Intelligent medicine最新文献

{"title":"Source independent multiple-domain adaptation for knee osteoarthritis cartilage and meniscus segmentation in clinical magnetic resonance imaging","authors":"Sheheryar Khan ,&nbsp;Siyue Li ,&nbsp;Fan Xiao ,&nbsp;Kevin Ho ,&nbsp;Michael Ong ,&nbsp;James Griffith ,&nbsp;Weitian Chen","doi":"10.1016/j.imed.2024.12.002","DOIUrl":"10.1016/j.imed.2024.12.002","url":null,"abstract":"<div><h3>Background</h3><div>Generalized knee tissue segmentation, such as cartilage and meniscus in magnetic resonance imaging (MRI), plays a vital role in the clinical assessment of knee osteoarthritis (OA). However, domain variability between MRI datasets poses a significant challenge for the application of robust segmentation methods in real-world clinical settings. Existing unsupervised domain adaptation (UDA) approaches, which rely on one-to-one assumptions between the source and target domains, often fail to preserve knee tissues such as cartilage and meniscus, which are critical for OA diagnosis in diverse clinical settings.</div></div><div><h3>Methods</h3><div>We propose a source-independent segmentation approach tailored for multi-domain knee MRI datasets. Our method emphasizes knee tissue regions to reduce domain gaps and label inconsistencies. By introducing a stepwise adaptation strategy, segmentation performance was refined progressively from intermediate domains to the final target domain. Pseudo-label attention mechanisms were integrated into the adaptation pipeline, enabling iterative fine-tuning of domain-specific segmentations while leveraging unidirectional generative adversarial networks to enhance tissue-specific adaptation. This iterative training process ensures the generation of reliable pseudo-labels, thereby improving segmentation accuracy in diverse clinical MRI datasets.</div></div><div><h3>Results</h3><div>We demonstrated the effectiveness of our approach on the OA initiative dataset as the source domain and self-collected, T1-weighted fast field echo (T1FFE) as the intermediate domain and three-dimensional fast spin echo (3D FSE) as the final target domain. Our method achieved an average dice scores of 0.8701 and 0.7990 for source and target domains, respectively, surpassing the typical UDA methods explored in our experiments.</div></div><div><h3>Conclusion</h3><div>The experiments conducted on clinical MRI data, spanning OA severity from healthy knees to KL Grades 1–4, validated the effectiveness of the proposed domain adaptation method in precise segmentation of the cartilage and meniscus.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 209-221"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908158","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}

{"title":"Embracing the challenges of digital orthopedics in the age of artificial intelligence","authors":"Om Prakash Choudhary","doi":"10.1016/j.imed.2025.06.001","DOIUrl":"10.1016/j.imed.2025.06.001","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 247-248"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908157","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}

{"title":"Iteration and evaluation of digital interface for clinical surgery design from the usability perspective","authors":"Bowen Sun ,&nbsp;Saisai Li ,&nbsp;Dijia Li ,&nbsp;Xin Peng ,&nbsp;Bowen Song ,&nbsp;Yirui Wang ,&nbsp;Zixu Zhang ,&nbsp;Xia Wang ,&nbsp;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}

{"title":"Artificial intelligence in the care of patients with rectal cancer undergoing neoadjuvant chemoradiation and intentional watchful waiting: a literature review","authors":"Boyang Qu,&nbsp;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}

{"title":"Guide for Authors","authors":"","doi":"10.1016/S2667-1026(25)00073-7","DOIUrl":"10.1016/S2667-1026(25)00073-7","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 3","pages":"Pages 250-256"},"PeriodicalIF":6.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908156","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}

{"title":"Evaluating large language models and agents in healthcare: key challenges in clinical applications","authors":"Xiaolan Chen ,&nbsp;Jiayang Xiang ,&nbsp;Shanfu Lu ,&nbsp;Yexin Liu ,&nbsp;Mingguang He ,&nbsp;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}

{"title":"DeepSeek and the future of drug discovery: a correspondence on artificial intelligence integration","authors":"Faiza Farhat","doi":"10.1016/j.imed.2025.03.001","DOIUrl":"10.1016/j.imed.2025.03.001","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 164-165"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196210","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}

{"title":"Guide for Authors","authors":"","doi":"10.1016/S2667-1026(25)00045-2","DOIUrl":"10.1016/S2667-1026(25)00045-2","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 166-172"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196211","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}

{"title":"Application of multimodal deep learning in the auxiliary diagnosis and treatment of dermatological diseases","authors":"Ting Li ,&nbsp;Bowei Li ,&nbsp;Yuying Jia ,&nbsp;Lian Duan ,&nbsp;Ping Sun ,&nbsp;Xiaozhen Li ,&nbsp;Xiaodong Yang ,&nbsp;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}

{"title":"Digital orthopedics: the third technological wave of orthopedics","authors":"Jiayao Zhang ,&nbsp;Zhewei Ye","doi":"10.1016/j.imed.2024.09.003","DOIUrl":"10.1016/j.imed.2024.09.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 91-94"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196281","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}