Journal of Medical Systems最新文献

{"title":"Governing Clinical Readiness Claims Derived from Medical AI Benchmark Results.","authors":"Yin Dong, Jiwei Cheng, Chao Ding, Renjie Lu","doi":"10.1007/s10916-026-02445-7","DOIUrl":"https://doi.org/10.1007/s10916-026-02445-7","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604094","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":"A Blueprint for Agentic Workflow Support in Dynamic SPECT Myocardial Perfusion Imaging.","authors":"Enyu Yang, Teck Soon Seah, Si Yong Yeo, Yong Jin Tan, Abigail Pc Wong, Xuefen Teng, Felix Yj Keng, Ru-San Tan, Angela S Koh","doi":"10.1007/s10916-026-02444-8","DOIUrl":"https://doi.org/10.1007/s10916-026-02444-8","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549455","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":"Multi-Axial Analysis of Clinical Reasoning in Large Language Models: Inter-Verifier Disagreement and Its Implications for Automated Evaluation.","authors":"Hyunjung Byun, Dahyoun Lee, Munyoung Jung, Beakcheol Jang","doi":"10.1007/s10916-026-02440-y","DOIUrl":"https://doi.org/10.1007/s10916-026-02440-y","url":null,"abstract":"<p><p>Evaluating clinical reasoning in large language models (LLMs) poses two open challenges: reference-oriented semantic metrics do not directly assess whether a model's stated diagnosis is supported by the evidence in its own justification, and the increasingly popular LLM-as-judge approach rests on a largely untested assumption-that independent verifier LLMs agree with one another. We assess three generator LLMs (HuatuoGPT-o1-8B, Meta-Llama-3.1-8B-Instruct, Meta-Llama-3.3-70B-Instruct) on 1,000 MIMIC-IV hospital-stay cases along four complementary axes (medical concept grounding, semantic similarity, semantic uncertainty, and evidence-conclusion coherence), with coherence judged independently by three frontier verifiers (Claude Sonnet 4.6, Gemini 2.5 Pro, GPT-5.4 mini). Two findings emerge. First, coherence reveals a dissociation that reference-oriented metrics do not capture: a model can score well on those axes yet still produce rationales that do not support its own conclusions. Second, inter-verifier agreement on coherence is consistently low (Fleiss' κ 0.087-0.223; disagreement 62.2%-74.3%), so the same rationale can be judged supported or unsupported depending on the verifier. A preliminary validation in which a physician adjudicated 50 cases echoed this: agreement with the physician varied across verifiers, underscoring that no single LLM reliably stands in for clinical assessment. Together, these results suggest a single LLM verifier lacks sufficient reliability to serve as a stand-alone judge of clinical reasoning at scale, and that structured human oversight remains essential. The unanimous-agreement tier offers a candidate for selective automation, but its clinical reliability remains to be confirmed in larger, multi-clinician adjudication studies.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549451","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":"Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients' Historical Data.","authors":"Seung Jin Maeng, Ye Rim Lee, Se Uk Lee, Jae Yong Yu, Jung Won Choi, Guntak Lee, Jong Eun Park, Tae Gun Shin, Sung Yeon Hwang, Hee Yoon, Won Chul Cha, Taerim Kim, Minha Kim, Hansol Chang, Sejin Heo","doi":"10.1007/s10916-026-02443-9","DOIUrl":"10.1007/s10916-026-02443-9","url":null,"abstract":"<p><p>This study aimed to develop an interpretable machine learning-based scoring system for predicting sepsis and septic shock among febrile patients at emergency department (ED) triage using longitudinal data. This retrospective, single-center study included adult patients, presented to ED of tertiary academic hospital with fever from January 2016 to December 2021. Using the AutoScore framework, we developed a novel scoring system for predicting sepsis and septic shock at the triage stage, incorporating nine variables and a maximum score of 29. The predictive performance of our score was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and its performance was compared with that of two existing scoring systems: the quick Sequential Organ Failure Assessment (qSOFA) and the Modified Early Warning Score (MEWS). Our model incorporated nine variables including initial vital signs, age, baseline platelet count, total bilirubin, and creatinine levels. Among these, initial systolic blood pressure was identified as the most important predictor. AUROC of our model was 0.844 (95% confidence interval [CI], 0.812-0.875) in predicting septic shock and 0.703 (95% CI, 0.687-0.720) for sepsis. Compared to qSOFA ≥ 2 (AUROC: 0.605) and MEWS ≥ 5 (AUROC: 0.678), our scoring system demonstrated superior predictive performance for septic shock. For comparable specificity levels (ranging from 0.50 to 0.95), our scoring system achieved higher sensitivity than MEWS. Our scoring system is an interpretable and practical scoring tool for predicting sepsis and septic shock among patients with documented fever at ED triage using patient's longitudinal data.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13388409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549394","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":"Measuring Digital Transformation in Chinese Hospitals: Development and Validation of a Digital Maturity Evaluation Framework.","authors":"Xinyi Liu, Xiao Han, Jianjun Chen, Guanghua Zhou, Guohong Li, Xianqun Fan","doi":"10.1007/s10916-026-02438-6","DOIUrl":"10.1007/s10916-026-02438-6","url":null,"abstract":"<p><p>Digital transformation is a key priority for modernizing China's public hospitals. However, a standardized and context-specific framework to evaluate their digital maturity remains absent. This study aims to develop and validate a comprehensive, multidimensional evaluation framework tailored to Chinese tertiary public hospitals to support systematic assessment and inform policy decisions. Based on systematic literature review and policy analysis, we constructed a framework comprising Digital Readiness, Technology Application, and Data Management Capability, with 11 subdimensions and 65 indicators. Indicator weights were derived using a two-round Delphi consultation, analytic hierarchy process, and criteria importance through intercriteria dependence method. The framework was applied to 1,361 tertiary public hospitals across 28 mainland provincial-level divisions in China. Digital maturity scores were analyzed using global sensitivity analysis (GSA), k-means clustering, and logistic regression with Firth's penalized likelihood. Digital Readiness received the largest combined weight (34.9%), followed closely by Technology Application (34.7%) and Data Management Capability (30.4%), suggesting that hospital digital maturity reflects a balanced combination of organizational readiness, technology-enabled service application, and data governance. GSA further revealed discrepancies between combined weights and empirical sensitivity rankings, indicating that these approaches captured different aspects of indicator importance. Clustering and regression analyses showed that higher-maturity hospitals had higher values across many indicators, particularly in clinical digital applications and data quality management, whereas data sharing and exchange remained relatively weak across maturity groups. Robustness checks using alternative weighting and clustering methods generally supported the stability of the main findings, while also indicating residual sensitivity to methodological choices. The proposed framework provides a structure- and process-oriented diagnostic tool for assessing digital maturity in Chinese tertiary public hospitals. It can support policy monitoring, institutional benchmarking, and targeted improvement of hospital digital transformation. Future research should update the framework with more recent data and validate maturity scores against healthcare quality, safety, efficiency, patient experience, and equity outcomes.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520596","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":"Mapping Rare Disease Registries in Brazil: Situational Analysis and Proposal for National Unification.","authors":"Filipe Andrade Bernardi, Bibiana Mello de Oliveira, Natan Monsores de Sá, Domingos Alves, Têmis Maria Félix","doi":"10.1007/s10916-026-02442-w","DOIUrl":"10.1007/s10916-026-02442-w","url":null,"abstract":"<p><p>Rare disease registries in Brazil remain fragmented across federal, state, and local initiatives, limiting the availability of reliable epidemiological information to support diagnosis, care planning, research, and public policy. This study aimed to map existing rare disease registry entities and registry-related initiatives in Brazil and to propose practical guidelines for their unification into an integrated national registry. We conducted a descriptive, exploratory mapping study combining a structured literature search with documentary analysis of public policies, health information systems, registry portals, institutional reports, and legislative documents related to rare diseases in Brazil. PRISMA-S was used to report the search component, and a PRISMA-style flow diagram documented source identification and selection. We identified a rapidly evolving legislative landscape, including federal bills proposing a national monitoring system or registry and recent state-level statutes related to identification and observatories. Using an expanded, auditability-oriented inventory definition, we mapped 28 registry entities and registry-related initiatives. Of these, 24 are implemented, three are legislative proposals, and one is under development. Among the 24 implemented initiatives, 16 are national or multicentre, Brazil-based initiatives; three are state-level; four are regional/local; and one is a transnational registry with documented participation of a Brazilian cohort. Registry creation and registry-related activity accelerated after 2018, particularly between 2020 and 2026. We conclude that Brazil exhibits substantial data fragmentation across uncoordinated systems. A unified approach should integrate epidemiological data from existing networks, state notification systems, specialised hospital registries, and technology appraisal information under coordinated governance, while embedding privacy-by-design and information security safeguards.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13385086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520543","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":"Recent Advances in AI for Automated ICD Coding: A Systematic Literature Review.","authors":"Abdul Rehman Khalid, Haider Ali, Kounen Fathima, Kouayep Sonia Carole, Hee-Cheol Kim","doi":"10.1007/s10916-026-02429-7","DOIUrl":"10.1007/s10916-026-02429-7","url":null,"abstract":"<p><p>International Classification of Diseases (ICD) codes enable correct billing, insurance reimbursement, and healthcare analytics. However, manual coding is time-consuming, expensive, and error-prone, creating bottlenecks in clinical workflow and limiting scalability. Artificial intelligence (AI) has emerged as a promising solution for automated ICD code assignment from unstructured clinical text. This systematic review explores the current state of automated ICD coding research, examining models applied to diverse clinical documents including discharge summaries, electronic health records, nursing notes, and pathology reports. Following PRISMA guidelines, we searched six databases for studies published between 2019 and 2024, selecting 54 relevant studies from 4,280 initial citations. Our analysis reveals the use of diverse datasets, preprocessing techniques, and feature extraction methods, alongside a clear evolution from traditional machine learning to deep learning approaches, with substantial architectural diversity across convolutional, recurrent, transformer, and hybrid models. Performance varies considerably across dataset configurations, with models achieving higher accuracy on frequent code subsets compared to full label spaces. However, critical gaps persist: overreliance on single-language, single-institution datasets limits generalizability; difficulties in predicting rare codes remain unresolved; lack of model interpretability undermines clinical trust; and inconsistent evaluation protocols hinder meaningful comparison. To address these challenges, we propose a 5P evidence-grounded research agenda: Population Diversity, Performance Robustness, Prediction of Rare Codes, Provenance Transparency, and Practical Integration. These findings underscore AI's potential to transform ICD coding while highlighting the need for standardized benchmarks, rigorous external validation, multilingual datasets, and explainable architectures to enable equitable and effective deployment in real-world healthcare systems.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13375852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471162","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":"A Digital Cueing Intervention for Parkinsonian Gait: Laboratory-Based Clinical Validation and Acute Gait Responses.","authors":"Conor Wall, Victoria Hetherington, Rodrigo Vitorio, Peter McMeekin, Richard Walker, Jason Moore, Rosie Morris, Yunus Celik, Alan Godfrey","doi":"10.1007/s10916-026-02441-x","DOIUrl":"10.1007/s10916-026-02441-x","url":null,"abstract":"<p><p>Falls are common in people with Parkinson's (PwP). There is a need for a scalable, pragmatic, personalisable, and single device intervention to improve gait characteristics related to fall risk. CuePD is an app-only approach to deliver personalised auditory cues to retrain gait in PwP. This study aimed to clinically validate and evaluate effectiveness of CuePD in PwP and to explore their perceptions of CuePD. Sixty/60 PwP performed cued walks via metronome, instrumental and vocal music personalised to their baseline walking speed (cadence). PwP chose music from e.g., pop, rock genres. Cued walks were +10% on baseline cadence. We examined effectiveness of each cue and any carryover effects across non-cued walks. CuePD clinical validation (e.g., intraclass correlation coefficients, ICC_(2,1)) was established against a reference standard. CuePD effectiveness was examined via improvement (e.g., standardised response means) of gait characteristics indicative of fall risk (stride length, walking speed, step time coefficient of variation). CuePD quantified fall relevant mean gait characteristics against a reference system with good-to-excellent agreement (ICC_(2,1) 0.849 - 0.986), though agreement was poor for several variability and asymmetry measures. Vocal music was the most preferred cue type, improving gait characteristics by increasing stride length (7cm, SRM of 0.873) and gait speed (0.115 m/s, SRM of 1.264), while reducing stride time coefficient of variation (-0.418%, SRM of-0.505). CuePD received positive feedback and is an app-only approach that is clinically valid to quantify mean gait characteristics and has demonstrated acute improvements in gait characteristics related to fall risk. Findings suggest that the personalised approach may have pragmatic utility beyond the lab, but ecological validity needs to be established.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13356047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148421657","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":"Exploring the Potential of Ambient AI for Inpatient Documentation: A Qualitative Study with Junior Doctors.","authors":"Aisling Bracken, Sean Whelehan, Anita Rose Babu, Khalid Merghani, Eoin Sheehan, Iain Feeley","doi":"10.1007/s10916-026-02437-7","DOIUrl":"10.1007/s10916-026-02437-7","url":null,"abstract":"<p><p>Clinical documentation is essential for safe and effective patient care but places a substantial clerical burden on doctors, particularly those early in training. Ambient artificial intelligence (AI) systems, which passively capture clinical conversations and generate structured notes, have demonstrated promise in outpatient and primary care settings. However, their use in inpatient ward rounds remains largely unexplored. Semi-structured interviews were conducted with ten postgraduate year one doctors following participation in simulated orthopaedic ward rounds incorporating an ambient AI scribe (Heidi Health, Melbourne, Australia). The topic guide was informed by Normalisation Process Theory (NPT) to explore participants' understanding of AI-assisted documentation workflows, the work they anticipated would be required at individual, team, and system levels for implementation, and their appraisal of the potential benefits, risks, and practical challenges of future use. Data were analysed collaboratively by two researchers using reflexive thematic analysis with a hybrid inductive-deductive approach. Seven themes were identified and mapped across the four NPT constructs. Coherence was reflected in participants' understanding of ambient AI as a way to reduce documentation burden, support patient-centred records, and improve accessibility. Cognitive participation was evident in their recognition that implementation would require individual engagement, including review and approval of AI-generated outputs. Collective action captured the practical work required to adapt ward round behaviours, redistribute documentation tasks, manage consent, and address infrastructure, access, and governance requirements. Reflexive monitoring was demonstrated through participants' appraisal of anticipated benefits, including potential for improved clinical engagement, multidisciplinary communication, and real-time capture of complex conversations, balanced against risks of clinical misrepresentation, cultural resistance, and privacy concerns. Through simulated exposure, participants made sense of ambient AI scribes as a potentially valuable addition to inpatient documentation workflows, anticipating effects on efficiency, communication, and patient-centred documentation. However, they also identified individual, team, and organisational work required for implementation, including workflow adaptation, consent processes, data privacy, access controls, infrastructure, and real-world appraisal in clinical settings.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13342209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148404903","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":"Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance.","authors":"Wanlin Chen, Xinhui He, Jing Zheng, Shulin Chen, Xiang Tian","doi":"10.1007/s10916-026-02435-9","DOIUrl":"10.1007/s10916-026-02435-9","url":null,"abstract":"<p><p>Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medical value for scalable sleep health management. This systematic review synthesizes methodological frameworks and performance outcomes of automatic sleep staging using cardiorespiratory signals. Four databases were searched and a total of 35 studies published since 2010 were identified. The analysis revealed that cardiorespiratory signal-based sleep staging achieved a practically meaningful accuracy of 70%, with no significant performance differences observed among signal modalities (cardiac signals, cardiorespiratory signals, or cardiac/cardiorespiratory signals combined with other non-EEG modalities) or between modeling algorithms (traditional machine learning vs. deep learning). However, we identified significant methodological heterogeneity and several critical model failure modes that hinder clinical translation, including the widespread lack of external validation, consistently poor classification of the N1 sleep stage, and limited generalization across diverse patient populations. To realize the technology's potential, future research must establish consensus-driven methodological guidelines and rigorously validate algorithms on large, demographically and clinically diverse datasets. These advances are essential for integrating cardiorespiratory-based sleep staging into healthcare systems as a scalable tool for population-level screening, longitudinal monitoring, and tiered clinical decision support.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"50 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148396888","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}