Pub Date : 2025-12-26DOI: 10.1016/j.mcpdig.2025.100334
Dominik Naumann MD , Tatjana Amler MSc , Doreen Schoeppenthau MD , Sergej Holzmann MSc , Jörg Preißinger PhD , Matthias Franz PhD , Heyo K. Kroemer PhD , Alexander Meyer MD
Cardiovascular and chronic disease prevention remains limited by episodic, clinic-based assessments that fail to capture physiological changes arising in daily life. As mobility constitutes one of the most stable and repetitive environments people inhabit, vehicles offer a unique setting for subliminal, continuous health monitoring. This narrative presents the rationale and foundational framework for Automotive Health 2.0, a clinically oriented paradigm that transforms connected vehicles into validated platforms for physiological sensing, data integration, and proactive care delivery. Building on existing in-cabin cameras, radar, and microphones, multimodal algorithms enable unobtrusive estimation of cardiovascular, respiratory, and behavioral parameters during routine driving. Technological innovation lies in combining these signals with artificial intelligence-driven analytics to detect early disease signatures, support dynamic risk assessment, and enable adaptive telemonitoring directly linked to electronic health records. Clinically, this approach distinguishes regulatory-grade monitoring from consumer wellness tools by prioritizing accuracy, reproducibility, and integration with established workflows. Patients gain earlier detection and more equitable access to preventive care; clinicians receive continuous actionable data, and health systems benefit from scalable population-level monitoring. Automotive Health 2.0 positions the vehicle as a novel extension of the health care ecosystem, embedding validated prevention seamlessly into everyday life.
{"title":"Automotive Health 2.0: Steering Toward Proactive Preventive Care","authors":"Dominik Naumann MD , Tatjana Amler MSc , Doreen Schoeppenthau MD , Sergej Holzmann MSc , Jörg Preißinger PhD , Matthias Franz PhD , Heyo K. Kroemer PhD , Alexander Meyer MD","doi":"10.1016/j.mcpdig.2025.100334","DOIUrl":"10.1016/j.mcpdig.2025.100334","url":null,"abstract":"<div><div>Cardiovascular and chronic disease prevention remains limited by episodic, clinic-based assessments that fail to capture physiological changes arising in daily life. As mobility constitutes one of the most stable and repetitive environments people inhabit, vehicles offer a unique setting for subliminal, continuous health monitoring. This narrative presents the rationale and foundational framework for Automotive Health 2.0, a clinically oriented paradigm that transforms connected vehicles into validated platforms for physiological sensing, data integration, and proactive care delivery. Building on existing in-cabin cameras, radar, and microphones, multimodal algorithms enable unobtrusive estimation of cardiovascular, respiratory, and behavioral parameters during routine driving. Technological innovation lies in combining these signals with artificial intelligence-driven analytics to detect early disease signatures, support dynamic risk assessment, and enable adaptive telemonitoring directly linked to electronic health records. Clinically, this approach distinguishes regulatory-grade monitoring from consumer wellness tools by prioritizing accuracy, reproducibility, and integration with established workflows. Patients gain earlier detection and more equitable access to preventive care; clinicians receive continuous actionable data, and health systems benefit from scalable population-level monitoring. Automotive Health 2.0 positions the vehicle as a novel extension of the health care ecosystem, embedding validated prevention seamlessly into everyday life.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100334"},"PeriodicalIF":0.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977607","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}
Pub Date : 2025-12-19DOI: 10.1016/j.mcpdig.2025.100331
George A. Gellert MD, MPH, MPA , Bettina McMahon MBA , Tim Price MS , Zhixin Liu PhD , Aleksandra Kabat-Karabon MS , Maria Marecka MD , Mitchell Burger MPH , Lijing Ma PhD , Nirvana Luckraj MD
Objective
To evaluate whether an artificial intelligence–based national virtual triage and care referral (VTCR) service in Australia improved care acuity level alignment, increased patient engagement of telemedicine services, and reduced emergency department demand by offering lower acuity, less costly options for urgent, virtual, or in-person care services.
Patients and Methods
Cross-sectional analyses examined changes in patient care intent following VTCR to determine whether it facilitated patient adoption of new emergency and nonurgent telemedicine and virtual care services.
Results
Virtual triage and care referral more than doubled the number of patients selecting appropriate, lower acuity nonurgent care from 330,279 (21.3%) to 820,800 (52.9%), an increase of 31.6 percentage points [PPs] (P<.01), and effectively eliminated uncertainty in patient care seeking from 670,502 to 2557 patients, a decrease of 99.6%. Intent for in-person emergency care fell significantly from 119,414 (36.7%) to 105,349 patients (24.6%) (–12.1 PP; P<.01), replaced by substantial growth in patient intent to use virtual emergency care (from 612 to 11,840 patients or +10.1 PP) and nonurgent virtual care use (from 20,467 to 26,289 patients or +2.9 PP) (P<.01). Victoria, a state within Australia, recorded the highest uptake. Extrapolated nationally, these shifts could prevent an estimated 2409 unnecessary in-person nonurgent visits and 19,286 unnecessary emergency department visits annually in Australia. Aboriginal and Indigenous patients showed similar benefits and engaged VTCR at higher rates than other patients.
Conclusion
Artificial intelligence–based VTCR improved alignment between patient perceived needs and recommended care pathways, not only driving greater use of appropriate, lower acuity, and telemedicine services but also reducing unnecessary in-person emergency visits. By eliminating uncertainty in care seeking and advancing adoption of new virtual emergency and nonurgent care options, VTCR offers a scalable, evidence-based solution for optimizing emergent and urgent care delivery and easing pressure on emergency departments across Australia.
{"title":"Increased Utilization of Telemedical Emergency and Nonurgent Care Following Deployment of Virtual Triage and Care Referral in Australia","authors":"George A. Gellert MD, MPH, MPA , Bettina McMahon MBA , Tim Price MS , Zhixin Liu PhD , Aleksandra Kabat-Karabon MS , Maria Marecka MD , Mitchell Burger MPH , Lijing Ma PhD , Nirvana Luckraj MD","doi":"10.1016/j.mcpdig.2025.100331","DOIUrl":"10.1016/j.mcpdig.2025.100331","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate whether an artificial intelligence–based national virtual triage and care referral (VTCR) service in Australia improved care acuity level alignment, increased patient engagement of telemedicine services, and reduced emergency department demand by offering lower acuity, less costly options for urgent, virtual, or in-person care services.</div></div><div><h3>Patients and Methods</h3><div>Cross-sectional analyses examined changes in patient care intent following VTCR to determine whether it facilitated patient adoption of new emergency and nonurgent telemedicine and virtual care services.</div></div><div><h3>Results</h3><div>Virtual triage and care referral more than doubled the number of patients selecting appropriate, lower acuity nonurgent care from 330,279 (21.3%) to 820,800 (52.9%), an increase of 31.6 percentage points [PPs] (<em>P</em><.01), and effectively eliminated uncertainty in patient care seeking from 670,502 to 2557 patients, a decrease of 99.6%. Intent for in-person emergency care fell significantly from 119,414 (36.7%) to 105,349 patients (24.6%) (–12.1 PP; <em>P</em><.01), replaced by substantial growth in patient intent to use virtual emergency care (from 612 to 11,840 patients or +10.1 PP) and nonurgent virtual care use (from 20,467 to 26,289 patients or +2.9 PP) (<em>P</em><.01). Victoria, a state within Australia, recorded the highest uptake. Extrapolated nationally, these shifts could prevent an estimated 2409 unnecessary in-person nonurgent visits and 19,286 unnecessary emergency department visits annually in Australia. Aboriginal and Indigenous patients showed similar benefits and engaged VTCR at higher rates than other patients.</div></div><div><h3>Conclusion</h3><div>Artificial intelligence–based VTCR improved alignment between patient perceived needs and recommended care pathways, not only driving greater use of appropriate, lower acuity, and telemedicine services but also reducing unnecessary in-person emergency visits. By eliminating uncertainty in care seeking and advancing adoption of new virtual emergency and nonurgent care options, VTCR offers a scalable, evidence-based solution for optimizing emergent and urgent care delivery and easing pressure on emergency departments across Australia.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100331"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023108","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}
Pub Date : 2025-12-19DOI: 10.1016/j.mcpdig.2025.100332
S. Moein Rassoulinejad-Mousavi PhD , Bardia Khosravi MD, MPH, MHPE , Alex D. Weston PhD , Ryan T. Moerer BSc , Aaron E. Carretero Benites BSc , Hillary W. Garner MD , Naoki Takahashi MD , Timothy L. Kline PhD , Michael F. Romero PhD , John C. Lieske MD , Bradley J. Erickson MD, PhD
Objective
To develop and evaluate a machine learning framework that detects intravenous contrast and distinguishes eight granular renal contrast phases on abdominal computed tomography (CT) scans to improve renal assessment.
Patients and Methods
This retrospective study included abdominal CT scans obtained at Mayo Clinic from January 1, 2001, to December 31, 2009. In total, 3033 scans from 1017 patients with renal cell carcinoma were included. A ConvNeXt-Femto deep learning (DL) model with dual output heads was trained for contrast detection and renal contrast phase prediction using binary classification and regression objectives, respectively. A random forest (RF) regression model was trained on DL-extracted features to predict 8 fine-grained phases spanning early to late corticomedullary, nephrographic, and pyelographic. Model performance was further evaluated using an internal-external cohort of abdominal CT scans from January 1, 2010, to December 31, 2020, comprising of 8856 series from 4760 patients.
Results
The DL classifier detected contrast presence with 100% accuracy. The DL-only regression model reached a mean absolute error of 0.34, compared with 0.29 for the hybrid DL+RF model. Agreement analysis between the models’ ensemble and 2 radiologists reported reliability, with κ values of 0.86 for predicting the exact category, 1.00 for neighboring categories, and 0.98 for super-category grouping. Internal-external validation indicated that the model successfully operated across datasets differing in patient cohort and imaging characteristics.
Conclusion
This DL+RF framework enables automated granular renal contrast phase discrimination and reduces inter-rater variability, representing a meaningful advancement in artificial intelligence-assisted abdominal CT interpretation and supporting improved patient care.
{"title":"Granular Machine Learning-Based Computed Tomography Contrast Phase Prediction","authors":"S. Moein Rassoulinejad-Mousavi PhD , Bardia Khosravi MD, MPH, MHPE , Alex D. Weston PhD , Ryan T. Moerer BSc , Aaron E. Carretero Benites BSc , Hillary W. Garner MD , Naoki Takahashi MD , Timothy L. Kline PhD , Michael F. Romero PhD , John C. Lieske MD , Bradley J. Erickson MD, PhD","doi":"10.1016/j.mcpdig.2025.100332","DOIUrl":"10.1016/j.mcpdig.2025.100332","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and evaluate a machine learning framework that detects intravenous contrast and distinguishes eight granular renal contrast phases on abdominal computed tomography (CT) scans to improve renal assessment.</div></div><div><h3>Patients and Methods</h3><div>This retrospective study included abdominal CT scans obtained at Mayo Clinic from January 1, 2001, to December 31, 2009. In total, 3033 scans from 1017 patients with renal cell carcinoma were included. A ConvNeXt-Femto deep learning (DL) model with dual output heads was trained for contrast detection and renal contrast phase prediction using binary classification and regression objectives, respectively. A random forest (RF) regression model was trained on DL-extracted features to predict 8 fine-grained phases spanning early to late corticomedullary, nephrographic, and pyelographic. Model performance was further evaluated using an internal-external cohort of abdominal CT scans from January 1, 2010, to December 31, 2020, comprising of 8856 series from 4760 patients.</div></div><div><h3>Results</h3><div>The DL classifier detected contrast presence with 100% accuracy. The DL-only regression model reached a mean absolute error of 0.34, compared with 0.29 for the hybrid DL+RF model. Agreement analysis between the models’ ensemble and 2 radiologists reported reliability, with κ values of 0.86 for predicting the exact category, 1.00 for neighboring categories, and 0.98 for super-category grouping. Internal-external validation indicated that the model successfully operated across datasets differing in patient cohort and imaging characteristics.</div></div><div><h3>Conclusion</h3><div>This DL+RF framework enables automated granular renal contrast phase discrimination and reduces inter-rater variability, representing a meaningful advancement in artificial intelligence-assisted abdominal CT interpretation and supporting improved patient care.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100332"},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977609","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}
Pub Date : 2025-12-17DOI: 10.1016/j.mcpdig.2025.100330
Yixi Xu PhD, Rahul Dodhia PhD, Juan M. Lavista Ferres PhD, MS, William B. Weeks MD, PhD, MBA
{"title":"Artificial Intelligence Research as a Continuous Clinical Service","authors":"Yixi Xu PhD, Rahul Dodhia PhD, Juan M. Lavista Ferres PhD, MS, William B. Weeks MD, PhD, MBA","doi":"10.1016/j.mcpdig.2025.100330","DOIUrl":"10.1016/j.mcpdig.2025.100330","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100330"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926225","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}
Pub Date : 2025-12-17DOI: 10.1016/j.mcpdig.2025.100329
Lauren Gatting PhD , Charlotte Kelley Jones PhD , Babak Jamshidi PhD , Angie A. Kehagia PhD , Jo Waller PhD
Objective
To compare acceptability of 2 artificial intelligence (AI) use cases in the English National Health Servic Breast Screening Program.
Patients and Methods
From February 7 to March 14 2024, we conducted an online survey, randomizing participants to information about using AI either as the second mammogram reader or to triage mammograms. In the triage scenario, only higher-risk images would be reviewed by a human reader. The survey was completed by 3419 women aged 45 to 70 years, recruited from an online panel. The primary outcome was acceptability of the presented AI use case. We assessed a range of psychological and demographic factors. Regression modeling examined predictors of acceptability.
Results
Using AI as a second reader was rated as more acceptable (P<.001), less concerning (P<.001), and less likely to put people off screening (P=.001) than using it as a triage tool. In both groups, most women said AI would not affect their breast screening attendance (1251/1710 [73%] and 1195/1709 [70%] in the second reader and triage groups, respectively). Nevertheless, 15% (498/3419) of participants stated that the use of AI would make them less likely to attend. After adjusting for AI use case, acceptability was higher in respondents of older age, White ethnicity, higher education, greater AI knowledge, and with more positive attitudes toward both AI and breast screening.
Conclusion
Artificial intelligence in breast screening was rated as more acceptable if used alongside, rather than instead of, a human reader. Ongoing careful evaluation is needed to ensure its roll-out does not widen existing social inequalities and that the risk-benefit profile of screening is maintained.
{"title":"Acceptability of Using Artificial Intelligence in the National Health Service Breast Screening Program: A Randomized Online Survey of Screening-Eligible Women in England","authors":"Lauren Gatting PhD , Charlotte Kelley Jones PhD , Babak Jamshidi PhD , Angie A. Kehagia PhD , Jo Waller PhD","doi":"10.1016/j.mcpdig.2025.100329","DOIUrl":"10.1016/j.mcpdig.2025.100329","url":null,"abstract":"<div><h3>Objective</h3><div>To compare acceptability of 2 artificial intelligence (AI) use cases in the English National Health Servic Breast Screening Program.</div></div><div><h3>Patients and Methods</h3><div>From February 7 to March 14 2024, we conducted an online survey, randomizing participants to information about using AI either as the second mammogram reader or to triage mammograms. In the triage scenario, only higher-risk images would be reviewed by a human reader. The survey was completed by 3419 women aged 45 to 70 years, recruited from an online panel. The primary outcome was acceptability of the presented AI use case. We assessed a range of psychological and demographic factors. Regression modeling examined predictors of acceptability.</div></div><div><h3>Results</h3><div>Using AI as a second reader was rated as more acceptable (<em>P</em><.001), less concerning (<em>P</em><.001), and less likely to put people off screening (<em>P</em><em>=</em>.001) than using it as a triage tool. In both groups, most women said AI would not affect their breast screening attendance (1251/1710 [73%] and 1195/1709 [70%] in the second reader and triage groups, respectively). Nevertheless, 15% (498/3419) of participants stated that the use of AI would make them less likely to attend. After adjusting for AI use case, acceptability was higher in respondents of older age, White ethnicity, higher education, greater AI knowledge, and with more positive attitudes toward both AI and breast screening.</div></div><div><h3>Conclusion</h3><div>Artificial intelligence in breast screening was rated as more acceptable if used alongside, rather than instead of, a human reader. Ongoing careful evaluation is needed to ensure its roll-out does not widen existing social inequalities and that the risk-benefit profile of screening is maintained.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100329"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977608","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}
Pub Date : 2025-12-06DOI: 10.1016/j.mcpdig.2025.100309
Alfredo Di Giovanni MD
{"title":"Uncontrolled Semantic Adaptation in Clinical Evaluation of Large Language Models","authors":"Alfredo Di Giovanni MD","doi":"10.1016/j.mcpdig.2025.100309","DOIUrl":"10.1016/j.mcpdig.2025.100309","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100309"},"PeriodicalIF":0.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885150","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}
Pub Date : 2025-12-05DOI: 10.1016/j.mcpdig.2025.100310
Ian Io Lei MD , Wojciech Marlicz MD, PhD , Ramesh P. Arasaradnam MD, PhD , Anastasios Koulaouzidis MD, PhD
{"title":"Clinical Reformation in the Age of Artificial Intelligence: Safeguarding the Ethical Centre of Medicine","authors":"Ian Io Lei MD , Wojciech Marlicz MD, PhD , Ramesh P. Arasaradnam MD, PhD , Anastasios Koulaouzidis MD, PhD","doi":"10.1016/j.mcpdig.2025.100310","DOIUrl":"10.1016/j.mcpdig.2025.100310","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841148","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}
Pub Date : 2025-12-01DOI: 10.1016/j.mcpdig.2025.100289
Weiguo Cao PhD , Jianfu Li PhD , Cui Tao PhD
{"title":"DD-NeuralNet: Dual-Domain Neural Network for Enhanced Multi-Lead ECG Decision Support","authors":"Weiguo Cao PhD , Jianfu Li PhD , Cui Tao PhD","doi":"10.1016/j.mcpdig.2025.100289","DOIUrl":"10.1016/j.mcpdig.2025.100289","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100289"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789729","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}
Pub Date : 2025-12-01DOI: 10.1016/j.mcpdig.2025.100284
Chris Varghese MBChB, BMedSc(Hons) , Elizabeth B. Habermann PhD , Kristine T. Hanson MPH , Ashok Choudhary PhD , Hojjat Salehinejad PhD , Cornelius A. Thiels DO, MBA
{"title":"Foundation Models as a New Portable Standard in Local Risk Stratification for Emergency Surgery","authors":"Chris Varghese MBChB, BMedSc(Hons) , Elizabeth B. Habermann PhD , Kristine T. Hanson MPH , Ashok Choudhary PhD , Hojjat Salehinejad PhD , Cornelius A. Thiels DO, MBA","doi":"10.1016/j.mcpdig.2025.100284","DOIUrl":"10.1016/j.mcpdig.2025.100284","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100284"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789797","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}
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