JMIR Medical Informatics最新文献

{"title":"A Data-Centric Approach for Health Care and Research in a Health Knowledge Management Platform: Implementation and Requirement-Based Evaluation Study.","authors":"Björn Schreiweis, Benjamin Kinast, Hannes Ulrich, Tobias Bronsch, Ann-Kristin Kock-Schoppenhauer, Björn Bergh","doi":"10.2196/83608","DOIUrl":"https://doi.org/10.2196/83608","url":null,"abstract":"<p><strong>Background: </strong>In the evolving landscape of health care, data use plays an ever-increasing role in health care IT. However, data are often siloed and uncoded free text distributed across several IT systems. This paper introduces a health knowledge management platform, designed to integrate, harmonize, and enable reuse of health care and medical research data. The platform aims to bridge the gap between research and patient care, showcased through real-world scenarios, emphasizing data harmonization and knowledge management within a health care institution. The study is based at the University Hospital Schleswig-Holstein.</p><p><strong>Objective: </strong>The main objective of this project is to design, implement, and evaluate a knowledge management platform that integrates health care and biomedical research to support use cases in both domains.</p><p><strong>Methods: </strong>The study describes the \"health knowledge management platform\" designed to access and gain knowledge from health care and medical research data. We performed several rounds of focus groups with stakeholders to elicit the platform requirements. In the process, we identified key aspects of the platform. From the functional requirements, we designed an architectural concept. The platform evaluation follows the Framework for Evaluation in Design Science Research and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 standard with a focus on key aspects identified and real-world scenarios. Two application scenarios, cardiology and radiology, are selected for a requirement-based, qualitative evaluation.</p><p><strong>Results: </strong>We show that our health knowledge management platform is capable of integrating diverse data formats like Health Level 7 Version 2 messages, CSV exports, and Digital Imaging and Communications in Medicine. It currently integrates over 46 million admit, discharge, transfer messages, 38 million imaging studies, and structured clinical data for approximately 1.5 million patients. The platform supports different scenarios based on its 5-layer architecture, including a clinical data repository and services like Master Patient Index and Consent Management. The evaluation against 39 predefined functional requirements showed our platform's capability in certain real-world scenarios of cardiology and radiology. Our evaluation demonstrates that the platform covers the majority of the identified requirements to support knowledge management in health care institutions.</p><p><strong>Conclusions: </strong>Our requirement-based evaluation of the health knowledge management platform at University Hospital Schleswig-Holstein reveals its capabilities, which is possibly leading to better knowledge transfer between patient care and research. The platform's architecture and standardized data improve the quality of data and facilitate access to knowledge. Ongoing development and potential quantitativ","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e83608"},"PeriodicalIF":3.8,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824047","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":"Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.","authors":"Hui Li, Jing Chen, Peijun Lin, Youmei Pan, Yawen Cao, Wenfeng Xie","doi":"10.2196/79985","DOIUrl":"https://doi.org/10.2196/79985","url":null,"abstract":"<p><strong>Background: </strong>Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF.</p><p><strong>Objective: </strong>This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models.</p><p><strong>Methods: </strong>This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis.</p><p><strong>Results: </strong>The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells.</p><p><strong>Conclusions: </strong>This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e79985"},"PeriodicalIF":3.8,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824091","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":"Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study.","authors":"Anoeska Schipper, Peter Belgers, Rory David O'Connor, Lieke van de Wouw, Luc Builtjes, Joeran S Bosma, Ron Kusters, Steef Kurstjens, Matthieu Rutten, Bram van Ginneken","doi":"10.2196/81500","DOIUrl":"10.2196/81500","url":null,"abstract":"<p><strong>Background: </strong>Most clinically relevant information in emergency department (ED) visits is documented in free text, limiting reuse for research and clinical decision support. Despite growing interest in large language model (LLM)-based feature extraction, very few studies have examined it directly on ED reports. Existing work has mainly addressed binary tasks and rarely evaluated their impact on downstream prediction models. Furthermore, evidence for small multilingual LLMs remains limited, especially for underrepresented languages such as Dutch. Locally deployable LLMs could enable automated feature extraction for decision support systems without increasing physician workload.</p><p><strong>Objective: </strong>We aim to evaluate whether a small open-source LLM (Qwen 2.5:14B) can automatically extract 16 clinical signs and symptoms from ED reports and use these as input for an appendicitis prediction model. LLM performance under minimal and optimized 0-shot prompts was assessed against researcher annotations (reference standard) and physician annotations.</p><p><strong>Methods: </strong>This retrospective study used 336 ED reports from patients presenting with acute abdominal pain to a Dutch teaching hospital (2016-2023). One hundred reports were randomly selected to develop a minimal and an optimized 0-shot prompt strategy. The remaining 236 reports, reserved for evaluation, were annotated by 2 ED physicians and processed by the LLM to extract 16 signs and symptoms, covering binary, multiclass, and multilabel classification tasks. These features were used as input to the HIVE (History, Intake, Vitals, Examination) appendicitis prediction model. LLM extraction accuracy, sensitivity, and specificity were measured against the researcher's (reference standard) and physician annotations. The HIVE model's area under the receiver operating characteristic curve was evaluated using LLM-extracted vs physician-annotated features.</p><p><strong>Results: </strong>Among 336 ED reports from patients with acute abdominal pain (median age 41, IQR 22-62 years, 205/336, 61% female), 50% (167/336) had appendicitis. The LLM achieved weighted average accuracies of 0.910 (95% CI (0.018) with minimal prompts and 0.929 (95% CI ±0.016) with optimized prompts, vs 0.961 (95% CI ±0.012) and 0.951 (95% CI ±0.015) for physicians. Corresponding HIVE model area under the receiver operating characteristic curves were 0.871 (95% CI ±0.019) and 0.911 (95% CI ±0.014) with LLM inputs under the minimal and optimized prompts, compared to 0.917 (95% CI ±0.015) and 0.924 (95% CI ±0.018) for physician inputs.</p><p><strong>Conclusions: </strong>A small locally deployable multilingual LLM can approach physician-level accuracy in extracting structured binary, multiclass, and multilabel clinical data from free-text Dutch ED reports, while preserving patient privacy, interpretability, and statistical transparency for downstream diagnostic modeling.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e81500"},"PeriodicalIF":3.8,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824102","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":"Detection of Interpretable and Fine-Grained Brain Tumor Magnetic Resonance Imaging Based on Progressive Pruning: Machine Learning Model Development and Validation Study.","authors":"Yupeng Liu, Shuwei Song, Shibo Lian, Xiaochen Zhang","doi":"10.2196/84095","DOIUrl":"10.2196/84095","url":null,"abstract":"<p><strong>Background: </strong>Brain tumor is one of the most malignant diseases of the central nervous system, and early accurate detection is of great significance for improving patient survival rate. However, the heterogeneity of brain tumors in terms of morphology, size, and location on magnetic resonance imaging (MRI) image, as well as their similarity to surrounding normal brain tissue, poses significant challenges for tumor detection.</p><p><strong>Objective: </strong>This study aims to develop a high-performance brain tumor detection framework that integrates feature enhancement, channel attention, and progressive pruning, achieving an optimal balance between detection accuracy, model efficiency, and interpretability for slice-level MRI tumor localization tasks.</p><p><strong>Methods: </strong>This paper proposes a convolution Prewitt-and-pooling-based preprocessing (CSPP) approach, based on the \"you only look once\" version 11 (YOLOv11) framework, which highlights important structural detail more effectively than traditional statistics. A dynamic convolution-based C3k2 (DCC) module was integrated to more efficiently capture both local and global features. A channel prior convolutional attention (CPCA) module was introduced before the detection head, enabling the network to specifically focus on information-rich channels and key spatial regions. Through a progressive hybrid pruning strategy (PHPS), the model was optimized for efficient inference. Furthermore, Eigen-class activation mapping (Eigen-CAM) was used to interpret the prediction result, making them more transparent.</p><p><strong>Results: </strong>Extensive experiments on 3 brain tumor MRI datasets demonstrated the superior performance of CDCP-YOLO (CSPP-DCC-CPCA-PHPS-YOLO). On Br35H, the mean average precision (mAP) at an intersection-over-union (IoU) threshold of 0.5 (mAP0.5) increased by 2.6%, average mAP over several IoU thresholds (0.50-0.95; mAP0.5:0.95) increased by 5.9%, and number of floating-point operations (×10⁹; GFLOPs) decreased by 47.7%. On Roboflow, mAP0.5 increased by 19.5%, mAP0.5:0.95 increased by 7.7%, and GFLOPs decreased by 47.7%. On Capstone, mAP0.5 increased by 6.9%, mAP0.5:0.95 increased by 5.8%, and GFLOPs decreased by 47.7%.</p><p><strong>Conclusions: </strong>The proposed CDCP-YOLO framework achieves an optimal balance between accuracy, efficiency, and interpretability, providing a lightweight and reliable solution for slice-level brain tumor detection in MRI images.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e84095"},"PeriodicalIF":3.8,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790257","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":"Evaluation of AI Chatbot Responses to a Standardized Patient Query on Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease: Cross-Sectional Content Analysis.","authors":"Meryem Tuba Sönmez, Mehmet Fatih Yetkin, Duygu Arslan Mehdiyev, Nazlı Durmaz Çelik, Merve Bahar Ercan, Pınar Öztürk, Yeşim Eylev Akboğa, Emine Rabia Koç, Semra Mungan","doi":"10.2196/81720","DOIUrl":"10.2196/81720","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Large language model-based chatbots are increasingly used by the public to access medical information. Although these tools can improve access and convenience, their quality, clarity, and transparency remain uncertain for rare and diagnostically complex neurological conditions, such as myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the scientific quality, understandability, citation transparency, and readability of responses generated by widely used artificial intelligence chatbot platforms to a standardized, patient-centered query on MOGAD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a cross-sectional content analysis using the query, \"What is MOGAD, and how is MOGAD treated?\" Ten widely accessible chatbot platforms were queried once on the same day in new sessions. Responses were anonymized and independently evaluated by 7 blinded neurologists using DISCERN (treatment-related scientific quality), Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P), and the Web Resource Rating (WRR; citation transparency). Readability was assessed using the Flesch-Kincaid Grade Level (FKGL) and Coleman-Liau Index, and word count was recorded. Platforms were compared by functional orientation and the access model. Mann-Whitney U and Kruskal-Wallis tests with Dunn post hoc tests were used. Interrater reliability was assessed using intraclass correlation coefficients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Significant differences were observed across platforms for DISCERN, PEMAT-P, and WRR scores (all P&lt;.001). Search-focused platforms achieved higher understandability than conversation-focused platforms (median PEMAT-P 52.6, IQR 47.4-54 vs 46.7, IQR 42-47.3; P=.04), whereas conversation-focused platforms had higher WRR scores (median 26.8, IQR 19.6-26.8 vs 19.6, IQR 19.6-25.9; P=.001). DISCERN scores did not differ significantly by functional orientation (P=.11). Paid-access platforms outperformed free-access platforms in DISCERN (median 42, IQR 36-45 vs 33, IQR 23.8-41.3; P&lt;.001), PEMAT-P (median 52.6, IQR 46-54 vs 46, IQR 26.3-47.4; P=.002), and WRR (median 26.8, IQR 23.2-26.8 vs 10.7, IQR 3.57-19.6; P&lt;.001). However, no statistically significant differences were observed between paid and free platforms in response length (median word count 336, IQR 271-369 vs 206, IQR 116-294; P=.11) or readability metrics. FKGL scores were comparable between paid and free outputs (median 17.54, IQR 16.6-18.4 vs 17.56, IQR 16.5-17.6; P=.61), and Coleman-Liau Index values similarly showed no significant difference by access model (median 21.30, IQR 20.6-22.3 vs 21.71, IQR 20.9-22.1; P=.91). Readability remained limited: all outputs exceeded recommended public health readability thresholds (FKGL≥8). High interrater agreement was observed (intraclass correlation coefficient=0.902 for DISCERN, 0.887 for WRR, and 0.838 for PEMAT-P).&lt;/p&gt;&lt;p&gt;&lt;stron","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e81720"},"PeriodicalIF":3.8,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790338","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":"Retraction: Optimization of Precontrol Methods and Analysis of a Dynamic Model for Brucellosis: Model Development and Validation.","authors":"","doi":"10.2196/98667","DOIUrl":"https://doi.org/10.2196/98667","url":null,"abstract":"","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e98667"},"PeriodicalIF":3.8,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790402","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":"Retraction: Application of Intelligent Computer-Assisted Taylor 3D External Fixation in the Treatment of Tibiofibular Fracture: Retrospective Case Study.","authors":"","doi":"10.2196/98674","DOIUrl":"https://doi.org/10.2196/98674","url":null,"abstract":"","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e98674"},"PeriodicalIF":3.8,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824129","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":"Retraction: Application of an Isolated Word Speech Recognition System in the Field of Mental Health Consultation: Development and Usability Study.","authors":"","doi":"10.2196/98661","DOIUrl":"https://doi.org/10.2196/98661","url":null,"abstract":"","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e98661"},"PeriodicalIF":3.8,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824148","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":"Data-Efficient Language Model for Assessing Pulmonary Embolism Diagnostic Certainty From Radiology Reports: Model Development and Validation Study.","authors":"Feifan Liu, Ruofan Hu, Donghyuk Kim, Hao Lo, Yevgeniy Kharonov, Ansh Johri, Ben S Gerber, Elke Rundensteiner, Lauren M Westafer, Jeroan Allison, Catarina Kiefe, Alexander Bankier, Max P Rosen","doi":"10.2196/79972","DOIUrl":"https://doi.org/10.2196/79972","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Computed tomography pulmonary angiography (CTPA) is the standard imaging modality for diagnosing pulmonary embolism (PE), but diagnostic uncertainty is common due to technical limitations and vague language, leading to inconsistent interpretation and clinician frustration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study develops a prompt-free, data-efficient method for assessing diagnostic certainty of PE in CTPA reports using small pretrained language models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study examined 173 consecutive CTPA reports from UMass Memorial Health, each annotated by 3 radiologists for PE diagnostic certainty. We developed PECertainty, a lightweight, prompt-free model, and compared it with advanced large language model (LLM)-based methods under limited supervision settings. Baselines included prompt-free methods (support vector machine, random forest, and RoBERTa [Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach]) and prompt-dependent methods (LLM fine-tuning, in-context learning, and ADAPET [A Densely-Supervised Approach to Pattern Exploiting Training]; UNC Chapel Hill) with open-source Gemma3-4B (Google DeepMind) and Llama3.2-3B (Meta), and the proprietary GPT-3.5 (OpenAI). Sensitivity analyses evaluated performance with 1 to 10 training examples per category for the top performer. Model performance was evaluated against radiologist annotations. External validation on 420 CTPA reports from the Baystate Medical Center, with validation limited to distinguishing certain from uncertain reports. Interpretability of the top-performing models (PECertainty and GPT-3.5) was evaluated using integrated gradients and prompt-based explanations reviewed by radiologists.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among prompt-dependent methods, GPT-3.5 fine-tuning (F1-score 0.86; 95% CI 0.71-1.0) and in-context learning (F1-score 0.87; 95% CI 0.71-1.0) performed best, and the performance of in-context learning consistently outperformed 0-shot learning for Gemma3-4B (F1-score 0.63, 95% CI 0.56-0.79 vs F1-score 0.45; 95% CI 0.29-0.56) and Llama3.2-3B (F1-score 0.54; 95% CI 0.41-0.71 vs F1-score 0.43, 95% CI 0.28-0.62). PECertainty demonstrated numerically better or equivalent performance compared with both the top-performing prompt-dependent methods and all prompt-free baselines. Compared with fine-tuned ClinicalBERT (Bidirectional Encoder Representations From Transformers Pretrained on Clinical Text), PECertainty achieved statistically significant improvements across all metrics (paired bootstrap significance test, P&lt;.05). RoBERTa (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach) fine-tuning lagged (F1-score 0.52; 95% CI 0.35-0.71), and simple models such as support vector machine underperformed. In few-shot settings (10 examples/category), PECertainty (F1-score 0.80; 95% CI 0.59-0.94) outperformed both GPT-3.5 fine-tuning (F1-scor","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e79972"},"PeriodicalIF":3.8,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13123884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790259","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":"Retraction: An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysi.","authors":"","doi":"10.2196/98670","DOIUrl":"https://doi.org/10.2196/98670","url":null,"abstract":"","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"14 ","pages":"e98670"},"PeriodicalIF":3.8,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790323","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}