Journal of Medical Systems最新文献

{"title":"Using Generative AI to Extract Structured Information from Free Text Pathology Reports.","authors":"Fahad Shahid, Min-Huei Hsu, Yung-Chun Chang, Wen-Shan Jian","doi":"10.1007/s10916-025-02167-2","DOIUrl":"10.1007/s10916-025-02167-2","url":null,"abstract":"<p><p>Manually converting unstructured text pathology reports into structured pathology reports is very time-consuming and prone to errors. This study demonstrates the transformative potential of generative AI in automating the analysis of free-text pathology reports. Employing the ChatGPT Large Language Model within a Streamlit web application, we automated the extraction and structuring of information from 33 unstructured breast cancer pathology reports from Taipei Medical University Hospital. Achieving a 99.61% accuracy rate, the AI system notably reduced the processing time compared to traditional methods. This not only underscores the efficacy of AI in converting unstructured medical text into structured data but also highlights its potential to enhance the efficiency and reliability of medical text analysis. However, this study is limited to breast cancer pathology reports and was conducted using data obtained from hospitals associated with a single institution. In the future, we plan to expand the scope of this research to include pathology reports for other cancer types incrementally and conduct external validation to further substantiate the robustness and generalizability of the proposed system. Through this technological integration, we aimed to substantiate the capabilities of generative AI in improving both the speed and reliability of data processing. The outcomes of this study affirm that generative AI can significantly transform the handling of pathology reports, promising substantial advancements in biomedical research by facilitating the structured analysis of complex medical data.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"36"},"PeriodicalIF":3.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143624956","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 Acceptance of Digital Health Technologies for Managing Non-Communicable Diseases Among Older Adults: A Systematic Scoping Review.","authors":"Sadia Azmin Anisha, Arkendu Sen, Badariah Ahmad, Chris Bain","doi":"10.1007/s10916-025-02166-3","DOIUrl":"10.1007/s10916-025-02166-3","url":null,"abstract":"<p><p>This review explores the acceptance of digital health (DH) technologies for managing non-communicable diseases (NCDs) among older adults (≥ 50 years), with an extended focus on artificial intelligence (AI)-powered conversational agents (CAs) as an emerging notable subset of DH. A systematic literature search was conducted in June 2024 using PubMed, Web of Science, Scopus, and ACM Digital Library. Eligible studies were empirical and published in English between January 2010 and May 2024. Covidence software facilitated screening and data extraction, adhering to PRISMA-ScR guidelines. The screening process finally yielded 20 studies. Extracted data from these selected studies included interventions, participant demographics, technology types, sample sizes, study designs and locations, technology acceptance measures, key outcomes, and methodological limitations. A narrative synthesis approach was used for analysis, revealing four key findings: (1) overall positive attitudes of older adults towards DH acceptance; (2) the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are the most frequently used standard frameworks for evaluating technology acceptance; (3) the key facilitators of technology acceptance include perceived usefulness, ease of use, social influence, and digital or e-health literacy, while barriers involve technical challenges, usability issues, and privacy concerns; (4) the acceptance of AI-based CAs for NCD management among older adults remains inadequately evaluated, possibly due to limited adaptation of established frameworks to specific healthcare contexts and technology innovations. This review significantly contributes to the DH field by providing a comprehensive analysis of technology acceptance for NCD management among older adults, extending beyond feasibility and usability. The findings offer stakeholders valuable insights into how to better integrate these technologies to improve health outcomes and quality of life for older adults. Protocol Registration: PROSPERO (Registration ID: CRD42024540035).</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"35"},"PeriodicalIF":3.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604884","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":"D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.","authors":"Xina Liu, Jun Xie, Junjun Hou, Xinying Xu, Yan Guo","doi":"10.1007/s10916-025-02165-4","DOIUrl":"https://doi.org/10.1007/s10916-025-02165-4","url":null,"abstract":"<p><p>Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (FFA), recognized as the gold standard for diagnosing DR, effectively reveals abnormalities in retinal vasculature. Given the labor-intensive and costly nature of manual DR diagnosis, along with its low accuracy, developing a DR classification model based on FFA using deep learning techniques is crucial. Furthermore, DR classification faces challenges such as minimal lesion variance between different disease stages and significant size variations of lesions within the same stage, with small lesions often overlooked by existing models. We propose a deep learning model, D-GET, utilizing a Group-Enhanced Transformer for classifying DR lesion severity in FFA images. The D-GET model incorporates a Full-Scale Transformer Block, where the Group-Focal module captures feature information at multiple scales, from fine details to broader patterns, and adaptively integrates contextual information, enhancing the model's ability to detect small-scale lesions. The model also includes a Channel Adaptive Attention Module (CAAM) that synthesizes channel and spatial information to improve feature detection and localization. Experimental findings indicate that the D-GET method we developed surpasses existing methods on a custom dataset. The D-GET model, developed for DR classification using FFA images, significantly improves the detection of small-scale lesions. This advancement enhances the diagnosis and treatment of DR, establishing a solid foundation for its broader application across various domains of ophthalmic and general medical imaging.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"34"},"PeriodicalIF":3.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143567243","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":"Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.","authors":"José Antonio Rivas-Navarrete, Humberto Pérez-Espinosa, A L Padilla-Ortiz, Ansel Y Rodríguez-González, Diana Cristina García-Cambero","doi":"10.1007/s10916-025-02154-7","DOIUrl":"https://doi.org/10.1007/s10916-025-02154-7","url":null,"abstract":"<p><p>Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from population centers. Sounds from the human respiratory system have displayed potential in autonomously detecting these diseases using artificial intelligence (AI). This article outlines the development of an audio-based edge computing system that automatically detects chronic respiratory diseases (CRDs). The system utilizes machine learning (ML) techniques to analyze audio recordings of respiratory sounds (cough and breath) and classify the presence or absence of these diseases, using features such as Mel frequency cepstral coefficients (MFCC) and chromatic attributes (chromagram) to capture the relevant acoustic features of breath sounds. The system was trained and tested using a dataset of respiratory sounds collected from 86 individuals. Among them, 53 had chronic respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD), while the remaining 33 were healthy. The system's final evaluation was conducted with a group of 13 patients and 22 healthy individuals. Our approach demonstrated high sensitivity and specificity in the classification of sounds on edge devices, including smartphone and Raspberry Pi. Our best results for CRDs reached a sensitivity of 90.0%, a specificity of 93.55%, and a balanced accuracy of 91.75% for accurately identifying individuals with both healthy and diseased. These results showcase the potential of edge computing and machine learning systems in respiratory disease detection. We believe this system can contribute to developing efficient and cost-effective screening tools.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"33"},"PeriodicalIF":3.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542270","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":"Feature-Based Audiogram Value Estimator (FAVE): Estimating Numerical Thresholds from Scanned Images of Handwritten Audiograms.","authors":"Paul G Mayo, Kenneth I Vaden, Lois J Matthews, Judy R Dubno","doi":"10.1007/s10916-025-02146-7","DOIUrl":"https://doi.org/10.1007/s10916-025-02146-7","url":null,"abstract":"<p><p>Hearing loss is a public health concern that affects millions of people globally. Clinically, a person's hearing sensitivity is often measured using pure-tone audiometry and visualized as a pure-tone audiogram, a plot of hearing sensitivity as a function of frequency. Digital test equipment allows clinicians to store audiograms as numerical values, though some practices write audiograms by hand and store them as digital images in electronic health records systems. This leaves the numerical values inaccessible to public-health researchers unless manually interpreted. Therefore, this study developed machine-learning models for estimating numerical threshold values from scanned images of handwritten audiograms. Training data were a novel set of 556 handwritten audiograms from a longitudinal cohort study of age-related hearing loss. The models were sliding-window, single-class object detectors based on Aggregate Channel Features, altogether called Feature-based Audiogram Value Estimator or \"FAVE\". Model accuracy was determined using symbol location accuracy and comparing estimated numerical threshold values to known values from an electronic database. FAVE resulted in an average of 87.0% recall and 96.2% precision for symbol locations. The numerical threshold values were less accurate, with 78.3% of estimations having no error, though threshold estimates were not significantly different from true thresholds. Threshold estimation was more accurate than pre-trained deep learning approaches for the current test set. Future work should consider implementing detectors with similar image channels and identify limitations on symbol and axis tick label detection.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"32"},"PeriodicalIF":3.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143515980","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":"Most Weekday Discharge Times at Acute Care Hospitals in the State of Florida Occurred After 3 PM in 2022, Unchanged from Before the COVID-19 Pandemic.","authors":"Richard H Epstein, Franklin Dexter, Brenda G Fahy","doi":"10.1007/s10916-025-02164-5","DOIUrl":"https://doi.org/10.1007/s10916-025-02164-5","url":null,"abstract":"<p><p>When the hospital census is near-capacity, either from insufficient physical beds or nurse staffing, discharge delays can result in postanesthesia care unit (PACU) congestion that backs up the operating rooms. Hospital administrators often promote increasing morning discharges as mitigation. Before the COVID-19 pandemic, most hospitalized Florida patients were discharged after 3 PM, without change from 2010 through 2018. The current study extended the observation period through 2022 to determine if discharge pressure during the COVID-19 pandemic from persistent high census resulted in overall earlier hospital discharges. Results showed the percentages of patients discharged by 12 noon or 3 PM remained unchanged. Among 1,034,515 discharges at 197 hospitals during the last 2 quarters of 2022, most discharges (P < 0.0001 versus 50%) occurred after 3 PM. The pooled incidence of discharges by noon was 13.2%, while the estimate of the incidence inverse weighted by the hospitals' counts of discharges was 13.3% (97.5% 12.6% to 14.1%). The corresponding pooled incidences of discharges by 3 PM was 42.5%, and 43.7% (97.5% confidence interval 42.3% to 45.2%). All 136,924 combinations of hospital and Medicare severity diagnosis-related groups were evaluated to examine why discharges did not occur earlier. Among the 1377 such combinations (1% of the total) with a significant change in median length of stay, 95% (1313) were decreases in lengths of stay. The implication is that the pandemic had no salutatory effect on earlier discharges. Therefore, post-anesthesia care unit managers should continue to plan for most hospital beds to be unavailable until late afternoon.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"31"},"PeriodicalIF":3.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143492369","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":"Reclaiming Patient-Centered Care: How Intelligent Time is Redefining Healthcare Priorities.","authors":"Elena Giovanna Bignami, Michele Russo, Valentina Bellini","doi":"10.1007/s10916-025-02163-6","DOIUrl":"https://doi.org/10.1007/s10916-025-02163-6","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"30"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468096","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":"Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.","authors":"David Reifs Jiménez, Lorena Casanova-Lozano, Sergi Grau-Carrión, Ramon Reig-Bolaño","doi":"10.1007/s10916-025-02153-8","DOIUrl":"10.1007/s10916-025-02153-8","url":null,"abstract":"<p><p>Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review explores the impact of technological advancements on the diagnosis of chronic wounds, focusing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. A literature search was conducted in databases including ACM, IEEE, PubMed, Scopus, and Web of Science, covering studies from 2013 to 2023. The focus was on articles applying complex computational techniques to analyze chronic wound images and clinical data. Exclusion criteria were non-image samples, review articles, and non-English or non-Spanish texts. From 2,791 articles identified, 93 full-text studies were selected for final analysis. The review identified significant advancements in tissue classification, wound measurement, segmentation, prediction of wound aetiology, risk indicators, and healing potential. The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care. The integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs. Continued research and innovation in computational techniques are essential to unlock their full potential in managing chronic wounds effectively.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"29"},"PeriodicalIF":3.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143449322","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":"Artificial Intelligence (AI) - Powered Documentation Systems in Healthcare: A Systematic Review.","authors":"Aisling Bracken, Clodagh Reilly, Aoife Feeley, Eoin Sheehan, Khalid Merghani, Iain Feeley","doi":"10.1007/s10916-025-02157-4","DOIUrl":"10.1007/s10916-025-02157-4","url":null,"abstract":"<p><p>Artificial Intelligence (AI) driven documentation systems are positioned to enhance documentation efficiency and reduce documentation burden in the healthcare setting. The administrative burden associated with clinical documentation has been identified as a major contributor to health care professional (HCP) burnout. The current systematic review aims to evaluate the efficiency, quality, and stakeholder opinion regarding the use of AI-driven documentation systems. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive search was conducted across PubMed, Embase and Cochrane library. Two independent reviewers applied inclusion and exclusion criteria to identify eligible studies. Details of AI technology, document type, document quality and stakeholder experience were extracted. The review included 11 studies. All included studies utilised Chat generated pretrained transformer (Chat GPT, Open AI, CA, USA) or an ambient AI technology. Both forms of AI demonstrated significant potential to improve documentation efficiency. Despite efficiency gains, the quality of AI-generated documentation varied across studies. The heterogeneity of methods utilised to assess document quality influenced interpretation of results. HCP opinion was generally positive, users highlighted ease of use and reduced task load as primary benefits. However, HCPs also expressed concerns about the reliability and validity of AI-generated documentation. Chat GPT and ambient AI show promise in enhancing the efficiency and quality of clinical documentation. While the efficiency benefits are clear, the challenges associated with accuracy and consistency need to be addressed. HCP experiences indicate a cautious optimism towards AI integration, however reliability will depend on continued refinement and validation of the technology.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"28"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11835907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143449316","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":"Electronic Prescribing in the Neonatal Intensive Care Unit: Analysis of Prescribing Errors and Risk Factors.","authors":"M D Canales-Siguero, C García-Muñoz, J M Caro-Teller, S Piris-Borregas, S Martín-Aragón, J M Ferrari-Piquero, M T Moral-Pumarega, C R Pallás-Alonso","doi":"10.1007/s10916-025-02161-8","DOIUrl":"https://doi.org/10.1007/s10916-025-02161-8","url":null,"abstract":"<p><p>Patients admitted to neonatal intensive care units are up to eight times more likely to experience medication errors than patients admitted to adult intensive care units. Prescribing errors account for up to 74% of medication errors. Electronic prescribing has been postulated as a tool to reduce errors. The objective was to analyse prescribing errors with the e-prescribing system and risk factors. All patients who were admitted for at least 24 h and who received active pharmacological treatment during the study period were included. Prescriptions were made using electronic assisted prescription software integrated into the medical record system. Treatment was reviewed daily by a pharmacist, and errors were graded according to taxonomic criteria. A total of 240 patients were included, 13,876 prescriptions were reviewed and 455 errors were found (3.3% of prescriptions were wrong). Prescribing errors were concentrated in 40 drugs/nutritional products. The most frequent error was a discrepancy between the prescription and the associated text-free field (n = 196). The drugs with the most errors were Lactobacillus acidophilus, caffeine citrate, acetaminophen, gentamycin and cholecalciferol. Patients with a birth weight from 1000 to 1500 g were 82% more likely to experience an error than those with an extremely low birth weight (< 1000 g) (OR = 1.81, 95% CI = 1.42-2.89, p < 0.05). Patients at the highest risk were those with gestational ages from 28 to 32 weeks, with a 29.80% greater risk of prescribing errors than those with gestational ages less than 28 weeks (OR = 1.29, 95% CI = 1.02-1.65, p < 0.05). Prescribing errors occur due to complex dosing rules based on patient characteristics and free-text use, highlighting process issues rather than specific medication risks.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"26"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441064","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}