Intelligent medicine最新文献

{"title":"The entropic critical illness theory: Rethinking from the first principles","authors":"Wei Huang,&nbsp;Dawei Liu,&nbsp;Xiaoting Wang,&nbsp;Wanhong Yin,&nbsp;on behalf of the Critical Care Ultrasound Study Group (CCUSG)","doi":"10.1016/j.imed.2026.07.003","DOIUrl":"10.1016/j.imed.2026.07.003","url":null,"abstract":"<div><div>Critical care medicine is at a pivotal stage of transformation from symptom-based approaches toward systems science. Building on a first-principles understanding of the mechanisms underlying critical illness, we propose the entropic critical illness theory (ECIT). Grounded in the second law of thermodynamics, ECIT posits that critical illness arises when the body’s capacity to regulate entropy production is impaired, leading to systemic disorder. This process is manifested by the concurrent escalation of host response entropy and hemodynamic entropy, resulting in disruptions in blood flow and oxygen delivery, ischemia, and hypoxia at the level of the critical unit, which is defined as the terminal microcirculatory–mitochondrial functional unit, ultimately culminating in multi-organ dysfunction. This study delineates the theoretical foundations of ECIT and outlines an entropy-based critical care framework that may inform future multimodal entropy-informed monitoring, risk stratification, and AI-assisted precision intervention in critical care medicine.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 4","pages":"Pages 374-377"},"PeriodicalIF":7.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148916485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and enabled technologies for neurological disorders: A narrative review and critical analysis","authors":"Keerthana Choudari,&nbsp;Samayaditya Singh,&nbsp;Sreelekshmi R,&nbsp;Pramod R. Somvanshi","doi":"10.1016/j.imed.2025.12.004","DOIUrl":"10.1016/j.imed.2025.12.004","url":null,"abstract":"<div><div>Neurological disorders, encompassing a diverse array of conditions affecting the brain, spinal cord, and peripheral nerves, have emerged as significant contributors to global disability and mortality. Recent studies indicate a marked increase in the burden of these disorders, necessitating innovative, scalable solutions for diagnosis and management. This review explores the translational potential of advanced artificial intelligence (AI) and machine learning (ML) technologies in the assessment and treatment of neurological and neurodevelopmental disorders, including epilepsy, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder, and neurodegenerative diseases like Alzheimer’s, Parkinson’s, Huntington’s, stroke, multiple sclerosis, etc. By integrating AI-driven toolsranging from diagnostic algorithms to wearable technologies,this research highlights their capabilities in early detection, personalized treatment, and improved patient outcomes. AI-powered technologies aid clinicians in understanding the complex, nonlinear neurological features of an individual for better diagnosis of neurological disorders. The review covers recent developments in learning algorithms and feature selection methodologies for better accuracy in disease prediction. The application of deep learning algorithms for brain image-based data analysis, feature extraction, and pattern learning for developing a diagnostic tool is highlighted. We have explored the integration of these AI models in wearables, ChatGPT, and gaming-based apps for real-time monitoring and patient-specific therapies. The paper discusses real-world applications, ethical considerations, and challenges associated with AI implementation in healthcare while providing a comprehensive overview of current methodologies and statistical insights. A critical analysis of the AI tools was performed using a rubric of seven domain grading system. We have discussed the importance of data volume and feature variablity for reducing the misdiagnosis due to bias or variance, which will improve diagnostic precision and user trust. The review serves as a resource for researchers, clinicians, and policymakers aiming to advance global brain health initiatives and optimize neurological care. Through systematic literature analysis, the findings highlight the critical role of AI in reshaping the landscape of neurological disorder management, emphasizing the need for continued research and development in this rapidly evolving field.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Pages 322-335"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148544646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting lateral pelvic lymph nodes in rectal cancer: response to neoadjuvant therapy and artificial intelligence driven clinical decision support","authors":"Ruiqing Liu,&nbsp;Yun Lu,&nbsp;Luca Stocchi","doi":"10.1016/j.imed.2025.08.007","DOIUrl":"10.1016/j.imed.2025.08.007","url":null,"abstract":"<div><div>Rectal cancer with lateral pelvic lymph node (LPLN) involvement presents significant challenges. Neoadjuvant chemoradiation (nCRT) became accepted as a standard preoperative approach in locally advanced rectal cancer, but its effectiveness in addressing LPLN metastasis remains debated, particularly when compared with prophylactic LPLN dissection (LPLND), due to patient and treatment heterogeneity. Current evidence supports reviewing pre- and post-treatment imaging to identify suspicious LPLNs, using a baseline short axis ≥7 mm and restaging short axis ≥4 mm as thresholds. The introduction of total neoadjuvant therapy has added complexity to LPLN management, with indications for LPLND evolving alongside advances in understanding LPLN size, location, and morphology. In recent years, artificial intelligence (AI) techniques including radiomics, image segmentation (e.g., conventional neural network), multimodal model, natural language processing, and surgery navigation have emerged as a promising solution to tackle the complex challenges in LPLN management. Accurate LPLN mapping and minimally invasive techniques enable more precise LPLND, reshaping the scale of surgical decision-making. Thus, optimizing LPLN evaluation methods and exploring AI applications in this process offer potential for tailoring treatment strategies, enhance tumor response, and improve patient outcomes.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Pages 313-321"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148544653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guide for Authors","authors":"","doi":"10.1016/S2667-1026(26)00077-X","DOIUrl":"10.1016/S2667-1026(26)00077-X","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Pages 360-366"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148544654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRC-BERT-GCN: Development and validation of a pre-trained language model for predicting colorectal cancer phenotypes from radiology reports and electronic health records","authors":"Jia Li,&nbsp;Xinghao Wang,&nbsp;Fanxin Zeng,&nbsp;Zhixiang Wang,&nbsp;Linkun Cai,&nbsp;Shui Liu,&nbsp;Songhua Yang,&nbsp;Xinke Jiang,&nbsp;Yuyi Li,&nbsp;Han Lyu,&nbsp;Wenjuan Liu","doi":"10.1016/j.imed.2025.03.004","DOIUrl":"10.1016/j.imed.2025.03.004","url":null,"abstract":"<div><h3>Background</h3><div>Colorectal cancer (CRC) presents a significant global health challenge owing to its rising incidence. Early detection and precise prediction of molecular target expression, guided by key biomarkers, such as CK-20, Ki-67, P-53, and microsatellite instability, are essential for personalizing treatment strategies and improving patient outcomes. However, current approaches often struggle to capture intricate semantic and relational structures within vast medical records and corpus.</div></div><div><h3>Methods</h3><div>We developed CRC-Bidirectional Encoder Representations from Transformer (BERT)-graph convolutional network (GCN), a pre-trained deep learning framework that integrates graph neural network, to fully use the semantic information in predicting cancer phenotypes. We retrospectively collected data on 6,468 patients with CRC from a tertiary hospital in Beijing, from 2012 to 2022, electronic health records and radiology reports were collected and divided into training sets (70%), validation sets (10%), and tests (20%) stratified by molecular phenotypes. The model performance was evaluated on accuracy, precision, recall, and F1-Score. Ablation studies and attention analysis were performed to interpret the model’s performance and internal mechanisms.</div></div><div><h3>Results</h3><div>CRC-BERT-GCN outperformed all basic models and achieved the best performance across all evaluation metrics, with an F1 Score of 73.67%. Compared with the baseline models, it improved accuracy by 4.25% and F1 Score by 4.39%, demonstrating the effectiveness of integrating GCN for biomarker prediction. Moreover, the results were interpreted using attention heatmap.</div></div><div><h3>Conclusion</h3><div>CRC-BERT-GCN may demonstrate superior performance in predicting CRC biomarkers, and outperforming baseline models, highlighting its potential for advancing personalized treatment strategies through improved molecular target prediction.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Pages 301-312"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148544583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepSeek in drug discovery: Comparing strengths, ethical constraints, and envisioning the future","authors":"Shahab Saquib Sohail","doi":"10.1016/j.imed.2025.04.003","DOIUrl":"10.1016/j.imed.2025.04.003","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Page 358"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148558148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translational application of a self-organized deep feature engineering pipeline for non-invasive pulmonary hypertension classification from routine chest radiographs","authors":"Tarık Kıvrak,&nbsp;Mehmet Ali Gelen,&nbsp;Ozge Salkin,&nbsp;Ozkan Karaca,&nbsp;Prabal Datta Barua,&nbsp;Sengul Dogan,&nbsp;Turker Tuncer,&nbsp;Ru-San Tan,&nbsp;Massimo Salvi,&nbsp;Vinitha Sree Subbhuraam,&nbsp;U.R. Acharya","doi":"10.1016/j.imed.2025.06.003","DOIUrl":"10.1016/j.imed.2025.06.003","url":null,"abstract":"<div><h3>Background</h3><div>Pulmonary hypertension (PH) causes high mortality and poses diagnostic challenges. Current guidelines require invasive right heart catheterization (RHC) to confirm mean pulmonary artery pressure ≥25 mmHg. Delayed diagnosis impairs timely treatment. It is unknown whether standard chest X-rays can stratify PH severity. We aimed to develop and validate Exemplar MobileNet (ExMobileNet), an explainable artificial intelligence (AI) model that classifies PH into hemodynamic categories from routine chest X-ray images and thus supports non-invasive severity assessment.</div></div><div><h3>Methods</h3><div>We collected 1,293 de-identified chest X-rays obtained from 2018 to 2023. The cohort comprised 135 patients with PH confirmed using RHC and 551 healthy controls. We defined seven multi-class tasks for key hemodynamic parameters (such as mean pulmonary artery pressure, pulmonary vascular resistance, and cardiac index). The ExMobileNet workflow consists of: (1) Feature extraction via MobileNetV2, (2) feature selection by neighborhood component analysis and chi-square feature selectors, (3) classification with k-nearest neighbors and support vector machines and (4) decision fusion by majority vote and greedy optimization.</div></div><div><h3>Results</h3><div>Task-level accuracy ranged from 90.3% to 93.2%. Geometric mean scores ranged from 78.9% to 85.1%. Overall sensitivity and specificity were 88.5% and 91.3%, respectively. Mean accuracy across all tasks was 92.0% (±1.2%). Average inference time was 2.3 ± 0.4 second per image on CPU-only hardware.</div></div><div><h3>Conclusion</h3><div>ExMobileNet achieved high agreement with RHC-based assessments using routine chest X-rays. This AI tool may enable earlier, non-invasive PH screening in clinical practice.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Pages 272-284"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148544650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing echocardiographic artificial intelligence systems with large language models: QHAutoEF and the role of DeepSeek in future development","authors":"Abdul Rahman,&nbsp;Sultan Alam,&nbsp;Shahab Saquib Sohail,&nbsp;Waseem Khan","doi":"10.1016/j.imed.2025.05.010","DOIUrl":"10.1016/j.imed.2025.05.010","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Page 359"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148558149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the assessment of discharge fitness in an acute medical unit using wearable sensor data through machine learning","authors":"Sjoerd H. Garssen,&nbsp;Sandra F. Oude Wesselink,&nbsp;Carine J.M. Doggen,&nbsp;Mark V. Koning,&nbsp;Bernard P. Veldkamp,&nbsp;Maryam Amir Haeri","doi":"10.1016/j.imed.2025.12.010","DOIUrl":"10.1016/j.imed.2025.12.010","url":null,"abstract":"<div><h3>Background</h3><div>Assessing the physiological discharge fitness of acute medical unit (AMU) patients is feasible using machine learning with electronic medical record (EMR) data, including intermittently measured vital signs. Wearable sensor data, including continuously monitored vital signs, may improve this assessment. Therefore, the aim was to investigate the value of sensor data in assessing the physiological discharge fitness of AMU patients.</div></div><div><h3>Methods</h3><div>EMR and sensor data of AMU patients (<em>n</em> = 145) who participated in a randomized controlled trial, conducted at a large teaching hospital in the Netherlands from December 2021 to October 2023, were used. EMR features were extracted from patient characteristics, vital signs, nursing assessments, and laboratory results. Statistical features and neural features, derived from convolutional neural networks, were extracted from sensor data. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were used as algorithms. For each, three models were optimized and assessed using nested cross-validation: based on EMR, EMR with sensor, or only sensor data. The mean area under the receiver operator characteristic curve (AUROC) value with a 95% confidence interval (95% CI) was used as the primary metric.</div></div><div><h3>Results</h3><div>For LR, RF, and XGB, the mean AUROC value (shown with 95% CI), obtained by models based on EMR with sensor data were 0.73 (95% CI: 0.70–0.77), 0.73 (95% CI: 0.69–0.77), and 0.69 (95% CI: 0.64–0.73), respectively, which were slightly better than or as good as that obtained by models only considering EMR data: 0.69 (95% CI: 0.65–0.73), 0.70 (95% CI: 0.66–0.73), and 0.69 (95% CI: 0.66–0.71), respectively, and as that obtained by models only considering sensor data: 0.70 (95% CI: 0.66–0.75), 0.71 (95% CI: 0.67–0.75), and 0.67 (95% CI: 0.63–0.71), respectively.</div></div><div><h3>Conclusion</h3><div>Wearable sensor data could slightly improve the assessment of the physiological discharge fitness of AMU patients compared with only using EMR data.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Pages 292-300"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148544651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in artificial intelligence for gynecological imaging: Technical bottlenecks and future engineering solutions","authors":"Jiahui Yu,&nbsp;Yirui Wang,&nbsp;Zheng Li,&nbsp;Yang Shen","doi":"10.1016/j.imed.2025.04.005","DOIUrl":"10.1016/j.imed.2025.04.005","url":null,"abstract":"<div><div>Artificial intelligence (AI) has shown significant promise in gynecological imaging, particularly in diagnosing gynecological cancers and benign diseases. However, the application of AI in this field faces several technical challenges, including data quality, model generalizability, and clinical interpretability. We have reviewed the current state of AI in gynecological imaging, identified key technical bottlenecks, and proposed future engineering solutions. Through case studies, we highlight the potential and challenges of AI in gynecological diagnosis, providing a roadmap for future research directions. We emphasize the importance of data sharing, model optimization, and clinical validation to overcome these challenges and enhance the integration of AI into clinical practice.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"6 3","pages":"Pages 250-259"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148544649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}