Journal of Medical Systems最新文献

{"title":"Unveiling Bias in Distilled Artificial Intelligence Models: Ethical and Clinical Impacts on Decision-Making and Medical Auditing.","authors":"Gerson Hiroshi Yoshinari Júnior, Luciano Magalhães Vitorino","doi":"10.1007/s10916-025-02230-y","DOIUrl":"https://doi.org/10.1007/s10916-025-02230-y","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"95"},"PeriodicalIF":3.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584152","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":"Evaluating the Performance of ChatGPT on Board-Style Examination Questions in Ophthalmology: A Meta-Analysis.","authors":"Jiawen Wei, Xiaoyan Wang, Mingxue Huang, Yanwu Xu, Weihua Yang","doi":"10.1007/s10916-025-02227-7","DOIUrl":"10.1007/s10916-025-02227-7","url":null,"abstract":"<p><p>To review empirical research on ChatGPT's accuracy in answering ophthalmology board-style examination questions up to March 2025 and to analyze the effects of GPT versions, question types, language differences, and ophthalmology topics on accuracy. A search was conducted in PubMed, Web of Science, Embase, Scopus, and the Cochrane Library in March 2025. Two authors extracted data and independently assessed study quality. Accuracy rates were calculated with Stata 17.0. GPT-4 had an integrated accuracy of 73%, higher than GPT-3.5's 54%. It scored 77% in text and 55% in image tasks. GPT-4's accuracy was 73% in English-speaking countries and 71% in non-English ones. In ophthalmology, General Medicine achieved the highest accuracy (80%), while Clinical Optics had the lowest performance (55%). GPT-4 outperforms GPT-3.5, but its image processing capability needs further validation. Performance varies by language and topic, suggesting the need for more research on cross-linguistic efficacy and error analysis.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"94"},"PeriodicalIF":3.5,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564863","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 Simulation-Based Approach for Inpatient Capacity Management at a Hospital Dedicated for Cancer Treatment.","authors":"Anup C Mokashi, Ginger J Gardner, Adam D Klotz, Jacquelyn J Burns, Jeena L Velzen","doi":"10.1007/s10916-025-02206-y","DOIUrl":"10.1007/s10916-025-02206-y","url":null,"abstract":"<p><p>This paper describes the development and application of an analytical solution to assist with inpatient flow and capacity management at Memorial Sloan Kettering Cancer Center (MSKCC) in New York City. We present a discrete-event simulation model that captures several key aspects of the complex patient flow patterns at MSKCC in the inpatient setting. The model captures the variation in admission patterns based on various patient cohorts and admit locations. The model also accounts for the variability in specialized care needs for distinct patient cohorts using categorical distributions. Durations for various patient flow states from admission till discharge are modeled as probability distributions. Key patient-and resource attributes are also incorporated to accurately capture the constraints affecting resource allocation. A comprehensive set of output metrics is used to validate the model, and to compare alternative scenarios. We present results for a scenario that tests the impact of resource allocation changes aimed at consolidating patients on certain floors based on the hospital department tasked with their inpatient care. Outputs for the scenario are compared with baseline using the following output metrics: mean bed utilization by floor, mean admit boarding times by service, proportion of home floor admissions by service, and wait times for step-down care beds. Our results show an estimated reduction in average admit wait times by 30 minutes or more across 4 inpatient services (an annual reduction of <math><mo>∼</mo></math> 116 days), with a neutral impact on other output metrics. The analysis from the scenario was utilized by hospital leadership to implement actual bed allocation changes in the hospital. The model demonstrates a structured analytical approach to evaluate the impact of strategic or tactical changes prior to implementing them in practice, specifically in an inpatient setting. It also provides the flexibility to design and test a wide variety of scenarios, and has proved its utility as a decision support tool that can be leveraged periodically by leadership at MSKCC.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"93"},"PeriodicalIF":5.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528379","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":"Addressing Model Inconsistencies and Latency Challenges in LLM-Driven Emergency Medical Documentation: A Commentary.","authors":"Zekai Yu, Siyi Liu","doi":"10.1007/s10916-025-02226-8","DOIUrl":"https://doi.org/10.1007/s10916-025-02226-8","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"92"},"PeriodicalIF":3.5,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505998","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":"Epilepsy Prediction via Time-Frequency Features and Multi-Scale Hybrid Neural Networks.","authors":"Wenwen Chang, Bingyang Ji, Dandan Li, Lei Zhen, Yaxuan Wei, Xuan Liu, Guanghui Yan","doi":"10.1007/s10916-025-02224-w","DOIUrl":"https://doi.org/10.1007/s10916-025-02224-w","url":null,"abstract":"<p><p>The prediction of epileptic seizures heavily depends on the precise embedding and classification of complex, multi-dimensional electroencephalogram (EEG) signals. Due to individual variability and the dynamic non-linear nature of EEG signals, extracting highly discriminative spatiotemporal features is a core challenge in this field. In this study, to address this issue, we proposed a novel architecture based on the Epilepsy Prediction using Multi-Scale Hybrid Neural Network (EPM-HNN), which integrates adaptive channel weighting, multi-scale spatial feature extraction, and bidirectional temporal dependency modeling. Specifically, we incorporated a sliding window mechanism with spatiotemporal resolution into the feature extraction process, enhancing the model's sensitivity to neural dynamics across frequency bands and improving its ability to capture micro-patterns. We used the Res2Net-50 multi-scale feature extractor to enhance the convolutional neural network's capacity to process complex local micro-features, such as polyspike-and-slow-wave complexes. Additionally, we introduced Squeeze-and-Excitation Networks (SENet) to adaptively capture potential effective features between different EEG channels. This dynamic weighting mechanism based on adaptive attention demonstrates strong robustness and high generalization across individual subject data. Furthermore, we proposed a non-single-subject, non-specific cross-subject training and testing method, demonstrating its ability to combat overfitting when addressing differences in data distribution. Experiments on the CHB-MIT scalp EEG dataset achieved an overall prediction accuracy of 97.7%, validating the effectiveness of the proposed EPM-HNN architecture.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"90"},"PeriodicalIF":3.5,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484681","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":"Key Players Overlooked in the Rapid Deployment of DeepSeek To China's Hospitals.","authors":"Hongnan Ye","doi":"10.1007/s10916-025-02225-9","DOIUrl":"https://doi.org/10.1007/s10916-025-02225-9","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"91"},"PeriodicalIF":3.5,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484682","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":"The Impact of Virtual-, Augmented- and Mixed Reality during Preoperative Informed Consent: A Systematic Review of the Literature.","authors":"Konstantin Wehrkamp, Rainer C Miksch, Hans Polzer, Fabian Gilbert, Markus Bühner, Boris M Holzapfel, Wolfgang Böcker, Rouven Neudeck","doi":"10.1007/s10916-025-02217-9","DOIUrl":"10.1007/s10916-025-02217-9","url":null,"abstract":"<p><p>This systematic literature review aimed to examine the effects of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) head-mounted displays (HMDs) on patient understanding, satisfaction, and anxiety during preoperative informed consent. Following PRISMA-P guidelines (Prospero ID: CRD42023487281), we searched four major databases from their inception to March 24, 2023. Studies were eligible if they utilized VR, AR, or MR HMDs to visualize patient-specific data during informed consent across any medical specialty. Two reviewers independently conducted all steps of the systematic review process, and the risk of bias was assessed using the Methodological Index for Non-Randomized Studies (MINORS). Sixteen studies involving a total of 1067 patients were identified and included. These comprised 10 Randomized Controlled Trials (RCTs) and 6 Non-Randomized Controlled Trials (non-RCTs), including one comparative study and five non-comparative studies. The literature reviewed was heterogeneous, encompassing patients with diverse conditions across various medical specialties, including cardiology, neurosurgery, transplantation surgery, vascular surgery, plastic surgery, and urology. The results demonstrated that VR, AR, and MR HMDs positively impact patient understanding, satisfaction, and anxiety reduction. Notably, the findings were more consistent for VR HMDs compared to the limited and variable literature on AR and MR HMDs. VR, AR, and MR HMDs generally show positive effects on patient understanding, satisfaction, and anxiety in preoperative informed consent. While VR HMDs consistently yield positive outcomes, further research is needed to elucidate the effectiveness and benefits of AR and MR HMDs in preoperative consultations.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"89"},"PeriodicalIF":3.5,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12187789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144484683","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":"Narrative Review of Electronic Health Record Systems in Anesthesia: Benefits, Risks, and Medico-Legal Considerations in the United States of America.","authors":"George Tewfik, Beth Minzter, Franklin Chiao, Joel Zivot, Matthew Wecksell, Allan F Simpao","doi":"10.1007/s10916-025-02221-z","DOIUrl":"10.1007/s10916-025-02221-z","url":null,"abstract":"<p><p>Electronic health records (EHRs) have transformed healthcare delivery and documentation by accurately capturing routine care and critical events through automated data recording. EHRs also enable clinical decision support, quality improvement initiatives, and large-scale research. A narrative review has been constructed using relevant research regarding medicolegal liability associated with EHRs, related to anesthesia care. EHRs have created new liability exposures through alert fatigue, system errors, and inappropriate use of functions such as copy-and-paste. Ethical issues and concerns with EHRs include privacy, informed consent, and secondary data uses. Metadata, \"data about the data\", provides insight into record authenticity, clinician involvement in care, and communication between providers. However, EHR metadata is legally discoverable, and courts have compelled its release to plaintiffs despite hospital objections. This narrative review covers the benefits of EHRs in anesthesiology practice, discusses medicolegal liability and ethical concerns, and highlights a method for assessing medicolegal risks using the EHR.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"87"},"PeriodicalIF":3.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12183129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144368989","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":"Enhancing Surgical Precision: A Systematic Review of Wearable Medical Devices for Assisted Surgery.","authors":"Houneida Sakly, Ramzi Guetari, Naoufel Kraiem, Nada Abdulaziz Alasbali, Ali Al Agarni","doi":"10.1007/s10916-025-02222-y","DOIUrl":"10.1007/s10916-025-02222-y","url":null,"abstract":"<p><p>The integration of wearable medical devices into surgical practice has transformed the field, enabling enhanced precision, informed decision-making, and improved patient outcomes. These devices, which include biosensors and augmented reality (AR) headsets, continuously collect and provide real-time data to support surgeons during complex procedures. A key advancement in this domain is the incorporation of artificial intelligence (AI), which enables these devices to analyze large datasets, generate predictive insights, and adapt to changing clinical scenarios, thus assisting in surgical decision-making. AI-driven analytics, combined with wearable technology, optimize surgical performance by detecting potential complications early and allowing for dynamic adjustments to surgical plans. However, widespread adoption faces challenges, including data privacy concerns, device interoperability issues, and regulatory compliance requirements. This paper presents a comprehensive review of wearable medical devices in surgery, examining their applications, limitations, and transformative role of AI in advancing surgical care. Additionally, the paper discusses strategies to overcome barriers, supporting the integration of these technologies into future surgical practices.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"88"},"PeriodicalIF":3.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144368988","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":"Generative Artificial Intelligence is Emerging as a Phantom Doctor in Low- and Middle-Income Countries - Health Systems Must Respond.","authors":"Md Doulotuzzaman Xames","doi":"10.1007/s10916-025-02223-x","DOIUrl":"10.1007/s10916-025-02223-x","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"86"},"PeriodicalIF":3.5,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340203","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}