Mayo Clinic Proceedings. Digital health最新文献

{"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 ,&nbsp;Bettina McMahon MBA ,&nbsp;Tim Price MS ,&nbsp;Zhixin Liu PhD ,&nbsp;Aleksandra Kabat-Karabon MS ,&nbsp;Maria Marecka MD ,&nbsp;Mitchell Burger MPH ,&nbsp;Lijing Ma PhD ,&nbsp;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>&lt;.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>&lt;.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>&lt;.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":"2026-03-01","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}

{"title":"Electronic Health Records and Their Role in the Surveillance of Infectious Disease","authors":"Dimple Yasvin Chudasama PhD,&nbsp;Theresa Lamagni PhD,&nbsp;Colin Brown MRCP,&nbsp;Russell Hope PhD","doi":"10.1016/j.mcpdig.2025.100307","DOIUrl":"10.1016/j.mcpdig.2025.100307","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100307"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791822","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":"The Effectiveness of Digital Therapeutics Intervention in Oral Anticoagulation Management: A Systematic Review and Meta-analysis","authors":"Jiayue Guo MPH ,&nbsp;Lili You PhD ,&nbsp;Lu Liu MPH ,&nbsp;Xitong Jiao MPH ,&nbsp;Debasish Kar MD ,&nbsp;Jitendra Jonnagaddala MD, PhD","doi":"10.1016/j.mcpdig.2026.100336","DOIUrl":"10.1016/j.mcpdig.2026.100336","url":null,"abstract":"<div><h3>Objective</h3><div>To summarize the key intervention characteristics and evaluate the effectiveness and safety of digital therapeutics (DTx) in patients receiving oral anticoagulation, with effectiveness evaluated using time in therapeutic range (TTR), thromboembolic events, and mortality, and safety evaluated based on bleeding events.</div></div><div><h3>Patients and Methods</h3><div>We searched PubMed, Embase, Web of Science, and the Cochrane Library from inception to June 20, 2025, and identified 10 randomized controlled trials involving 7237 patients. The criteria required studies to assess software-based DTx supporting anticoagulation management and report effectiveness or safety outcomes. Study quality was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation framework, and random-effects models were applied.</div></div><div><h3>Results</h3><div>Digital therapeutics interventions were associated with a lower incidence of major bleeding than usual care: no clear differences in TTR, thromboembolic events, or mortality. Evidence quality ranged from very low to high. Secondary analyses showed more international normalized ratio testing with DTx; rehospitalization rates did not differ significantly between the groups. Sensitivity analysis changed TTR effect after excluding a study with enhanced control, but other outcomes remained unchanged.</div></div><div><h3>Conclusion</h3><div>Digital therapeutics interventions for anticoagulation management improve safety outcomes, particularly reducing major bleeding, and with greater monitoring intensity. Larger, long-term trials are needed to confirm the clinical benefits and evaluate cost-effectiveness.</div></div><div><h3>Trial Registration</h3><div>PROSPERO Identifier: CRD420251107441.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100336"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328399","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":"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":"2026-03-01","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}

{"title":"Diagnostic Accuracy of Clinical Decision Support Systems ORADIII and ORAD DDx to Histopathological Diagnosis of Jaw Lesions","authors":"Harleen Bali MDS ,&nbsp;Dashrath Kafle MDS ,&nbsp;Sagar Adhikari MDS ,&nbsp;Nitesh Kumar Chaurasia MDS ,&nbsp;Pratibha Poudel MDS ,&nbsp;Bhoj Raj Adhikari PhD ,&nbsp;Garima Adhikari BDS ,&nbsp;Sachita Thapa MDS","doi":"10.1016/j.mcpdig.2025.100306","DOIUrl":"10.1016/j.mcpdig.2025.100306","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate and compare the diagnostic performance of 2 clinical decision support system tools—ORADIII and ORAD DDx—against histopathological diagnosis in identifying intrabony jaw lesions using orthopantomograms.</div></div><div><h3>Patients and Methods</h3><div>A diagnostic accuracy, cross-sectional study was conducted in the Department of Oral Medicine and Radiology, Kathmandu University School of Medical Sciences, Dhulikhel Hospital, Kavre, Nepal, from January 1, 2025, to April 30, 2025, after institutional review committee approval. The study was conducted on a sample comprising both lesion and nonlesion cases based on radiographic evaluation. Diagnostic outputs from ORADIII and ORAD DDx were compared with histopathology. Key performance indicators—including sensitivity, specificity, accuracy, F1 score, positive predictive value, negative predictive value, and likelihood ratios (positive and negative)—were calculated for both systems.</div></div><div><h3>Results</h3><div>Among the 350 samples evaluated, including 175 lesion positive and 175 nonlesion cases, ORAD DDx demonstrated superior diagnostic performance compared with ORADIII. The sensitivity, specificity, accuracy, and F1 score for ORADIII were 64.57%, 60.00%, 62.28%, and 0.6314, respectively. In contrast, ORAD DDx achieved sensitivity, specificity, accuracy, and F1 score of 70.29%, 65.71%, 68.00%, and 0.687, respectively.</div></div><div><h3>Conclusion</h3><div>ORAD DDx showed better diagnostic performance than ORADIII across most metrics, indicating its potential as a more reliable clinical decision support system for diagnosis decision support for intrabony jaw lesions. This could also be due to its categorizing of lesions and variations. Further validation with larger, stratified, and multicenter data sets is recommended.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100306"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705855","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":"State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI","authors":"Cole J. Cook PhD ,&nbsp;Jason R. Klug PhD ,&nbsp;Blaize W. Kandler MS ,&nbsp;Abraham Baez-Suarez PhD ,&nbsp;Adam P. Dachowicz PhD ,&nbsp;Daniel J. Blezek PhD ,&nbsp;Andrew D. Missert PhD ,&nbsp;Gian Marco Conte MD, PhD ,&nbsp;Justin D. Benfield MS ,&nbsp;Amanda Mensing-Diggs MS ,&nbsp;Matthew T. Edwards BA ,&nbsp;Michele A. Powell BAS ,&nbsp;Emily N. Sheedy MBA ,&nbsp;Holly M. Meyer MS ,&nbsp;Joseph Melnick BS ,&nbsp;Bryce F. Flor BS ,&nbsp;David E. Vidal JD ,&nbsp;Vera Sorin MD ,&nbsp;B. Selnur Erdal PhD ,&nbsp;Steve G. Langer PhD ,&nbsp;Timothy L. Kline PhD","doi":"10.1016/j.mcpdig.2026.100342","DOIUrl":"10.1016/j.mcpdig.2026.100342","url":null,"abstract":"<div><div>Articles on the development of medical image artificial intelligence (AI) algorithms are numerous in the literature, but deployment to clinical practice is infrequently discussed. The Enterprise Radiology Framework for AI Software Technology Team at Mayo Clinic has been focused on bridging the gap in clinical translation of medical image AI algorithms since its inception in 2019. During this time, we have released 17 algorithms into our radiology clinical practice. Recently, we have placed an increased focus on monitoring these algorithms, as there are few reports with practical experience documented in the literature. Our increased monitoring efforts include daily, weekly, and yearly monitoring of utilization, failure modes, data drift, and end-user feedback through automated alerts, dedicated dashboards, and pointed investigations to enable optimal algorithmic processing. End-user feedback is elicited yearly during annual reviews to ensure clinical needs are still being met. Automated monitoring has enabled earlier identification of problems, such as images no longer routing through the orchestration engine to the appropriate algorithm, minimizing potential disruption to the clinical practice and ensuring continued algorithmic utilization. Monitoring has also reinforced the importance of key aspects of interdisciplinary research and translation, such as early discussions on clinical needs coupled with technological ability and proper training. By providing our experience in and continuing to improve monitoring methods as a community, we can all minimize risk and maximize the benefits of medical pixel-based AI.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100342"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147379958","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":"Granular Machine Learning-Based Computed Tomography Contrast Phase Prediction","authors":"S. Moein Rassoulinejad-Mousavi PhD ,&nbsp;Bardia Khosravi MD, MPH, MHPE ,&nbsp;Alex D. Weston PhD ,&nbsp;Ryan T. Moerer BSc ,&nbsp;Aaron E. Carretero Benites BSc ,&nbsp;Hillary W. Garner MD ,&nbsp;Naoki Takahashi MD ,&nbsp;Timothy L. Kline PhD ,&nbsp;Michael F. Romero PhD ,&nbsp;John C. Lieske MD ,&nbsp;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":"2026-03-01","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}

{"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 ,&nbsp;Charlotte Kelley Jones PhD ,&nbsp;Babak Jamshidi PhD ,&nbsp;Angie A. Kehagia PhD ,&nbsp;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>&lt;.001), less concerning (<em>P</em>&lt;.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":"2026-03-01","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}

{"title":"Leveraging Artificial Intelligence to Enhance Oncology Care Delivery in Sub-Saharan Africa","authors":"Jasmine G. Hughes MA, MScGH ,&nbsp;Ketan Tamirisa ,&nbsp;Ammar Dharani BS ,&nbsp;Faraan Rahim BS ,&nbsp;Erick Mahatara ,&nbsp;Ntuli A. Kapologwe MD, RCPE, PhD ,&nbsp;Almotasm Ben Hmeda MD","doi":"10.1016/j.mcpdig.2026.100337","DOIUrl":"10.1016/j.mcpdig.2026.100337","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100337"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230215","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":"Evaluation of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Incidental Pulmonary Nodules: The CREATE Study","authors":"Deniz Koksal MD ,&nbsp;Arunkumar Govindarajan MD ,&nbsp;Hari Kishan Gonuguntla MD ,&nbsp;Sibel Nayci MD ,&nbsp;Ricardo Cordova MD ,&nbsp;Mohamed Helmy Zidan MD ,&nbsp;Susan McCutcheon PhD ,&nbsp;Ashwini Saha MBBS ,&nbsp;Pushpalatha Kantharaju BAMS ,&nbsp;Sagar Sen BTech ,&nbsp;Rohitashva Agrawal MPH ,&nbsp;Laksmi Wulandari PhD","doi":"10.1016/j.mcpdig.2026.100335","DOIUrl":"10.1016/j.mcpdig.2026.100335","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the effectiveness of the artificial intelligence–based qXR lung nodule malignancy score (qXR-LNMS) in detecting high-risk incidental pulmonary nodules (IPNs) on chest X-rays (CXRs).</div></div><div><h3>Patients and Methods</h3><div>The CREATE (NCT05817110), a prospective, observational study for participants aged 35 years or older with IPN (size, ≥8 to ≤30 mm) on CXR, enrolled 712 participants (high-risk: 498 and low-risk: 214) between April 1, 2023, and December 31, 2024. Participants were flagged by the Food and Drug Administration–cleared qXR detection algorithm and confirmed by radiologists. Threshold for success was set at 20% for positive predictive value (PPV) and 70% for negative predictive value (NPV). The primary and secondary outcomes included PPV and NPV of qXR-LNMS against the risk of malignancy assessed by radiologists using low-dose computed tomography (LDCT) and binarized risk categories based on Lung-RADS score and Mayo Clinic model and PPVs and NPVs by clinicodemographic characteristics with 95% CIs using Wilson score method.</div></div><div><h3>Results</h3><div>Overall, the PPV and the NPV of qXR-LNMS risk prediction against radiologists’ assessment on LDCT were 54.2% (95% CI, 49.8-58.5) and 93.5% (95% CI, 89.3-96.1), respectively. The agreement between Mayo Clinic model and qXR-LNMS was observed in 70.6% participants (Spearman correlation, 0.247). Results across key subgroups were consistent with all PPV and NPV point estimates crossing the prespecified threshold.</div></div><div><h3>Conclusion</h3><div>The results demonstrate the potential of qXR-LNMS in predicting benign and malignant IPN on CXR, thereby supporting lung cancer screening, particularly in resource-limited settings, although further validation is needed.</div></div><div><h3>Trials Registration</h3><div><span><span>clinicaltrials.gov</span><svg><path></path></svg></span> Identifier: NCT05817110</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100335"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230117","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}