Journal of Medical Systems最新文献

{"title":"An Exploratory Comparison of AI Models for Preoperative Anesthesia Planning: Assessing ChatGPT-4o, Claude 3.5 Sonnet, and ChatGPT-o1 in Clinical Scenario Analysis.","authors":"Bing Wang, Yue Tian, Xue Ting Wang","doi":"10.1007/s10916-025-02243-7","DOIUrl":"10.1007/s10916-025-02243-7","url":null,"abstract":"<p><p>This exploratory study examined the effectiveness of ChatGPT-4o, Claude 3.5 Sonnet, and ChatGPT-o1 in developing anesthesia plans for critical cases. Personalized anesthesia plans are essential for ensuring surgical safety and patient satisfaction. These artificial intelligence (AI) models can understand and generate anesthesia-related information. The study included a panel of five anesthesia experts, each with over ten years of experience. They qualitatively and quantitatively assessed the capabilities of the three models in formulating anesthesia plans for critical cases. The results showed no significant differences in the response quality, relevance, and applicability scores among the models; however, variations were observed in the error types and severity. ChatGPT-o1 surpassed the other models in terms of content relevance and information accuracy, demonstrating a lower error rate and higher suitability for clinical application. As an initial investigation in this rapidly evolving field, this research provides preliminary insights while acknowledging the need for further validation in clinical settings before implementation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"104"},"PeriodicalIF":5.7,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855564","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":"Integrating mHealth Innovations into Decentralized Oncology Trials.","authors":"Fatma Nur Cicin, Irfan Cicin","doi":"10.1007/s10916-025-02233-9","DOIUrl":"https://doi.org/10.1007/s10916-025-02233-9","url":null,"abstract":"<p><p>The integration of mobile health (mHealth) technologies into decentralized clinical trials (DCTs) may represent a paradigm shift in oncology research, offering innovative solutions to longstanding challenges in clinical trial design and execution. mHealth tools, including wearable biosensors, telemedicine platforms, and artificial intelligence (AI)-driven analytics, have the potential to enable real-time patient monitoring, support participant engagement, and facilitate remote data collection. Also, these advancements have the potential to improve recruitment rates, optimize treatment adherence, and ensure more equitable access to clinical trials, particularly for patients in underserved regions. Moreover, precision oncology approaches leveraging mHealth data may contribute to personalized treatment strategies that improve patient outcomes. However, regulatory complexities, data privacy concerns, and technological disparities remain critical challenges that must be addressed to ensure the widespread adoption of mHealth-enabled DCTs. Future advancements in AI, blockchain technology, and remote diagnostic tools may further enhance the scalability, efficiency, and inclusivity of these trials. By embracing these innovations, oncology research can transition toward a more patient-centric, data-driven paradigm that has the potential to accelerates drug development and enhances clinical decision-making. This narrative review explores the potentially transformative role of mHealth technologies in DCTs within oncology.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"103"},"PeriodicalIF":5.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144799375","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":"Implementation and Evaluation of an Offline RPA-Based Scheduling Visualization Tool for Radiotherapy Under Security Constraints.","authors":"Takuya Ito, Ryuhei Takeo","doi":"10.1007/s10916-025-02238-4","DOIUrl":"10.1007/s10916-025-02238-4","url":null,"abstract":"<p><p>Timely access to radiotherapy appointment information remains challenging in hospitals-including many in Japan-due to fragmented information systems and strict security policies restricting access to databases or application programming interfaces (API). We developed an offline robotic process automation (RPA) workflow that captures scheduling data directly from the graphical user interfaces (GUIs) of the electronic medical record (EMR) and radiology information system (RIS) consolidating new-patient consultations, computed tomography (CT) simulations, and first-fraction irradiation sessions into a color-coded, four-week Excel calendar. The robot, scripted using the no-code KEYENCE RK-10 environment, logs into each application, applies rule-based filters, exports comma-separated-values (CSV) files, and populates a pre-formatted template without requiring any back-end modifications. During a two-week deployment at a community radiotherapy center, each weekday run finished in a median 3.9 min [inter-quartile range, IQR 3.1-4.8 min] and listed 36 appointments (16 consultations, five CTs, and 15 first-fraction irradiations). Two discrepancies compared to a legacy whiteboard revealed manual omissions. A semi-structured survey of 12 staff members recorded the highest Likert score [median 5.0, IQR 4.0-5.0] for reductions in documentation time, perceived workload, and error frequency in favor of the RPA implementation. This lightweight GUI-level approach provides a secure and rapidly deployable solution for small- to medium-sized radiotherapy centers operating under stringent information-technology (IT) policies, enhancing scheduling accuracy and staff efficiency without database integration. EMR, RPA, Radiation Therapy, Automation, Visualization.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"102"},"PeriodicalIF":5.7,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12328462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794722","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":"Understanding Clinician Perceptions of GenAI: A Mixed Methods Analysis of Clinical Documentation Tasks.","authors":"David Fraile Navarro, A Baki Kocaballi, Shlomo Berkovsky","doi":"10.1007/s10916-025-02234-8","DOIUrl":"10.1007/s10916-025-02234-8","url":null,"abstract":"<p><p>This mixed-methods study evaluated clinicians' user experience (UX) with Generative AI (GenAI) in Electronic Health Record (EHR) systems across three clinical documentation tasks (Information Extraction, Summarization, and Speech-to-Text) at varying levels of user supervision (low, medium, high), focusing on workflow improvements, safety, and acceptable automation levels. Using conceptual prototyping in a usability study framework, we evaluated how incorporating GenAI into EHR could support the three documentation tasks at varying automation levels. A total of 38 clinicians interacted with the prototype and completed a questionnaire on task relevance, perceived importance, desired automation level, and EHR satisfaction. Both quantitative (descriptive statistics, Kruskal-Wallis tests, Spearman correlations) and qualitative (thematic) analyses were conducted with equal priority to explore preferences, perceived safety, and practical requirements. Clinicians showed positive reception to GenAI integration, particularly for streamlining documentation. While task relevance and importance were strongly correlated, EHR satisfaction did not significantly predict automation acceptance. Medium automation emerged as the preferred level, considered \"safe with caution\". Five key themes emerged from qualitative analysis: efficiency and quality benefits; system reliability concerns; safety and medico-legal considerations; automation bias and loss of nuance; and deployment requirements including adjustable settings and oversight. While clinicians welcome GenAI-driven documentation, they prefer moderate automation to balance efficiency with clinical control. Successful integration requires addressing safety concerns, conducting real-world trials, and mitigating potential biases and medico-legal challenges. These findings suggest a cautious but optimistic path forward for AI integration in EHR systems, emphasizing the importance of maintaining clinician oversight while leveraging automation benefits.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"101"},"PeriodicalIF":5.7,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768686","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":"Dissecting HealthBench: Disease Spectrum, Clinical Diversity, and Data Insights from Multi-Turn Clinical AI Evaluation Benchmark.","authors":"Jialin Liu, Siru Liu","doi":"10.1007/s10916-025-02232-w","DOIUrl":"10.1007/s10916-025-02232-w","url":null,"abstract":"<p><p>HealthBench is an open-source, large-scale benchmark consisting of 5,000 multi-turn clinical conversations evaluated against 48,562 criteria developed by clinicians. Recognized as a significant advancement in assessing realistic artificial intelligence (AI) models, HealthBench deserves further exploration. In this article, we systematically analyze the benchmark's disease spectrum, diagnostic and therapeutic focuses, and demographic diversity. We evaluate its representativeness and strengths, as well as the essential limitations that AI researchers and clinicians should consider when using it for realistic model evaluations.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"100"},"PeriodicalIF":5.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12304011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144731835","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":"Quantum Oncology: The Applications of Quantum Computing in Cancer Research.","authors":"Jiaran Li, Li Li, Ning Liu, Fuhao Xu, Tao Hu, Shuanghu Yuan","doi":"10.1007/s10916-025-02215-x","DOIUrl":"https://doi.org/10.1007/s10916-025-02215-x","url":null,"abstract":"<p><p>A global technological race is underway to develop increasingly powerful and precise quantum computers. As a transformative computing paradigm, quantum computing offers the potential for exponentially accelerating specific algorithms, thereby providing the necessary computational power to process vast amounts of data. In light of the challenges classical computing faces with the complexity and volume of oncology data, we introduce the concept of \"quantum oncology\" and explore its potential applications throughout the cancer care continuum. Additionally, we address several challenges and potential solutions for integrating quantum computing into oncology research. By illuminating these issues, we aim to deepen the understanding of quantum computing's potential in oncology and advocate for multidisciplinary collaboration to propel the advancement of precision oncology. CLINICAL TRIAL NUMBER: Not applicable.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"99"},"PeriodicalIF":3.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690541","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":"Why Clinical Trials Will Fail to Ensure Safe AI.","authors":"David P W Rastall, Mohamed Rehman","doi":"10.1007/s10916-025-02231-x","DOIUrl":"10.1007/s10916-025-02231-x","url":null,"abstract":"<p><p>Recent reports have raised concerns about emergent behaviors in next-generation artificial intelligence (AI) models. These systems have been documented selectively adapting their behaviors during testing to falsify experimental outcomes and bypass regulatory oversight. This phenomenon-alignment faking-represents a fundamental challenge to medical AI safety. Regulatory strategies have largely adapted established protocols like clinical trials and medical device approval frameworks, but for next-generation AI these approaches may fail. This paper introduces alignment faking to a medical audience and critically evaluates how current regulatory tools are inadequate for advanced AI systems. We propose continuous logging through \"AI SOAP notes\" as a first step toward transparent and accountable AI functionality in clinical settings.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"98"},"PeriodicalIF":3.5,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649703","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":"Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models.","authors":"Vishal Singh Roha, Rahul Ranjan, Mehmet Rasit Yuce","doi":"10.1007/s10916-025-02228-6","DOIUrl":"10.1007/s10916-025-02228-6","url":null,"abstract":"<p><p>Traditional cuffless blood pressure (BP) estimation methods often require collecting physiological signals, such as electrocardiogram (ECG) and photoplethysmography (PPG), from two distinct body sites to compute metrics like pulse transit time (PTT) or pulse arrival time (PAT). While these metrics strongly correlate with BP, their reliance on multiple signal sources and susceptibility to noise from modern wearable devices present significant challenges. Addressing these limitations, we propose an innovative framework that requires only PPG signals from a single body site, leveraging advancements in artificial intelligence and computer vision. Our approach employs images of PPG signals, along with their first (vPPG) and second (aPPG) derivatives, for enhanced BP estimation. ResNet-50 is utilized to extract features and identify regions within the PPG, vPPG, and aPPG images that correlate strongly with BP. These features are further refined using multi-head cross-attention (MHCA) mechanism, enabling efficient information exchange across the modalities derived from ResNet-50 outputs, thereby improving estimation accuracy. The framework is validated on three distinct datasets, demonstrating superior performance compared to traditional PAT and PTT-based methods. Furthermore, it adheres to stringent medical standards, such as those defined by the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS), ensuring clinical reliability. By reducing the need for multiple signal sources and incorporating cutting-edge AI techniques, this framework represents a significant advancement in non-invasive BP monitoring, offering a more practical and accurate alternative to traditional methodologies.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"97"},"PeriodicalIF":3.5,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12241270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591464","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":"Using Performance Frontiers To Evaluate Non-OR Anesthesia (NORA) Efficiency.","authors":"Justin S Routman, Erik J Zhang, Jonathan D Blocker, Juhan Paiste, Mitchell H Tsai","doi":"10.1007/s10916-025-02229-5","DOIUrl":"10.1007/s10916-025-02229-5","url":null,"abstract":"<p><strong>Introduction: </strong>In high-cost, high-revenue operating room (OR) suites, dashboards displaying key performance indicators are commonplace to optimize efficiency. Given the significant successes attained, further gains may risk compromising safety. In contrast, challenges unique to non-operating room anesthesia (NORA) sites have hindered operational efficiency. Existing productivity evaluation frameworks often fall short in guiding strategic and tactical improvements in NORA delivery. Performance frontiers have proven effective in evaluating OR systems, but their application to NORA remains unexplored. This study applies performance frontiers to assess NORA site efficiency and formulates potential operational strategies.</p><p><strong>Methods: </strong>We evaluated anesthesia billing records at our primary hospital from 1 April 2022 to 30 March 2023. Cases from operating room and NORA locations were included, except for sites with irregular volume or financial arrangements. We included only non-holiday weekdays, defining NORA block time as 7 AM to 5 PM. For each room, we calculated under-utilized (time with no anesthesia billing) and over-utilized minutes (time billed outside of NORA block hours). Data for each location were plotted as rolling 4-week sums, normalized to scheduled NORA block time. Performance frontiers were then developed and plotted.</p><p><strong>Results: </strong>Over 246 non-holiday weekdays, 42,424 cases had billable minutes during NORA block time, comprising 20,003 (47.2%) NORA cases and 22,421 (52.8%) OR cases. Performance frontiers revealed significant variability, with nonparametric tests confirming statistical significance and non-equivalence.</p><p><strong>Discussion: </strong>Performance frontiers reveal substantial efficiency variability across NORA sites, underscoring the need for targeted interventions. Some sites matched OR efficiency levels, while others showed substantial differences, particularly those with high variability and urgency. Efficient sites can leverage performance frontiers to optimize resource allocation, while inefficient locations may benefit from a shared anesthesia resource pool for real-time resource allocation. Performance frontiers provide a novel approach for operational leaders to make more effective strategic decisions.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"96"},"PeriodicalIF":3.5,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12238198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591465","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":"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}