Mayo Clinic Proceedings. Digital health最新文献

{"title":"Development and Evaluation of an Artificial Intelligence–Powered Surgical Oral Examination Simulator: A Pilot Study","authors":"Arya S. Rao BA ,&nbsp;Siona Prasad BA ,&nbsp;Richard S. Lee BS ,&nbsp;Susan Farrell MD ,&nbsp;Sophia McKinley MD, MED ,&nbsp;Marc D. Succi MD","doi":"10.1016/j.mcpdig.2025.100241","DOIUrl":"10.1016/j.mcpdig.2025.100241","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and validate an artificial intelligence–powered platform that simulates surgical oral examinations, addressing the limitations of traditional faculty-led sessions.</div></div><div><h3>Patients and Methods</h3><div>This cross-sectional study, conducted from June 1, 2024, through December 1, 2024, comprised technical validation and educational assessment of a novel large language model (LLM)–based surgical education tool (surgery oral examination large language model [SOE-LLM]). The study involved 12 surgical clerkship students completing their core rotation at a major academic medical center. The SOE-LLM, using MIMIC-IV–derived surgical cases (acute appendicitis and pancreatitis), was implemented to simulate oral examinations. Technical validation assessed performance across 8 domains: case presentation accuracy, physical examination findings, historical detail preservation, laboratory data reporting, imaging interpretation, management decisions, and recognition of contraindicated interventions. Educational utility was evaluated using a 5-point Likert scale.</div></div><div><h3>Results</h3><div>Technical validation showed the SOE-LLM’s ability to function as a consistent oral examiner. The model accurately guided students through case presentations, responded to diagnostic questions, and provided clinically sound responses based on MIMIC-IV cases. When tested with standardized prompts, it maintained examination fidelity, requiring proper diagnostic reasoning and differentiating operative versus medical management. Student evaluations highlighted the platform’s value as an examination preparation tool (mean, 4.250; SEM, 0.1794) and its ability to create a low-stakes environment for high-stakes decision practice (mean, 4.833; SEM, 0.1124).</div></div><div><h3>Conclusion</h3><div>The SOE-LLM shows potential as a valuable tool for surgical education, offering a consistent and accessible platform for simulating oral examinations.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522419","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":"Artificial Intelligence Image-Diagnosis for Female Genital Schistosomiasis","authors":"Jiayuan Zhu MSc ,&nbsp;J. Alison Noble DPhil ,&nbsp;Mireille Gomes DPhil","doi":"10.1016/j.mcpdig.2025.100245","DOIUrl":"10.1016/j.mcpdig.2025.100245","url":null,"abstract":"<div><h3>Objective</h3><div>To introduce a novel, artificial Intelligence (AI), deep learning-based application for automated diagnosis of female genital schistosomiasis (FGS), a disease estimated to affect around 56 million women and girls in sub-Saharan Africa.</div></div><div><h3>Patients and Methods</h3><div>This study focused on cervical images collected from a high endemic FGS area in Cameroon, from August 1, 2020 to August 31, 2021. We applied the You Only Look Once deep learning model and employed a 5-fold cross-validation approach, accompanied by sensitivity analysis, to optimize model performance.</div></div><div><h3>Results</h3><div>The model achieved a sensitivity of 0.96 (76/78) and an accuracy of 0.78 (97/125), demonstrating improved performance over an existing, non-AI-based, computerized image diagnostic method, which has a sensitivity of 0.94 (73/78) but an accuracy of 0.58 (73/125) on the same dataset. In addition, the AI model significantly reduced processing time, decreasing from 47 minutes to under 90 seconds for testing 250 images.</div></div><div><h3>Conclusion</h3><div>This study highlights the potential of deep learning-based models for automated diagnosis for FGS while reducing the reliance on specialized clinical expertise. It also underscores the need for further work to address current limitations of such AI-based methods for FGS diagnosis.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100245"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605925","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":"Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine","authors":"Mike Zack MD, PhD, MPH,&nbsp;Danil N. Stupichev MSc,&nbsp;Alex J. Moore BSc,&nbsp;Ioan D. Slobodchikov MSc,&nbsp;David G. Sokolov MSc,&nbsp;Igor F. Trifonov MSc,&nbsp;Allan Gobbs MSc","doi":"10.1016/j.mcpdig.2025.100246","DOIUrl":"10.1016/j.mcpdig.2025.100246","url":null,"abstract":"<div><div>Pharmacogenomics is entering a transformative phase as high-throughput “omics” techniques become increasingly integrated with state-of-the-art artificial intelligence (AI) methods. Although early successes in single-gene pharmacogenetics reported clear clinical benefits, many drug response phenotypes are governed by intricate networks of genomic variants, epigenetic modifications, and metabolic pathways. Multi-omics approaches address this complexity by capturing genomic, transcriptomic, proteomic, and metabolomic data layers, offering a comprehensive view of patient-specific biology. Advanced AI models, including deep neural networks, graph neural networks, and representation learning techniques, further enhance this landscape by detecting hidden patterns, filling gaps in incomplete data sets, and enabling in silico simulations of treatment responses. Such capabilities not only improve predictive accuracy but also deepen mechanistic insights, revealing how gene–gene and gene–environment interactions shape therapeutic outcomes. At the same time, real-world data from diverse patient populations is broadening the evidence base, underscoring the importance of inclusive datasets and population-specific algorithms to reduce health disparities. Despite challenges related to data harmonization, interpretability, and regulatory oversight, the synergy between multi-omics integration and AI-driven analytics holds relevant promise for revolutionizing clinical decision-making. In this review, we highlighted key technological advances, discussed current limitations, and outlined future directions for translating multi-omics plus AI innovations into routine personalized medicine.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662090","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":"Battle of the Bots: Solving Clinical Cases in Osteoarticular Infections With Large Language Models","authors":"Fabio Borgonovo MD ,&nbsp;Takahiro Matsuo MD ,&nbsp;Francesco Petri MD ,&nbsp;Seyed Mohammad Amin Alavi MD ,&nbsp;Laura Chelsea Mazudie Ndjonko ,&nbsp;Andrea Gori MD ,&nbsp;Elie F. Berbari MD, MBA","doi":"10.1016/j.mcpdig.2025.100230","DOIUrl":"10.1016/j.mcpdig.2025.100230","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the ability of 15 different large language models (LLMs) to solve clinical cases with osteoarticular infections following published guidelines.</div></div><div><h3>Materials and Methods</h3><div>The study evaluated 15 LLMs across 5 categories of osteoarticular infections: periprosthetic joint infection, diabetic foot infection, native vertebral osteomyelitis, fracture-related infections, and septic arthritis. Models were selected systematically, including general-purpose and medical-specific systems, ensuring robust English support. In total, 126 text-based questions, developed by the authors from published guidelines and validated by experts, assessed diagnostic, management, and treatment strategies. Each model answered individually, with responses classified as correct or incorrect based on guidelines. All tests were conducted between April 17, 2025, and April 28, 2025. Results, presented as percentages of correct answers and aggregated scores, highlight performance trends. Mixed-effects logistic regression with a random question effect was used to quantify how each LLM compared in answering the study questions.</div></div><div><h3>Results</h3><div>The performance of 15 LLMs was evaluated, with the percentage of correct answers reported. OpenEvidence and Microsoft Copilot achieved the highest score (119/126 [94.4%]), excelling in multiple categories. ChatGPT-4o and Gemini 2.5 Pro scored 117 of the 126 (92.8%). When used as references, OpenEvidence was not inferior to any comparator and was superior to 5 LLMs. Performance varied across categories, highlighting the strengths and limitations of individual models.</div></div><div><h3>Conclusion</h3><div>OpenEvidence and Miccrosoft Copilot achieved the highest accuracy among evaluated LLMs, highlighting their potential for precisely addressing complex clinical cases. This study emphasizes the need for specialized, validated artificial intelligence tools in medical practice. Although promising, current models face limitations in real-world applications, requiring further refinement to support clinical decision making reliably.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100230"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280102","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":"Erratum to “Assessment of Positive Cardiac Remodeling in Hypertrophic Obstructive Cardiomyopathy Using an Artificial Intelligence-Based Electrocardiographic Platform in Patients Treated With Mavacamten”","authors":"","doi":"10.1016/j.mcpdig.2025.100209","DOIUrl":"10.1016/j.mcpdig.2025.100209","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280103","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":"2025-09-01","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":"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":"2025-09-01","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}

{"title":"Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study","authors":"Stephanie Zawada PhD, MS ,&nbsp;Jestrii Acosta MS ,&nbsp;Caden Collins BA ,&nbsp;Oana Dumitrascu MD, MS ,&nbsp;Ehab Harahsheh MBBS ,&nbsp;Clinton Hagen MS ,&nbsp;Ali Ganjizadeh MD ,&nbsp;Elham Mahmoudi MD ,&nbsp;Bradley Erickson MD, PhD ,&nbsp;Bart Demaerschalk MD, MSc","doi":"10.1016/j.mcpdig.2025.100240","DOIUrl":"10.1016/j.mcpdig.2025.100240","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the feasibility of using smartphones to longitudinally collect objective behavior measures and establish the extent to which they can predict gold-standard depression severity in patients with ischemic stroke and transient ischemic attack (IS/TIA) symptoms.</div></div><div><h3>Patients and Methods</h3><div>Participants with IS/TIA symptoms were monitored in real-world settings using the Beiwe application for 8 or more weeks during March 1, 2024 to November 15, 2024. Depression symptoms were tracked via weekly Patient Health Questionnaire (PHQ)-8 surveys, monthly personnel-administered Montgomery–Åsberg Depression Rating Scale (MADRS) assessments, and weekly averages of smartphone sensor measures. Repeated measures correlation established associations between PHQ-8 scores and objective behavior measures. To investigate how closely smartphone data predicted MADRS scores, linear mixed models were used.</div></div><div><h3>Results</h3><div>Among enrolled participants (n=54), 35 completed the study (64.8%). PHQ-8 scores were associated with distance from home (<em>r</em>=0.173), time spent at home (<em>r</em>=−0.147) and PHQ-8 administration duration (<em>r</em>=0.151). Using demographic data and the most recent PHQ-8 scores, average root-mean-squared error for depression severity prediction across models was 1.64 with only PHQ-8 scores, 1.49 also including accelerometer and GPS data, and 1.36 also including PHQ-8 administration duration.</div></div><div><h3>Conclusion</h3><div>Smartphone sensors captured objective behavior measures in patients with IS/TIA. In predictive models, the accuracy of depression severity scores improved as measures from additional smartphone sensors were included. Future research should validate this decentralized, exploratory approach in a larger cohort. Our work is a step toward showing that real-world monitoring with active and passive data may triage patients with IS/TIA for efficient depression screening and provide digital mobility and response time endpoints.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596839","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 Technology for Informed Choices at Childbirth in Brazil: A Randomized Controlled Trial","authors":"Carmen Simone Grilo Diniz PhD ,&nbsp;Ana Carolina Arruda Franzon PhD ,&nbsp;Beatriz Fioretti-Foschi PhD ,&nbsp;Livia Sanches Pedrilio MSc ,&nbsp;Edson Amaro Jr. PhD ,&nbsp;João Ricardo Sato PhD ,&nbsp;Denise Yoshie Niy PhD","doi":"10.1016/j.mcpdig.2025.100238","DOIUrl":"10.1016/j.mcpdig.2025.100238","url":null,"abstract":"<div><h3>Objective</h3><div>To design and evaluate an information and communication intervention via a smartphone application that provides access to essential information on best practices and safety in maternity services.</div></div><div><h3>Participants and Methods</h3><div>A randomized controlled trial using a mobile application to recruit and deliver the intervention, conducted from October 31, 2020, through December 12, 2020. The study was offered to all users registered on the application who self-identified as women, with ages between 18 and 49 years, with at least 1 child, pregnant or interested in having children in the future. The primary outcome measured was increased participant engagement in seeking an active role and informed choices. Participants received information about best practices (intervention) or about diapers (control). The trial was registered on the Brazilian Clinical Trials Registry Platform, and the protocol was published according to CONSORT e-Health guidelines. Effect size was estimated by odds ratio, with CI and <em>P</em> values.</div></div><div><h3>Results</h3><div>In total, 20,608 users were invited to participate in the study; of 17,643 enrolled (85.6% of invited users), 13,969 (79.1% of enrolled participants) women completed the intervention stage and were included in the analyses; 7121 (50.9% of all women included) had up to high school level; and 5855 (41.9%) used both public and private services. The intervention group registered an increased engagement in seeking an active role or making informed choices (odds ratio, 2.06; <em>P</em>&lt;.001). The intervention proved to be highly effective for all secondary outcomes, as well.</div></div><div><h3>Conclusion</h3><div>This affordable digital technology effectively promoted awareness of safer, empowered choices in childbirth care, facilitating the translation of evidence-based, rights-based knowledge from institutional guidelines and recommendations to a broader audience.</div></div><div><h3>Trial Registration</h3><div>Brazilian Registry of Clinical Trials Identifier: RBR-3g5f9f; WHO’s Unique Trial Identifier: UTN U1111-1255-8683.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534515","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":"Using Social Media to Combat Influenza Vaccine Misinformation and Improve Uptake: A Social Media Campaign and Repeated Cross-sectional Survey Analysis","authors":"Jessica B. Steier DrPH","doi":"10.1016/j.mcpdig.2025.100229","DOIUrl":"10.1016/j.mcpdig.2025.100229","url":null,"abstract":"<div><h3>Objective</h3><div>To combat influenza (flu)-vaccine misinformation and improve vaccine uptake using social media.</div></div><div><h3>Patients and Methods</h3><div>Unbiased Science used an online survey to identify flu vaccine-hesitant demographic groups and their specific objections to vaccination. Targeted educational content was then created and deployed through a variety of media formats, including podcasts, newsletters, reels, and infographics. A postcampaign survey determined the proportion of individuals who changed their minds about vaccination as a result of the educational content. The study was conducted between October 28, 2022 and February 7, 2023.</div></div><div><h3>Results</h3><div>In 3626 precampaign surveys, 187 individuals (5.1%) reported being unvaccinated and not planning to get the flu vaccine (the unvaccinated group). Multivariable analysis showed that geographic region (Northeast and Southeast), gender identity (male and other), race–ethnicity (non-Hispanic Black, and non-Hispanic other), and education level (high-school or less and some college) were independently associated with being unvaccinated. The main reasons were needlephobia, dismissal of flu severity, and concerns about vaccine components, multiple vaccines, and side effects. In 838 postcampaign surveys, 39 individuals (4.7%) indicated changing their mind about vaccination: of these, 27 (69.2%) said they were more likely to get vaccinated and 22 (56.4%) had gotten vaccinated. Twenty individuals (51.3%) said they changed their mind at least in part because of the targeted educational content.</div></div><div><h3>Conclusion</h3><div>Social media has the potential to change attitudes and behaviors around vaccination. When science messaging is deployed across several platforms and targeted to key demographic characteristics, it has the ability to combat misinformation and influence vaccine uptake.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297540","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}