Mayo Clinic Proceedings. Digital health最新文献

{"title":"Reimagining Pediatrics in a World of Artificial Intelligence: Will We Be Empowered or Imperiled?","authors":"Shelby Kutty MD, PhD, MHCM , Yiu-fai Cheung MD , Sowmya Viswanathan MD , David A. Danford MD, MPH","doi":"10.1016/j.mcpdig.2025.100258","DOIUrl":"10.1016/j.mcpdig.2025.100258","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100258"},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098564","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":"2025-08-28","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":"A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery","authors":"Busisiwe Mlambo MD ,&nbsp;Mallory Shields PhD ,&nbsp;Simon Bach MD ,&nbsp;Armin Bauer PhD ,&nbsp;Andrew Hung MD ,&nbsp;Omar Yusef Kudsi MD ,&nbsp;Felix Neis MD ,&nbsp;John Lazar MD ,&nbsp;Daniel Oh MD ,&nbsp;Robert Perez MD ,&nbsp;Seth Rosen MD ,&nbsp;Naeem Soomro MD ,&nbsp;Michael Stany MD ,&nbsp;Mark Tousignant MD ,&nbsp;Christian Wagner MD ,&nbsp;Ken Whaler MS ,&nbsp;Lilia Purvis MS ,&nbsp;Benjamin Mueller BS ,&nbsp;Sadia Yousaf MD ,&nbsp;Casey Troxler BS ,&nbsp;Anthony Jarc PhD","doi":"10.1016/j.mcpdig.2025.100257","DOIUrl":"10.1016/j.mcpdig.2025.100257","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and share the first clinical temporal annotation guide library for 10 robotic procedures accompanied with a standardized ontology framework for surgical video annotation.</div></div><div><h3>Patients and Methods</h3><div>A standardized temporal annotation framework of surgical videos paired with consistent, procedure-specific annotation guides is critical to enable comparisons of surgical insights and facilitate large-scale insights for exceptional surgical practice. Existing ontologies and guidance not only provide foundational frameworks but also provide limited scalability in clinical settings. Building on these, we developed a temporal annotation framework with nested surgical phases, steps, tasks, and subtasks. Procedure-specific annotation resource guides consistent with this framework that define each surgical segment with formulaic start and stop parameters and surgical objectives were iteratively created across 7 years (January 1, 2018, to January 1, 2025) through global research collaborations with surgeon researchers and industry scientists.</div></div><div><h3>Results</h3><div>We provide the first resource library of annotation guides for 10 common robotic procedures consistent with our proposed temporal annotation framework, enabling consistent annotations for clinicians and large-scale data comparisons with computer-readable examples. These have been used in over 13,000 annotated surgical cases globally, demonstrating reproducibility and broad applicability.</div></div><div><h3>Conclusion</h3><div>This resource library and accompanying ontology framework provide critical structure for standardized temporal segmentation in robotic surgery. This framework has been applied globally in private studies examining surgical objective performance metrics, surgical education, workflow characterization, outcome prediction, algorithms for surgical activity recognition, and more. Adoption of these resources will unify clinical, academic, and industry efforts, ultimately catalyzing transformational advancements in surgical practice.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109429","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":"How is Engagement Defined Across Health Care Services and Technology Companies? A Systematic Review","authors":"Sanjay Basu MD, PhD ,&nbsp;Ariela Simerman BA ,&nbsp;Ari Hoffman MD","doi":"10.1016/j.mcpdig.2025.100256","DOIUrl":"10.1016/j.mcpdig.2025.100256","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically examine how digital health startups define and operationalize engagement in the post- coronavirus disease environment (2020-2025).</div></div><div><h3>Patients and Methods</h3><div>Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines adapted for web-based literature, we systematically reviewed publicly available information from digital health startups founded or significantly operating between 2020-2025. We extracted engagement definitions from company websites, white papers, blog posts, and press releases. Definitions were coded by type (explicit, implicit, or nondefinition) and dimensional focus (behavioral, cognitive, affective, and social). Inter-rater reliability was assessed using Cohen’s κ (κ=0.82). We conducted this systematic review from April 20, 2025, to May 21, 2025.</div></div><div><h3>Results</h3><div>We analyzed 64 engagement definitions from 30 digital health startups. Only 18.8% (n=12) were explicit definitions with clear measurement criteria, whereas 45.3% (n=29) were implicit definitions and 35.9% (n=23) were nondefinitions that mentioned engagement without defining it. The behavioral dimension dominated (64.1%, n=41), followed by social (28.1%, n=18), cognitive (21.9%, n=14), and affective dimensions (17.2%, n=11). Statistical analysis revealed significant associations between definition type and dimensional focus (<em>P</em>&lt;.05). Based on our findings, we developed a taxonomy of engagement definitions and a 5-level engagement definition maturity model.</div></div><div><h3>Conclusion</h3><div>Digital health startups predominantly use implicit or undefined engagement concepts with a strong behavioral focus. The proposed taxonomy and maturity model provide frameworks for standardizing engagement definitions across the digital health ecosystem, potentially improving measurement consistency, facilitating more meaningful comparisons between solutions, and establishing a baseline for evaluating effectiveness.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908449","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":"iCardio: The Brazilian Population-Based Real-World Data Platform for Cardiovascular Disease","authors":"Miriam Allein Zago Marcolino PhD ,&nbsp;Ana Paula Beck da Silva Etges PhD ,&nbsp;Luciana Rodrigues de Lara MBA ,&nbsp;Nayê Balzan Schneider Msc ,&nbsp;Yohan Casiraghi MD ,&nbsp;Wanderson Maia Da Silva MD ,&nbsp;Carisi Anne Polanczyk ScD","doi":"10.1016/j.mcpdig.2025.100255","DOIUrl":"10.1016/j.mcpdig.2025.100255","url":null,"abstract":"<div><div>Technological advances that contribute to improving organizations and systems’ capability to manage care services and pathways are impactful in improving efficiency and reducing waste in health care. This narrative paper presents the implementation of iCardio, a dashboard of population real-world data-based analytical online open-access solution for the cardiovascular field in Brazil. The platform was developed using hospitalization data from patients who underwent cardiovascular operation or interventional procedures, identified by procedure codes reimbursed by the public health system. Patient-level data from hospital and mortality systems were provided by the Brazilian Ministry of Health, cleaned, and organized into individual-level and hospitalization-level datasets to enable parameter calculation. A web-based solution was developed to provide user-friendly, interactive access to 17 indicators relevant to evaluating cardiovascular service efficiency, quality, and equity. Data from 291,490 patients with 317,338 index hospitalizations and 375,809 procedures (172,874 of cardiovascular operations and 202,935 of interventional cardiology) performed in 558 health care centers in Brazil compose the dataset behind the platform. The platform offers 4 analytical views: “patients,’ profile,’’ “by location,’’ “procedure rates,’’ and “detailed exploration,’’ displaying data by year (2019-2020) with multiple stratification options (eg, patient characteristics, procedures, health care centers, and geography). The iCardio is an online open-access platform based on real-world data that provides ready-to-use information about cardiovascular care in Brazil, which can be used as a transformative tool to sustain data-driven health policies and research in the cardiovascular field in Brazil.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861047","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":"Digital Health Rights, Adult Systems: Are We Leaving Adolescents Behind?","authors":"Louisa Peralta PhD ,&nbsp;Cristyn Davies PhD ,&nbsp;Kellie Burns PhD ,&nbsp;Leena Paakkari PhD","doi":"10.1016/j.mcpdig.2025.100254","DOIUrl":"10.1016/j.mcpdig.2025.100254","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813863","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":"Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.","authors":"Isaiah Z Yao, Min Dong, William Y K Hwang","doi":"10.1016/j.mcpdig.2025.100253","DOIUrl":"10.1016/j.mcpdig.2025.100253","url":null,"abstract":"<p><p>Deep learning (DL) has revolutionized cancer detection accuracy, speed, and accessibility. Leveraging sophisticated algorithms, DL has demonstrated transformative potential across diverse applications, including imaging-based diagnostics and genomic analysis, ultimately leading to better detection, improved patient treatment outcomes, and decreased overall mortality rates. Despite its promise, integrating DL into clinical practice presents substantial challenges, including limitations in data quality and standardization, as well as ethical and regulatory concerns, and the need for model interpretability and transparency. This review provides a comprehensive analysis of recent research (2018-2024) retrieved from PubMed and IEEE Xplore databases, encompassing 1304 studies from PubMed and 115 from IEEE, to highlight the current applications, opportunities, and challenges of DL in oncology. Additionally, this paper explores emerging solutions, including federated learning, explainable artificial intelligence, and synthetic data generation, to address these barriers. The review also emphasizes the importance of interdisciplinary collaboration, the integration of next-generation artificial intelligence techniques, and the adoption of multimodal data approaches to improve diagnostic precision and support personalized cancer treatment. By systematically analyzing key developments and challenges, this review aims to guide future research and DL technologies in oncology, promoting equitable and impactful advancements in cancer care.</p>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"100253"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Automated Mobile Cognitive Test for the Identification of Cognitive Impairment: A Cross-sectional Feasibility and Diagnostic Study","authors":"Louis Y. Tee MD, PhD ,&nbsp;Li Feng Tan MBBS ,&nbsp;Santhosh Seetharaman MBBS ,&nbsp;Lian Leng Low MBBS ,&nbsp;Zhi Peng Ong BS ,&nbsp;Munirah Bashil BS ,&nbsp;Hock Hai Teo PhD","doi":"10.1016/j.mcpdig.2025.100252","DOIUrl":"10.1016/j.mcpdig.2025.100252","url":null,"abstract":"<div><h3>Objective</h3><div>To develop Digital Processing Speed Test (DPST), a free, automated, multilingual, artificial intelligence–based cognitive testing application, with the aim to enhance recognition of cognitive impairment in underserved communities by leveraging mobile health to improve cognitive testing’s accessibility.</div></div><div><h3>Patients and Methods</h3><div>In this cross-sectional feasibility and diagnostic study, we determined the test performance of DPST for the identification of mild cognitive impairment (MCI) and dementia, compared with traditional cognitive tests, such as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The study was conducted from January 19, 2021, to November 12, 2023. In total, 476 adult participants were recruited by consecutive sampling at waiting areas of primary and secondary care clinics. The participants completed MMSE and MoCA with trained assessors and then performed DPST independently on a mobile device. The reference standard was a clinical diagnosis of MCI/dementia by a memory specialist blinded to the DPST score.</div></div><div><h3>Results</h3><div>Area under the receiver operating characteristic curve analyses showed that area under the curves were similar for the 3 tests (MMSE, 0.862; MoCA, 0.888; DPST, 0.861). Likewise, sensitivity (DPST, 85.2%; MMSE, 85.2%; MoCA, 90.2%), negative likelihood ratio (DPST, 0.197; MMSE, 0.193; MoCA, 0.129), specificity (DPST, 75.0%; MMSE, 76.5%; MoCA, 76.2%), and positive likelihood ratio (DPST, 3.41; MMSE, 3.62; MoCA, 3.79) were similar.</div></div><div><h3>Conclusion</h3><div>Digital Processing Speed Test, a free, automated, multilingual cognitive test conducted on a mobile device, has similar test performance to MMSE and MoCA. Nonetheless, DPST does not capture the multidomain cognitive deficits that characterize MCI/dementia. Moreover, test-retest reliability and interrater agreement of artificial intelligence–based handwriting recognition needs further confirmation.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756917","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":"Designing Artificial Intelligence-Powered Health Care Assistants to Reach Vulnerable Populations: A Discrete Choice Experiment Among South African University Students","authors":"Amy Zheng MPH ,&nbsp;Lawrence Long PhD ,&nbsp;Caroline Govathson MSc ,&nbsp;Candice Chetty-Makkan PhD ,&nbsp;Sarah Morris BS ,&nbsp;Dino Rech MBA ,&nbsp;Matthew P. Fox DSc ,&nbsp;Sophie Pascoe PhD","doi":"10.1016/j.mcpdig.2025.100248","DOIUrl":"10.1016/j.mcpdig.2025.100248","url":null,"abstract":"<div><h3>Objective</h3><div>To understand what preferences are important to university students in South Africa when engaging with a hypothetical artificial intelligence-powered health care assistant (AIPHA) to access health information using a discrete choice experiment.</div></div><div><h3>Patients and Methods</h3><div>We conducted an unlabeled, forced choice discrete choice experiment among adult South African university students through Prolific, an online research platform, from June 26, 2024 to August 31, 2024. Each choice option described a hypothetical AIPHA using 8 attribute characteristics (cost, confidentiality, security, health care topics, language, persona, access, and services). Participants were presented with 10 choice sets each comprised of 2 choice options and asked to choose between the 2. A conditional logit model was used.</div></div><div><h3>Results</h3><div>Three hundred participants were recruited and enrolled. Most participants were Black, born in South Africa, heterosexual, working for a wage, and had a mean age of 26.5 years (SD, 6.0). Language, security, and receiving personally tailored advice were the most important attributes for AIPHA. Participants strongly preferred the ability to communicate with the AIPHA in any South African language of their choosing instead of only English and receive information about health topics specific to their context including information on clinics geographically near them. The results were consistent when stratified by sex and socioeconomic status.</div></div><div><h3>Conclusion</h3><div>Participants had strong preferences for security and language, which is in line with previous studies where successful uptake and implementation of such health interventions clearly addressed these concerns. These results build the evidence base for how we might engage young adults in health care through technology effectively.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748792","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":"Comparing Machine Learning and Nurse Predictions for Hospital Admissions in a Multisite Emergency Care System","authors":"Jonathan Nover MBA, RN ,&nbsp;Matthew Bai MD ,&nbsp;Prem Tismina MS ,&nbsp;Ganesh Raut MS ,&nbsp;Dhavalkumar Patel MS ,&nbsp;Girish N. Nadkarni MD, MPH ,&nbsp;Benjamin S. Abella MD, MPhil ,&nbsp;Eyal Klang MD ,&nbsp;Robert Freeman DNP, RN, NE-BC","doi":"10.1016/j.mcpdig.2025.100249","DOIUrl":"10.1016/j.mcpdig.2025.100249","url":null,"abstract":"<div><h3>Objective</h3><div>To prospectively compare nurse predictions with a machine learning (ML) model for hospital admissions and evaluate whether adding the nurse prediction to ML outputs enhances predictive performance.</div></div><div><h3>Patients and Methods</h3><div>In this prospective, observational study at 6 hospitals in a large mixed quaternary/community emergency department (ED) system (annual ED census ∼500,000), triage nurses recorded a binary admission prediction for adult patients. These predictions were compared with an ensemble ML model (XGBoost + Bio-Clinical BERT) trained on structured data (demographics, vital signs, and medical history) and triage text. Nurse predictions were similarly analyzed and then integrated with the ML output to assess for improvement in predictive accuracy.</div></div><div><h3>Results</h3><div>The ensemble ML model (XGBoost + Bio-Clinical BERT) was trained on 1.8 million historical ED visits (January 2019 to December 2023). It was then tested on 46,912 prospective ED visits with recorded nurse predictions (September 1, 2024 to October 31, 2024). In the prospective arm, nurse predictions yielded an accuracy of 81.6% (95% CI, 81.3-81.9), a sensitivity of 64.8% (63.7-65.8), and a specificity of 85.7% (85.3-86.0). At a 0.30 probability threshold, the ML model attained an accuracy of 85.4% (85.0-85.7) and a sensitivity of 70.8% (69.8-71.7). Combining nurse predictions with the ML output did not improve accuracy beyond the model alone.</div></div><div><h3>Conclusion</h3><div>Machine learning-based predictions outperformed triage nurse estimates for hospital admissions. These findings suggest that an admission prediction system anchored by ML can perform reliably using data available at triage.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739648","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}