Journal of Medical Systems最新文献

{"title":"Security Analysis of a Federated Learning Framework for Medical Image-to-Image Translation.","authors":"Ciro Benito Raggio, Lina Bucher, Oliver Blanck, Francesco Cicone, Paolo Zaffino, Maria Francesca Spadea","doi":"10.1007/s10916-026-02436-8","DOIUrl":"10.1007/s10916-026-02436-8","url":null,"abstract":"<p><p>Federated Learning (FL) emerged as a privacy-preserving paradigm for collaborative training of deep learning models across institutions without sharing patient data. This approach has been applied to complex tasks such as medical image-to-image (I2I) translation, including MRI-to-synthetic CT (sCT) generation. However, existing federated I2I frameworks often assume privacy preservation as an inherent property of FL rather than a requirement to be explicitly validated, leaving their robustness to representative adversarial threat scenarios largely unexplored. In this study, we evaluated the vulnerability of a federated MRI-to-sCT translation framework (FedSynthCT-Brain) to three representative attack classes: Deep Leakage from Gradients (DLG), Federated Membership Inference Attack (FedMIA), and data poisoning. The efficacy of corresponding defense mechanisms, such as Secure Aggregation (SecAgg) and Byzantine-robust median aggregation (FedMedian), were assessed. DLG enabled only the recovery of coarse anatomical structures, with no clinically identifiable details (SSIM ≤ 0.16, PSNR ≤ 11 dB) across clients, suggesting limited vulnerability under the evaluated DLG setting. In contrast, FedMIA achieved high membership discrimination, with AUC scores between 0.92 and 0.99, revealing a critical privacy vulnerability. The introduction of SecAgg reduced AUC values to near-random levels (0.23-0.56) across all centers without impacting synthesis quality. Under high-noise poisoning, the standard federated averaging (FedAvg) aggregation rendered the federation inoperative, while FedMedian restored performance close to the no-poisoning baseline in most scenarios, with significant residual degradation in specific center configurations. At low noise levels, the advantage of FedMedian was less consistent, as low-level noise injection may be indistinguishable from natural heterogeneity across centers, potentially enabling stealthy degradation. These findings demonstrate that federated I2I translation frameworks are not inherently secure and require explicit, multi-layered evaluation. As FL is increasingly adopted in clinical workflows, our results underscore the necessity of integrating cryptographic, algorithmic, and infrastructural safeguards for secure deployment.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13332882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Designing Operating Rooms as an Integrated Socio-Technical Ecosystem: Practical Lessons from a High-Volume Tertiary Center.","authors":"Dina Orkin, Ofir Morag, Hadas Moshonov-Cohavi, Tania Fishman, Gad Segal","doi":"10.1007/s10916-026-02439-5","DOIUrl":"https://doi.org/10.1007/s10916-026-02439-5","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-enabled clinical decision support in breast cancer care: a blinded multicenter benchmarking study comparing medically specialized with a general-purpose system.","authors":"Jonas Freudenberg, Johannes Knitza, Niklas Gremke, Niklas Amann, Thomas M Deutsch, Nikolas Tauber, Kerstin Muras, Zoe S Oftring, Tobias Engler, Alexander Englisch, Stefan Lukac, Adriano Fabi, André S Alves, Kristin Reinhardt, Markus Wallwiener, Moritz Kuhlmann, Jonathan Bamberger, Uwe Wagner, Sebastian Kuhn, Sebastian Griewing","doi":"10.1007/s10916-026-02434-w","DOIUrl":"10.1007/s10916-026-02434-w","url":null,"abstract":"<p><p>Medically specialized AI systems that have obtained regulatory clearance as medical devices can be deployed for patient-specific clinical decision support under defined compliance requirements. However, it remains unclear whether medical specialization and regulatory status translate into higher-quality breast cancer treatment recommendations than those produced by a general-purpose large language model (LLM). This blinded, multicenter study compared the performance of two medically specialized AI systems with a general-purpose model in breast cancer care. Two medically specialized (Prof. Valmed and OpenEvidence) and one general-purpose system (ChatGPT-5 Thinking) were prompted to generate treatment plans for 20 standardized breast cancer patient cases. Outputs were rated and ranked by blinded, board-certified breast cancer specialists from seven university breast cancer centers for safety, guideline adherence, medical adequacy, completeness, overall quality, and logical coherence. Statistical analyses comprised descriptive statistics, inter-rater reliability assessment, non-parametric performance comparisons of rating and ranking outcomes, and correlation analyses. Mean (± standard deviation) processing time for ChatGPT-5 Thinking (159 ± 58 s) was more than fourfold higher than that of Prof. Valmed (35 ± 4) and OpenEvidence (9 ± 1). ChatGPT-5 Thinking achieved significantly higher ratings across all evaluation categories, with no significant differences between the two medically specialized systems. Treatment plans generated by ChatGPT-5 Thinking were ranked as the top choice in 96.4% of rater-case combinations, compared with 3.6% for OpenEvidence, while Prof. Valmed was never ranked first. In this blinded, multicenter evaluation, a general-purpose LLM outperformed two medically specialized, retrieval-augmented systems in generating breast cancer treatment plans across all assessed categories. These results indicate that while regulatory clearance and domain specialization address key requirements for AI-enabled clinical decision support systems, these factors alone do not translate into superior performance in breast cancer care. At present, medically specialized systems may be best used as supportive tools under expert oversight, while further optimization and real-world validation are needed.Clinical trial number: Not applicable.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13332881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.","authors":"Zhifan Li, Xiaoxia Liu, Tianhao Chen, Yuting Yang, Xiaoyan Liu, Yuanyuan Lv, Zixuan Zhao, Xueying Li, Xiaoqing Yin, Zhongwen Feng, Yue Lan, Yanjie Zhao, Wei Ke, Yong Lin, Kefeng Li","doi":"10.1007/s10916-026-02433-x","DOIUrl":"https://doi.org/10.1007/s10916-026-02433-x","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) affects tens of millions of families worldwide, yet parents confront abundant but unreliable online advice and limited access to timely, empathetic guidance. To address this critical gap, we developed Starmate ( http://kefeng.mpu.edu.mo/starmate ), a 1.5B-parameter, domain-tuned AI assistant for ASD caregivers, using a rigorous user-centered mixed-methods framework. Informed by in-depth interviews ([Formula: see text]) and a Kano survey ([Formula: see text]) that identified \"Hands-on guidance\" as a must-have caregiver requirement, we engineered a novel modular architecture that integrates sentiment analysis, expert-vetted knowledge-graph-augmented retrieval (LightRAG), and a domain-fine-tuned Qwen2.5-1.5B model. In a blinded, side-by-side comparison against leading commercial LLMs, Starmate demonstrated improved performance across key metrics within this evaluation framework (86.76 vs 78.43-83.84; [Formula: see text]) and showed specific advantages in Empathy, Hands-on guidance, and Logical clarity (all [Formula: see text]). Automated benchmarking corroborated these results, with top scores for Professional accuracy (86.18), Empathy (86.79), and Hands-on guidance (82.58). These findings demonstrate the technical feasibility of a lightweight, privacy-conscious, domain-specific LLM to generate accurate, empathetic, and actionable responses in benchmarked scenarios, laying the groundwork for future real-world usability and clinical testing.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148368751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the Predictor: Unresolved Validity Threats in LLM-Based ASA Classification.","authors":"Kaijian Yang","doi":"10.1007/s10916-026-02431-z","DOIUrl":"https://doi.org/10.1007/s10916-026-02431-z","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148361092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Internal Validation of a Vectorcardiography-Augmented Model for 12-Month Major Adverse Cardiovascular Events in Chronic Heart Failure.","authors":"Kaiyuan Cen, Zhuoqiao He, Hong Chen, Yi Tan, Lixia Lin, Xiaojuan Xu","doi":"10.1007/s10916-026-02432-y","DOIUrl":"10.1007/s10916-026-02432-y","url":null,"abstract":"<p><p>To develop and internally validate a vectorcardiography (VCG)-augmented model for estimating 12-month major adverse cardiovascular events (MACE) in hospitalized patients with chronic heart failure (CHF). We conducted a single-centre retrospective cohort study of adults hospitalized with CHF between 31 May 2023 and 31 May 2024, with data lock on 31 May 2025. The prespecified endpoint was any MACE within 12 months after the index hospitalization. ECG-to-VCG transformation was performed using the Kors method. The prespecified primary six-predictor model included LVEDD, NYHA class, frontal, horizontal, and sagittal QRS-T angles, and the QRS-loop reversal/U-turn sign. BNP and LVEF were evaluated in full-model, comparator-model, and incremental-value analyses. Because the prediction target was fixed 12-month risk rather than time-to-event hazard, multivariable logistic regression was used as the primary modelling approach. Internal validation was performed using 1000 bootstrap resamples, with apparent, optimism-corrected, calibration, and shrinkage-adjusted performance reported. Of 201 screened, 160 were included; MACE occurred in 68/160 (42.5%). The six-predictor model showed an apparent AUC of 0.946 (95% CI 0.914-0.978). Bootstrap internal validation yielded an AUC optimism estimate of 0.012 and an optimism-corrected AUC of 0.934. The apparent Brier score was 0.091, the apparent calibration slope was 1.000, and the apparent calibration intercept was 0.000. After bootstrap correction, the Brier score was 0.106, calibration-in-the-large was - 0.009, calibration intercept was - 0.010, calibration slope was 0.852, and the applied uniform shrinkage factor was 0.852. In formal incremental analyses, adding VCG features to a conventional base model improved AUC from 0.890 to 0.955 (ΔAUC 0.065; DeLong P = 0.001), with a lower Brier score and favourable discrimination/reclassification indices. A VCG-augmented model incorporating LVEDD, NYHA class, spatial QRS-T angles, and the QRS-loop reversal/U-turn sign showed promising internally validated performance for estimating 12-month MACE risk in hospitalized patients with CHF. External multicentre validation is required before clinical implementation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148352341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibration of Self-Reported Confidence and Accuracy of Large Language Models in Medical Question Answering.","authors":"Sebastian Daniel Boie, Florian Reis, Nicolas Frey, Elias Grünewald, Felix Balzer","doi":"10.1007/s10916-026-02430-0","DOIUrl":"10.1007/s10916-026-02430-0","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the alignment between self-reported confidence of large language models (LLM) and their accuracy in answering medical multiple-choice questions.</p><p><strong>Materials and methods: </strong>We prompted six LLMs (GPT-5, GPT-5-mini, GPT-5-nano, GPT-4o, Claude Sonnet 4.5, and Gemini 2.5) to answer MedMCQA items and report confidence scores. Based on 12,000 LLM responses, we calculated Expected Calibration Errors (ECE) by averaging absolute differences between observed accuracy and predicted confidence.</p><p><strong>Results: </strong>Mean ECE differed by model (Claude Sonnet 4.5 best: 0.06; Gpt-4o worst: 0.127) and varied across specialties (\"Skin\" best: 0.041; \"Social & Preventive Medicine\" worst: 0.141). Accuracy of examined LLMs showed analogous variation between specialties.</p><p><strong>Discussion: </strong>Our results demonstrate that high accuracy does not guarantee reliable uncertainty estimation. We identified substantial heterogeneity across medical specialties, where pooled metrics masked a threefold ECE increase between best- and worst-performing domains.</p><p><strong>Conclusion: </strong>We recommend incorporating calibration reporting into LLM evaluations, as larger models exhibit improved \"self-knowledge\", but uneven overconfidence persists.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148338971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of an Automated Acute Kidney Injury E-Alert System Integrated with Clinical Decision Support for Hospitalized Patients.","authors":"Chien-Hao Su, Chien-Ning Hsu","doi":"10.1007/s10916-026-02428-8","DOIUrl":"10.1007/s10916-026-02428-8","url":null,"abstract":"<p><p>Automated electronic alerts integrated into inpatient electronic health records (EHRs) may improve recognition of moderate-to-severe acute kidney injury (AKI) and support timely medication review. An AKI e-alert system integrated with medication-focused clinical decision support (CDS) was implemented to identify adults with KDIGO stage 2-3 hospital-acquired AKI in near real time while minimizing alert burden. The alert logic used KDIGO serum creatinine (SCr) criteria, defined the operational real-time inpatient baseline as the lowest SCr value within the preceding seven days, and applied suppression rules for dialysis within seven days or imminent transfer/discharge. Alerts were generated through scheduled near-real-time processing four times daily and were accompanied by secure in-hospital medication guidance; the CDS did not automatically place orders. System performance was evaluated at a 2,768-bed tertiary medical center in Taiwan using EHR data from March 2018 to May 2023 and validated against a retrospective computerized reference algorithm designed to replicate the deployed logic. The system generated 3,946 stage 2-3 AKI alerts, achieving 90.94% sensitivity and 99.65% accuracy compared with the reference algorithm. Seasonal alert rates were highest in winter (4.48%) and lowest in summer (3.17%), supporting the temporal face validity of the detection approach. In a hospital-wide physician survey (n = 78), 63.0% agreed that the medication guidance accompanying alerts was clinically helpful. An EHR-integrated e-alert targeting advanced AKI can achieve high real-world performance, and linking alerts to targeted medication CDS may facilitate nephrotoxin stewardship and medication safety while limiting alert fatigue.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148338929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Throughput Benchmarking and Throughput Variance Analysis to Evaluate the Efficiency of an Outpatient Endoscopy Unit.","authors":"Xing Li, Eli Patt, Madeline Koehn, Michael Hu, Ana Cecilia Zenteno Langle, Hrudya Viswanadh, Erin Stewart, Duncan J Flynn, Amandeep Singh","doi":"10.1007/s10916-026-02427-9","DOIUrl":"10.1007/s10916-026-02427-9","url":null,"abstract":"<p><p>Rising demand for colorectal cancer screening has increased colonoscopy volume, producing backlogs and prolonged wait times. Commonly used metrics such as room utilization provide limited insight into actionable drivers of efficiency; we applied a novel throughput benchmarking framework to quantify performance and identify operational sources of inefficiency. We conducted a pilot study in a four-room outpatient endoscopy unit from July-December 2022. Using routinely recorded timestamps for procedural stages, we derived a realistic daily target throughput (RDTT). Monthly realistic target throughput (RMTT) was calculated from RDTT. Actual monthly throughput (AMT) was benchmarked against RMTT to generate a throughput efficiency ratio (TER = AMT/RMTT) and throughput variance (TV = RMTT - AMT), which was decomposed into seven predefined operational factors. AMT ranged from 567 to 717 procedures per month, whereas RMTT ranged from 1,165 to 1,332, yielding a mean TER of 53%. The largest contributors to variance were rooms closed due to lack of endoscopist assignment (45%), workflow delays (21%), and patient no-shows or late cancellations (11%). In this pilot, we demonstrate that using commonly captured operational metrics, throughput benchmarking provides a practical framework to quantify efficiency and identify specific, actionable drivers of underperformance in endoscopy operations.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148308480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MTA-Swin: A Multi-Token Attention Swin Transformer for Brain Tumor Classification with Leakage-Free MRI Benchmarking.","authors":"Dong Lu, Yu Zhang, Divya Chaudhary","doi":"10.1007/s10916-026-02418-w","DOIUrl":"10.1007/s10916-026-02418-w","url":null,"abstract":"<p><p>Brain tumors represent a major global health challenge, and accurate classification of brain tumors is essential for effective diagnosis and treatment. Magnetic resonance imaging (MRI) is the most commonly used and reliable modality in early brain tumor detection, and numerous studies have leveraged MRI datasets to train deep learning models for classification. However, many widely adopted brain tumor MRI datasets suffer from duplicate-induced data leakage, which can lead to artificially inflated performance metrics and unreliable model evaluation. In this study, we systematically analyze this issue and develop an automated data cleaning pipeline capable of identifying and removing duplicate scans. By applying this pipeline to a widely used public dataset, we obtain a leakage-free benchmark dataset containing 3,522 unique MRI scans, which is used for all comparative experiments. Additionally, we propose MTA-Swin, an enhanced Swin Transformer that incorporates Multi-Token Attention by re-designing the attention computation within Swin blocks. The design refines attention logits to enrich local context and enables explicit cross-head information exchange in deeper layers, while preserving Swin's hierarchical stages and windowing scheme. MTA-Swin is first pre-trained on ImageNet-1K, and then fine-tuned on our leakage-free dataset. Experimental results show that MTA-Swin reaches an overall accuracy of 98.57% over three random seeds, outperforming thirteen representative baselines. Additional stratified cross-validation and Grad-CAM analyses further support the robustness and interpretability of the proposed model. These results indicate that MTA-Swin can serve as a practical computer-aided diagnostic support model for brain tumor MRI classification.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148295465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}