.

Results The interface design of the liver cancer ablation surgery robot was iteratively optimized. The results showed that the interface after iteration improved in skilled operation time, subjective evaluation scores, risk priority number value, and risk level. The rationality of the scheme was verified, and interface design paradigm was constructed based on intelligent technology.

Conclusion After improving the design, the interface effectively reduced the frequency of problems and average skilled operation time, thereby, improving the subjective satisfaction score of users.

目的基于数字化医疗技术的发展背景，建立相关领域的设计指南和参考，利用智能技术更好地服务于临床医疗，增强机器人手术系统交互界面的可用性，降低数字化手术过程中潜在的人为因素风险。方法以机器人肝癌消融手术系统为研究对象，基于可用性理论，从有效性、效率、满意度三个维度建立主客观评价指标。采用分层任务分析法，进行可用性实验，收集相关数据。通过观察和访谈，对实验过程中出现的问题进行反馈。采用故障模式及影响分析和故障树分析来评估风险水平，制定设计策略。结果对肝癌消融手术机器人的界面设计进行了迭代优化。结果表明，迭代后的界面在熟练操作时间、主观评价分数、风险优先级数值、风险等级等方面均有提高。验证了方案的合理性，构建了基于智能技术的界面设计范式。结论改进设计后的界面有效降低了出现问题的频率和平均熟练操作时间，从而提高了用户的主观满意度。

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引用次数: 0

Artificial intelligence in the care of patients with rectal cancer undergoing neoadjuvant chemoradiation and intentional watchful waiting: a literature review 人工智能在直肠癌新辅助放化疗患者护理和有意观察等待中的应用：文献综述

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/j.imed.2025.05.003

Boyang Qu, Aiwen Wu

Rectal cancer remains a major global health challenge, prompting ongoing efforts to optimize treatment strategies. In recent years, organ-preserving approaches—particularly the “watch-and-wait” strategy—have gained growing interest. Concurrently, the advent of artificial intelligence (AI) has opened new avenues in personalized oncology. This review explored the emerging role of AI in the individualized management of rectal cancer, with a focus on its potential to improve treatment outcomes and patient prognosis. Herein, we provide a comprehensive synthesis of recent studies investigating AI applications in predicting pathological complete response, metastasis, and disease-free survival following neoadjuvant therapy. These studies employ diverse data modalities, including radiomics (magnetic resonance imaging (MRI), computerized tomography (CT), and endoscopy), clinical parameters, and other omics-based features. The study evaluated the predictive models developed using machine learning and deep learning algorithms, discussing their performance metrics, strengths, and limitations. Despite the ongoing challenges—such as limited data availability, lack of model interpretability, and suboptimal predictive accuracy—AI has demonstrated potential to outperform conventional assessment methods in select areas. These findings may highlight the growing significance of AI in supporting personalized, evidence-based decision-making in rectal cancer care.

直肠癌仍然是一个主要的全球健康挑战，促使人们不断努力优化治疗策略。近年来，器官保存方法——尤其是“观察和等待”策略——获得了越来越多的兴趣。同时，人工智能（AI）的出现为个性化肿瘤学开辟了新的途径。本综述探讨了人工智能在直肠癌个体化治疗中的新作用，重点关注其改善治疗结果和患者预后的潜力。在此，我们综合了最近的研究，研究了人工智能在预测新辅助治疗后的病理完全缓解、转移和无病生存方面的应用。这些研究采用不同的数据模式，包括放射组学（磁共振成像（MRI）、计算机断层扫描（CT）和内窥镜检查）、临床参数和其他基于组学的特征。该研究评估了使用机器学习和深度学习算法开发的预测模型，讨论了它们的性能指标、优势和局限性。尽管存在持续的挑战，例如有限的数据可用性，缺乏模型可解释性，以及次优的预测准确性，但人工智能已经证明了在某些领域优于传统评估方法的潜力。这些发现可能突出了人工智能在支持直肠癌治疗中个性化、循证决策方面日益重要的意义。

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引用次数: 0

Guide for Authors 作者指南

IF 6.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-08-01 DOI: 10.1016/S2667-1026(25)00073-7

引用次数: 0

Evaluating large language models and agents in healthcare: key challenges in clinical applications 评估医疗保健中的大型语言模型和代理：临床应用中的关键挑战

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 DOI: 10.1016/j.imed.2025.03.002

Xiaolan Chen , Jiayang Xiang , Shanfu Lu , Yexin Liu , Mingguang He , Danli Shi

Large language models (LLMs) have emerged as transformative tools with significant potential across healthcare and medicine. In clinical settings, they hold promises for tasks ranging from clinical decision support to patient education. Advances in LLM agents further broaden their utility by enabling multimodal processing and multitask handling in complex clinical workflows. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the high-risk nature of healthcare and the complexity of medical data. This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine. We contributed 3 main aspects: First, we summarized data sources used in evaluations, including existing medical resources and manually designed clinical questions, offering a basis for LLM evaluation in medical settings. Second, we analyzed key medical task scenarios: closed-ended tasks, open-ended tasks, image processing tasks, and real-world multitask scenarios involving LLM agents, thereby offering guidance for further research across different medical applications. Third, we compared evaluation methods and dimensions, covering both automated metrics and human expert assessments, while addressing traditional accuracy measures alongside agent-specific dimensions, such as tool usage and reasoning capabilities. Finally, we identified key challenges and opportunities in this evolving field, emphasizing the need for continued research and interdisciplinary collaboration between healthcare professionals and computer scientists to ensure safe, ethical, and effective deployment of LLMs in clinical practice.

大型语言模型（llm）已经成为在医疗保健和医学领域具有巨大潜力的变革性工具。在临床环境中，他们承担着从临床决策支持到患者教育等任务的承诺。LLM代理的进步通过在复杂的临床工作流程中实现多模式处理和多任务处理进一步扩大了它们的效用。然而，由于医疗保健的高风险性质和医疗数据的复杂性，评估法学硕士在医学背景下的表现提出了独特的挑战。本文提供了一个全面的概述当前的评估实践法学硕士和法学硕士代理人在医学。我们主要贡献了3个方面：首先，我们总结了评估中使用的数据源，包括现有的医疗资源和人工设计的临床问题，为医疗环境中的LLM评估提供了基础。其次，我们分析了关键的医疗任务场景：封闭式任务、开放式任务、图像处理任务和涉及LLM代理的真实多任务场景，从而为不同医疗应用的进一步研究提供指导。第三，我们比较了评估方法和维度，涵盖了自动化度量和人类专家评估，同时解决了传统的准确性度量以及特定于代理的维度，如工具使用和推理能力。最后，我们确定了这一不断发展的领域的关键挑战和机遇，强调了医疗保健专业人员和计算机科学家之间持续研究和跨学科合作的必要性，以确保法学硕士在临床实践中的安全、道德和有效部署。

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引用次数: 0

Guide for Authors 作者指南

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 DOI: 10.1016/S2667-1026(25)00045-2

引用次数: 0

DeepSeek and the future of drug discovery: a correspondence on artificial intelligence integration DeepSeek与药物发现的未来：人工智能集成通信

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 DOI: 10.1016/j.imed.2025.03.001

Faiza Farhat

引用次数: 0

Application of multimodal deep learning in the auxiliary diagnosis and treatment of dermatological diseases 多模态深度学习在皮肤病辅助诊断与治疗中的应用

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 DOI: 10.1016/j.imed.2024.10.002

Ting Li , Bowei Li , Yuying Jia , Lian Duan , Ping Sun , Xiaozhen Li , Xiaodong Yang , Hong Cai

Skin diseases are important factors affecting health and quality of life, especially in rural areas where medical resources are limited. Early and accurate diagnosis can reduce unnecessary health and economic losses. However, traditional visual diagnosis poses a high demand on both doctors’ experience and the examination equipment, and there is a risk of missed diagnosis and misdiagnosis. Recently, advances in artificial intelligence technology, particularly deep learning, have resulted in the use of unimodal computer-aided diagnosis and treatment technologies based on skin images in dermatology. However, due to the small amount of information contained in unimodality, this technology cannot fully demonstrate the advantages of multimodal data in the real-world medical environment. Multimodal data fusion can fully integrate various types of data to help doctors make more accurate clinical decisions. This review aimed to provide a comprehensive overview of multimodal data and deep learning methods that could help dermatologists diagnose and treat skin diseases.

皮肤病是影响健康和生活质量的重要因素，特别是在医疗资源有限的农村地区。早期和准确的诊断可以减少不必要的健康和经济损失。然而，传统的视觉诊断对医生的经验和检查设备的要求都很高，存在漏诊和误诊的风险。最近，人工智能技术的进步，特别是深度学习，已经导致在皮肤病学中使用基于皮肤图像的单模计算机辅助诊断和治疗技术。然而，由于单模态所包含的信息量较少，该技术无法在现实医疗环境中充分体现多模态数据的优势。多模式数据融合可以充分整合各类数据，帮助医生做出更准确的临床决策。本综述旨在提供多模态数据和深度学习方法的全面概述，这些方法可以帮助皮肤科医生诊断和治疗皮肤病。

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引用次数: 0

Digital orthopedics: the third technological wave of orthopedics 数字骨科：骨科的第三次技术浪潮

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 DOI: 10.1016/j.imed.2024.09.003

Jiayao Zhang , Zhewei Ye

As an emerging interdisciplinary field, digital orthopedics is hailed as the third technological wave in orthopedics, with its applications gradually expanding into various areas and continuously innovating orthopedic clinical practice. Through advanced technologies such as 3D printing, extended reality, finite-element analysis, robotic-assisted surgery, and artificial intelligence, the diagnosis, treatment, and rehabilitation of orthopedic diseases have become more convenient, precise, and personalized. This article primarily introduced the main advantages and applications of digital orthopedic technology and evaluates its clinical efficacy and development potential, providing important references for future research and clinical practice.

数字骨科作为一门新兴的交叉学科，被誉为骨科领域的第三次技术浪潮，其应用领域逐渐扩展到各个领域，不断创新骨科临床实践。通过3D打印、扩展现实、有限元分析、机器人辅助手术、人工智能等先进技术，骨科疾病的诊断、治疗和康复变得更加便捷、精准和个性化。本文主要介绍了数字骨科技术的主要优势和应用，并对其临床疗效和发展潜力进行了评价，为今后的研究和临床实践提供重要参考。

引用次数: 0

Application of artificial intelligence-based computer vision methods in liver diseases: a bibliometric analysis 基于人工智能的计算机视觉方法在肝病中的应用：文献计量学分析

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Intelligent medicine

Pub Date : 2025-05-01 DOI: 10.1016/j.imed.2024.09.008

Yutian Feng , Qi Wang , Yuxin Su , Wenrui Ma , Guifang Du , Jian Wu , Juan Liu , Yunfang Wang

Medical imaging is essential for the diagnosis and treatment of liver diseases, and the objective analysis of such images is vital for precision medicine. Integration of artificial intelligence (AI), particularly computer vision, into hepatology has seen considerable growth. This study conducts a bibliometric analysis to map the evolution, principal trends, and focal points of AI in liver disease imaging research. We conducted a comprehensive literature review using the Web of Science Core Collection and PubMed databases, spanning January 1990 to July 2023, with keywords related to liver diseases and AI in medical imaging. The search resulted in 3,629 documents, with a surge in publications after 2017. The United States and China led in terms of publication volume, with the former exhibiting higher H-index scores and citation counts. However, greater number of research institutions that contribute significantly to publications in the relevant fields are based in China. Keyword analysis revealed extensive research on liver fibrosis, hepatocellular carcinoma, cirrhosis, and fatty liver disease. Techniques such as image segmentation, classification, and registration are prevalent, meeting clinical needs like lesion detection and disease prognosis. Convolutional neural networks (CNNs), particularly U-Net models, are predominantly utilized. This review synthesizes the findings to guide future advancements in AI-assisted liver disease diagnosis and management.

医学影像对肝脏疾病的诊断和治疗至关重要，对影像的客观分析对精准医学至关重要。人工智能（AI），特别是计算机视觉，与肝病学的整合已经取得了相当大的增长。本研究进行文献计量学分析，以绘制人工智能在肝脏疾病影像学研究中的演变、主要趋势和重点。我们使用Web of Science Core Collection和PubMed数据库进行了全面的文献综述，时间跨度为1990年1月至2023年7月，关键词为肝脏疾病和医学成像中的人工智能。搜索结果为3629份文件，2017年之后出版物激增。美国和中国在论文发表量方面处于领先地位，前者的h指数得分和引用次数更高。然而，对相关领域的出版物做出重大贡献的更多研究机构位于中国。关键词分析显示对肝纤维化、肝细胞癌、肝硬化和脂肪肝疾病的广泛研究。目前，图像分割、分类、配准等技术较为普遍，满足了病灶检测、疾病预后等临床需求。卷积神经网络（cnn），特别是U-Net模型，被广泛使用。本文综述了这些发现，以指导人工智能辅助肝病诊断和管理的未来进展。

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