Intelligent medicine最新文献_第2页

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

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

{"title":"Iteration and evaluation of digital interface for clinical surgery design from the usability perspective","authors":"Bowen Sun , Saisai Li , Dijia Li , Xin Peng , Bowen Song , Yirui Wang , Zixu Zhang , Xia Wang , Xu He","doi":"10.1016/j.imed.2024.12.003","DOIUrl":"10.1016/j.imed.2024.12.003","url":null,"abstract":"<div><div><strong>Objective</strong> 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.</div><div><strong>Methods</strong> 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.</div><div><strong>Results</strong> 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.</div><div><strong>Conclusion</strong> 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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 222-233"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Boyang Qu, Aiwen Wu

{"title":"Artificial intelligence in the care of patients with rectal cancer undergoing neoadjuvant chemoradiation and intentional watchful waiting: a literature review","authors":"Boyang Qu, Aiwen Wu","doi":"10.1016/j.imed.2025.05.003","DOIUrl":"10.1016/j.imed.2025.05.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 178-186"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guide for Authors 作者指南

IF 6.9

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

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

{"title":"Evaluating large language models and agents in healthcare: key challenges in clinical applications","authors":"Xiaolan Chen , Jiayang Xiang , Shanfu Lu , Yexin Liu , Mingguang He , Danli Shi","doi":"10.1016/j.imed.2025.03.002","DOIUrl":"10.1016/j.imed.2025.03.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 151-163"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guide for Authors 作者指南

IF 4.4

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

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

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

{"title":"Application of multimodal deep learning in the auxiliary diagnosis and treatment of dermatological diseases","authors":"Ting Li , Bowei Li , Yuying Jia , Lian Duan , Ping Sun , Xiaozhen Li , Xiaodong Yang , Hong Cai","doi":"10.1016/j.imed.2024.10.002","DOIUrl":"10.1016/j.imed.2024.10.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 132-140"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Jiayao Zhang , Zhewei Ye

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

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

{"title":"Application of artificial intelligence-based computer vision methods in liver diseases: a bibliometric analysis","authors":"Yutian Feng , Qi Wang , Yuxin Su , Wenrui Ma , Guifang Du , Jian Wu , Juan Liu , Yunfang Wang","doi":"10.1016/j.imed.2024.09.008","DOIUrl":"10.1016/j.imed.2024.09.008","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 111-122"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improvement of classification accuracy of functional near-infrared spectroscopy signals for hand motion and motor imagery using a common spatial pattern algorithm 基于通用空间模式算法的手部运动和运动图像功能近红外光谱信号分类精度提高

IF 4.4

Intelligent medicine Pub Date : 2025-05-01 DOI: 10.1016/j.imed.2024.05.004

Omid Asadi , Mahsan Hajihosseini , Sima Shirzadi , Zahra Einalou , Mehrdad Dadgostar

{"title":"Improvement of classification accuracy of functional near-infrared spectroscopy signals for hand motion and motor imagery using a common spatial pattern algorithm","authors":"Omid Asadi , Mahsan Hajihosseini , Sima Shirzadi , Zahra Einalou , Mehrdad Dadgostar","doi":"10.1016/j.imed.2024.05.004","DOIUrl":"10.1016/j.imed.2024.05.004","url":null,"abstract":"<div><h3>Objective</h3><div>Classifying motor imagery tasks via functional near-infrared spectroscopy (fNIRS) poses a significant challenge in brain-computer interface (BCI) research due to the high-dimensional nature of the signals. This study aimed to address this challenge by employing the common spatial pattern (CSP) algorithm to reduce input dimensions for support vector machine (SVM) and linear discriminant analysis (LDA) classifiers.</div></div><div><h3>Methods</h3><div>Data were collected from 15 healthy right-handed volunteers performing tasks involving left-hand motion, left-hand motor imagery, right-hand motion, and right-hand motor imagery. Signals from 20-channel fNIRS were utilized, with input features including statistical descriptors such as mean, variance, slope, skewness, and kurtosis. The CSP algorithm was integrated into both SVM and LDA classifiers to reduce dimensionality. The main statistical methods included classification accuracy assessment and comparison.</div></div><div><h3>Results</h3><div>Mean and slope were found to be the most discriminative features. Without CSP, SVM and LDA classifiers achieved average accuracies of 59.81 % ± 0.97 % and 69 % ± 11.42 %, respectively. However, with CSP integration, accuracies significantly improved to 81.63 % ± 0.99 % and 84.19 % ± 3.18 % for SVM and LDA, respectively. This value represents an increase of 21.82 % and 15.19 % in accuracy for SVM and LDA classifiers, respectively. Dimensionality reduction from 100 to 25 dimensions was achieved for SVM, leading to reduced computational complexity and faster calculation times. Additionally, the CSP technique enhanced LDA classifier accuracy by 3.31 % for both motion and motor imagery tasks.</div></div><div><h3>Conclusion</h3><div>Integration of the CSP algorithm may demonstrate promising potential for improving BCI systems' performance.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 123-131"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0