Mayo Clinic Proceedings. Digital health最新文献

{"title":"Integrating U-Net in a LLM Supervisor Agent Pipeline for Pancreatic Ductal Adenocarcinoma Diagnosis","authors":"Rahul Gomes PhD, William L. Jerome BS, Sushil K. Garg MBBS","doi":"10.1016/j.mcpdig.2025.100287","DOIUrl":"10.1016/j.mcpdig.2025.100287","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100287"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph-Based Deep Ensemble Learning to Enhance Diagnostic Efficiency in Lung Adenocarcinoma H&E-Stained Histopathological Subtyping","authors":"Mohammad Mehdi Hosseini , Meghdad Sabouri Rad , Junze (Vincent) Huang , Rakesh Choudhary , Saverio J. Carello , Ola El-Zammar , Michel Nasr , Bardia Rodd","doi":"10.1016/j.mcpdig.2025.100285","DOIUrl":"10.1016/j.mcpdig.2025.100285","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100285"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements","authors":"Stephanie M. Helman PhD ,&nbsp;Nathan T. Riek PhD ,&nbsp;Susan M. Sereika PhD ,&nbsp;Ahmad P. Tafti PhD ,&nbsp;Robert Olsen BS ,&nbsp;J. William Gaynor MD ,&nbsp;Amy Jo Lisanti PhD ,&nbsp;Salah S. Al-Zaiti PhD","doi":"10.1016/j.mcpdig.2025.100270","DOIUrl":"10.1016/j.mcpdig.2025.100270","url":null,"abstract":"<div><h3>Objective</h3><div>To identify distinct postoperative temperature trajectories in neonates with congenital heart defects after cardiopulmonary bypass (CPB), using advanced unsupervised machine learning clustering methods, corroborate findings, and evaluate their prognostic value on outcomes.</div></div><div><h3>Patients and Methods</h3><div>A secondary cohort analysis of prospective data collected from a single pediatric referral center’s CardioAccess data registry, consistent of neonates who underwent CPB between January 1, 2015, and January 1, 2019, was performed. Postoperative temperatures were extracted from medical records (48 hours). Group-based trajectory modeling (GBTM) performance was compared with self-organizing maps (SOM) and k-means clustering. Cluster membership and model fit were optimized for 3 temperature clusters per method. The primary outcome was a composite of postoperative complications. Clustering techniques were compared and associated with outcomes using adjusted multivariable binary logistic regression.</div></div><div><h3>Results</h3><div>Neonates of ≥34 weeks’ gestation underwent CPB (<em>N</em>=450). GBTM, SOM, and k-means identified membership for 3 groups: (1) persistent hypothermia (n=38 [9%]; n=49 [11%]; and n=40 [9%], respectively); (2) resolving hypothermia (n=233 [51%]; n=227 [50%]; and n=147 [33%], respectively); and (3) normothermia (n=179 [40%]; n=174 [39%]; and n=263 [58%], respectively). Concordance between techniques found strong agreement between GBTM and SOM (κ=0.92) and weak agreement between GBTM and k-means (κ=0.41). After adjustment, persistently hypothermic neonates compared with normothermic neonates were associated with higher odds of the complication composite outcome in the GBTM (odds ratio [OR], 2.8; 95% CI, 1.0-7.3; <em>P</em>=.04) and SOM (OR, 2.3; 95% CI, 1.0-5.4; <em>P</em>=.04) models, but not in the k-means model (OR, 1.4; 95% CI, 0.7-3.1; <em>P</em>=.38).</div></div><div><h3>Conclusion</h3><div>Exploring concordance between different machine learning techniques shows that temperature in neonates after CPB follows unique trajectories. Those exhibiting persistent hypothermia trends are at higher risk of adverse outcomes.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100270"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Technology Selection Tool Applying Multiple Criteria Decision Analysis for Virtual Care Implementation","authors":"Joseph P. Deason MBA ,&nbsp;Scott J. Adams MD, PhD, FRCPC ,&nbsp;Ahmad Rahman MSc ,&nbsp;Stacey Lovo PhD ,&nbsp;Ivar Mendez MD, PhD, FRCSC","doi":"10.1016/j.mcpdig.2025.100260","DOIUrl":"10.1016/j.mcpdig.2025.100260","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and pilot a technology selection tool (TST) designed to evaluate and recommend virtual care technologies tailored to specific community clinical needs.</div></div><div><h3>Patients and Methods</h3><div>Developed through collaborations among clinicians, software developers, technology experts, and health administrators, the TST uses a multiple criteria decision analysis framework to recommend technologies based on clinical relevance and technical quality. Its functionality was tested in a pilot project that assessed 5 technologies for their application in virtual wound care to support a remote community in Saskatchewan, Canada. The pilot study was completed March 7, 2025, through July 28, 2025.</div></div><div><h3>Results</h3><div>The TST identified the TeleVU Glass View as the optimal technology for virtual wound care. The TST generated product scores for the TeleVU Glass View (71.67), Teladoc Xpress (70.10), 19 Labs GALE (50.67), and TytoCare TytoKit (47.00), whereas disqualifying the Teladoc Lite Cart for not meeting the pass–fail portability criterion. TeleVU’s high product score resulted primarily from its technological attribute quality scores for Telestration (10), Audio (9), Video (9), and Share Content (9), which were all determined as clinically relevant for virtual wound care. The pilot enabled real-time wound care support by connecting local clinicians with virtual teams.</div></div><div><h3>Conclusion</h3><div>The TST offers a practical and adaptable tool to support evidence-based decision making for selecting technologies for specific clinical applications.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100260"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Bias at Scale in Clinical Notes Using Large Language Models","authors":"Donald U. Apakama MD, MS ,&nbsp;Kim-Anh-Nhi Nguyen MS ,&nbsp;Daphnee Hyppolite MPA, RHIA ,&nbsp;Shelly Soffer MD ,&nbsp;Aya Mudrik BS ,&nbsp;Emilia Ling MD, MBA, MS ,&nbsp;Akini Moses MD ,&nbsp;Ivanka Temnycky MS ,&nbsp;Allison Glasser MBA ,&nbsp;Rebecca Anderson MPH ,&nbsp;Prathamesh Parchure MS ,&nbsp;Evajoyce Woullard MS ,&nbsp;Masoud Edalati PhD ,&nbsp;Lili Chan MD, MS ,&nbsp;Clair Kronk PhD ,&nbsp;Robert Freeman RN ,&nbsp;Arash Kia MD ,&nbsp;Prem Timsina MD, PhD ,&nbsp;Matthew A. Levin MD ,&nbsp;Rohan Khera MD, MS ,&nbsp;Girish N. Nadkarni MD, MPH","doi":"10.1016/j.mcpdig.2025.100296","DOIUrl":"10.1016/j.mcpdig.2025.100296","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate whether generative pretrained transformer (GPT)-4 can detect and revise biased language in emergency department (ED) notes, against human-adjudicated gold-standard labels, and to identify modifiable factors associated with biased documentation.</div></div><div><h3>Patients and Methods</h3><div>We randomly sampled 50,000 ED medical and nursing notes from the Mount Sinai Health System (January 1, 2023, to December 31, 2023). We also randomly sampled 500 discharge notes from the Medical Information Mart for Intensive Care IV database. The GPT-4 flagged 4 types of bias: discrediting, stigmatizing/labeling, judgmental, and stereotyping. Two human reviewers verified model detections. We used multivariable logistic regression to examine associations between bias and health care utilization, presenting problems (eg, substance use), shift timing, and provider type. We then asked physicians to rate GPT-4’s proposed language revisions on a 10-point scale.</div></div><div><h3>Results</h3><div>The GPT-4 showed 97.6% sensitivity and 85.7% specificity compared with the human review. Biased language appeared in 6.5% (3229 of 50,000) of Mount Sinai notes and 7.4% (37 of 500) of Medical Information Mart for Intensive Care IV notes. In adjusted models, frequent health care utilization (adjusted odds ratio [aOR], 2.85; 95% CI, 1.95-4.17), substance use presentations (aOR, 3.09; 95% CI, 2.51-3.80), and overnight shifts (aOR, 1.37; 95% CI, 1.23-1.52) showed elevated odds of biased documentation. Physicians were more likely to include bias than nurses (aOR, 2.26; 95% CI, 2.07-2.46); GPT-4’s recommended revisions received mean physician ratings above 9 of 10.</div></div><div><h3>Conclusion</h3><div>The study showed that GPT-4 accurately detects biased language in clinical notes, identifies modifiable contributors to that bias, and delivers physician-endorsed revisions. This approach may help mitigate documentation bias and reduce disparities in care.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100296"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundation Models and Their Applications in Gastrointestinal Endoscopy","authors":"Jeffrey R. Fetzer PhD ,&nbsp;Saghir A. Al-Fasly PhD ,&nbsp;Cadman L. Leggett MD ,&nbsp;Nayantara Coelho-Prabhu MD ,&nbsp;Shounak Majumder MD ,&nbsp;John B. League III MMIS ,&nbsp;Shradha Shalini MS ,&nbsp;Ghazal Alabtah ,&nbsp;Christine V. Dvorak MAOL ,&nbsp;Hamid R. Tizhoosh PhD","doi":"10.1016/j.mcpdig.2025.100282","DOIUrl":"10.1016/j.mcpdig.2025.100282","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100282"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Selection and Machine Learning Strategies Optimize a Minimal Molecular Assay for Cholangiocarcinoma Subtype","authors":"Ellen L. Larson MD ,&nbsp;Erik Jessen PhD ,&nbsp;Dong-Gi Mun PhD ,&nbsp;Jennifer L. Tomlinson MD ,&nbsp;Amro M. Abdelrahman MBBS, MS ,&nbsp;Danielle M. Carlson ,&nbsp;Hojjat Salehinejad PhD ,&nbsp;Rory L. Smoot MD","doi":"10.1016/j.mcpdig.2025.100283","DOIUrl":"10.1016/j.mcpdig.2025.100283","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100283"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Byline or Botline? The Dilemma of Artificial Intelligence in Medical Scholarship","authors":"James Connor BSc, MB BCh, BAO","doi":"10.1016/j.mcpdig.2025.100259","DOIUrl":"10.1016/j.mcpdig.2025.100259","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100259"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internet of Things-Based Wayfinding for Hospital Visitors: A Digital Solution for Complex Health Care Infrastructures","authors":"Prajwal L. Salins MHA ,&nbsp;Ganesh Anandan MSc ,&nbsp;Basilio Duke Ananda MSc ,&nbsp;Bhageerathy Reshmi MSc, PhD ,&nbsp;Roshan David Jathanna MTech, PhD","doi":"10.1016/j.mcpdig.2025.100293","DOIUrl":"10.1016/j.mcpdig.2025.100293","url":null,"abstract":"<div><h3>Objective</h3><div>To design, implement, and evaluate a digital indoor wayfinding web application (KH Wayfinder) for a tertiary care hospital, assessing its effects on spatial orientation and navigation-related stress among visitors.</div></div><div><h3>Participants and Methods</h3><div>A 3-phase study was conducted in a tertiary care hospital in coastal Karnataka, India, from April 1, 2023, through July 31, 2024. Phase 1 involved a cross-sectional survey (n=41) to assess user attitudes toward digital wayfinding. In phase 2, a browser-based application was developed using HyperText Markup Language, JavaScript, cascading style sheets, and Leaflet.js, covering 5 hospital floors with 52 destination points and 758 routes. Phase 3 consisted of usability testing with 54 participants using a validated questionnaire to assess performance, satisfaction, and ease of use.</div></div><div><h3>Results</h3><div>The majority of users 33 (80.5%) expressed willingness to use a digital Wayfinder. Postimplementation results showed that 46 (85.2%) found the tool easy to use, 47 (87%) reported a reduction in navigation time, and 45 (83.3%) experienced reduced psychological stress. Additionally, 51 (94.4%) preferred the digital system over traditional signage, and 54 (100%) would recommend it to others.</div></div><div><h3>Conclusion</h3><div>KH Wayfinder demonstrated high usability, effectiveness, and user satisfaction as a low-cost digital navigation solution. Its browser-based architecture and open-source design make it scalable and adaptable for broader use in smart hospital environments. Future enhancements may include real-time positioning, multilingual support, and accessibility features.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100293"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dentistry as a Test Case for Responsible Artificial Intelligence in Healthcare: Why External Validation Matters More Than Marketing","authors":"Owais A. Farooqi DDS, MDS, MPH, FACHE","doi":"10.1016/j.mcpdig.2025.100298","DOIUrl":"10.1016/j.mcpdig.2025.100298","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100298"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}