Mayo Clinic Proceedings. Digital health最新文献

{"title":"Erratum to “Impact of Ambient Artificial Intelligence Documentation on Cognitive Load”","authors":"","doi":"10.1016/j.mcpdig.2025.100219","DOIUrl":"10.1016/j.mcpdig.2025.100219","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848529","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":"Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease","authors":"Yashbir Singh ME, PhD ,&nbsp;Quincy A. Hathaway MD, PhD ,&nbsp;Karthik Dinakar PhD ,&nbsp;Leslee J. Shaw PhD ,&nbsp;Bradley Erickson MD, PhD ,&nbsp;Francisco Lopez-Jimenez MD, MBA, MSc ,&nbsp;Deepak L. Bhatt MD, MPH, MBA","doi":"10.1016/j.mcpdig.2025.100217","DOIUrl":"10.1016/j.mcpdig.2025.100217","url":null,"abstract":"<div><div>This article aimed to explore topological uncertainty in medical imaging, particularly in assessing coronary artery calcification using artificial intelligence (AI). Topological uncertainty refers to ambiguities in spatial and structural characteristics of medical features, which can impact the interpretation of coronary plaques. The article discusses the challenges of integrating AI with topological considerations and the need for specialized methodologies beyond traditional performance metrics. It highlights advancements in quantifying topological uncertainty, including the use of persistent homology and topological data analysis techniques. The importance of standardization in methodologies and ethical considerations in AI deployment are emphasized. It also outlines various types of uncertainty in topological frameworks for coronary plaques, categorizing them as quantifiable and controllable or quantifiable and not controllable. Future directions include developing AI algorithms that incorporate topological insights, establishing standardized protocols, and exploring ethical implications to revolutionize cardiovascular care through personalized treatment plans guided by sophisticated topological analysis. Recognizing and quantifying topological uncertainty in medical imaging as AI emerges is critical. Exploring topological uncertainty in coronary artery disease will revolutionize cardiovascular care, promising enhanced precision and personalization in diagnostics and treatment for millions affected by cardiovascular diseases.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848528","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":"Impact of Digital Interventions in Occupational Health Care: A Systematic Review","authors":"Mirjam M. Jern-Matintupa MD, MPH ,&nbsp;Anita M. Riipinen MD, PhD ,&nbsp;Merja K. Laine MD, PhD","doi":"10.1016/j.mcpdig.2025.100216","DOIUrl":"10.1016/j.mcpdig.2025.100216","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the existing body of evidence and impact of digital interventions on occupational health care.</div></div><div><h3>Methods</h3><div>The search strategy and review process were conducted in accordance with the PRISMA guidelines. The search was carried out during a period from January 1, 2013 to June 5, 2023, using the SCOPUS and Ovid Medline databases. After the identification of the relevant records, screening was conducted in 3 stages, following specific predetermined inclusion and exclusion criteria. A data-extraction model was created on the basis of the aim of the review. The quality of the selected studies was evaluated using the Effective Public Health Practice framework. Owing to the heterogeneity of the outcome measures, we used narrative synthesis to summarize the findings.</div></div><div><h3>Results</h3><div>We identified 382 records in SCOPUS and 441 in Ovid Medline. We selected 54 studies to be included in the evidence synthesis. The health targets of the interventions varied widely, but we identified 2 main focus areas: sedentary behavior (n=17, 32%) and mental health (n=14, 26%). Even when the studies had the same health target, the outcomes and chosen measures varied widely. Given the considerable effect of the primary outcome, mental health appears to be a good target for digital interventions. Online training and computer software could be especially effective.</div></div><div><h3>Conclusion</h3><div>The potential positive impact of digital interventions on mental health, especially online training, should be leveraged by health care professionals and providers. In order to provide more specific recommendations for health care professionals, occupational health care researchers should strive for consensus on outcome measures.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100216"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792485","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":"Erratum to Leveraging the Metaverse for Enhanced Longevity as a Component of Health 4.0 [Mayo Clinic Proceedings: Digital Health. 2024;2:139-151]","authors":"","doi":"10.1016/j.mcpdig.2025.100215","DOIUrl":"10.1016/j.mcpdig.2025.100215","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705167","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":"Selecting Wearable Devices to Measure Cardiovascular Functions in Community-Dwelling Adults: Application of a Practical Guide for Device Selection","authors":"Jessica K. Lu MEng ,&nbsp;Weilan Wang PhD ,&nbsp;Jorming Goh PhD ,&nbsp;Andrea B. Maier MD, PhD","doi":"10.1016/j.mcpdig.2025.100202","DOIUrl":"10.1016/j.mcpdig.2025.100202","url":null,"abstract":"<div><div>Continuous monitoring of cardiovascular functions can provide crucial insights into the health status and lifestyle behaviors of an individual. Wearable devices offer a convenient and cost-effective solution for collecting cardiovascular measurements outside clinical settings. However, the abundance of available devices poses challenges for researchers, health care professionals, and device users in selecting the most suitable one. This article illustrates the application of a practical guide for selecting wearable devices for the continuous monitoring of cardiovascular functions in community-dwelling adults who are generally healthy or have minimal, well-managed chronic conditions. An initial systematic review of the literature revealed 216 devices, each of which were assessed on the basis of 5 core criteria from the guide: (1) continuous monitoring capability, (2) device availability and suitability, (3) technical performance (accuracy and precision), (4) feasibility of use, and (5) cost evaluation. From the 216 devices, there were 136 devices capable of continuous monitoring. After the exclusion of unavailable and unsuitable devices, 53 devices underwent validation assessment of accuracy and precision. Although COSMIN criteria were applied to evaluate technical performance, a lack of validation for certain devices limits a comprehensive evaluation. After selection of valid devices, the feasibility and cost of 20 devices were examined. Wearable devices, such as the Apple Watch Series 9, Fitbit Charge 6, Garmin vívosmart 5, and Oura Ring Gen3, emerged as suitable devices to measure cardiovascular function in community-dwelling adults. The systematic process for device selection could also be applied to select wearable devices for the measurement of other physiologic variables and lifestyle behaviors.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697823","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":"The PERFORM Study: Artificial Intelligence Versus Human Residents in Cross-Sectional Obstetrics-Gynecology Scenarios Across Languages and Time Constraints","authors":"Canio Martinelli MD ,&nbsp;Antonio Giordano MD ,&nbsp;Vincenzo Carnevale PhD ,&nbsp;Sharon Raffaella Burk PhD ,&nbsp;Lavinia Porto MD ,&nbsp;Giuseppe Vizzielli MD ,&nbsp;Alfredo Ercoli MD","doi":"10.1016/j.mcpdig.2025.100206","DOIUrl":"10.1016/j.mcpdig.2025.100206","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically evaluate the performance of artificial intelligence (AI) large language models (LLMs) compared with obstetrics-gynecology residents in clinical decision-making, examining diagnostic accuracy and error patterns across linguistic domains, time constraints, and experience levels.</div></div><div><h3>Patients and Methods</h3><div>In this cross-sectional study, we evaluated 8 AI LLMs and 24 obstetrics-gynecology residents (Years 1-5) using 60 standardized clinical scenarios. Most AI LLMs and all residents were assessed in May 2024, whereas chat GPT-01-preview, chat-GPT4o, and Claude Sonnet 3.5 were evaluated in November 2024. The assessment framework incorporated English and Italian scenarios under both timed and untimed conditions, along with systematic error pattern analysis. The primary outcome was diagnostic accuracy; secondary end points included AI system stratification, resident progression, language impact, time pressure effects, and integration potential.</div></div><div><h3>Results</h3><div>The AI LLMs reported superior overall accuracy (73.75%; 95% confidence interval [CI], 69.64%-77.49%) compared with residents (65.35%; 95% CI, 62.85%-67.76%; <em>P</em>&lt;.001). High-performing AI systems (ChatGPT-01-preview, GPT4o, and Claude Sonnet 3.5) achieved consistently high cross-linguistic accuracy (88.33%) with minimal language impact (6.67%±0.00%). Resident performance declined significantly under time constraints (from 73.2% to 56.5% adjusted accuracy; Cohen’s d=1.009; <em>P</em>&lt;.001), whereas AI systems reported lesser deterioration. Error pattern analysis indicated a moderate correlation between AI and human reasoning (r=0.666; <em>P</em>&lt;.001). Residents exhibited systematic progression from year 1 (44.7%) to year 5 (87.1%). Integration analysis found variable benefits across training levels, with maximum enhancement in early-career residents (+29.7%; <em>P</em>&lt;.001).</div></div><div><h3>Conclusion</h3><div>High-performing AI LLMs reported strong diagnostic accuracy and resilience under linguistic and temporal pressures. These findings suggest that AI-enhanced decision-making may offer particular benefits in obstetrics and gynecology training programs, especially for junior residents, by improving diagnostic consistency and potentially reducing cognitive load in time-sensitive clinical settings.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697951","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":"Corrigendum to “Experience With an Optical Character Recognition Search Application for Review of Outside Medical Records”","authors":"","doi":"10.1016/j.mcpdig.2025.100208","DOIUrl":"10.1016/j.mcpdig.2025.100208","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697824","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":"Global Artificial Intelligence Arms Race: The Future of Artificial Intelligence in Medicine","authors":"Hamrish Kumar Rajakumar MBBS","doi":"10.1016/j.mcpdig.2025.100207","DOIUrl":"10.1016/j.mcpdig.2025.100207","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705166","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":"A Systematic Review of Natural Language Processing Techniques for Early Detection of Cognitive Impairment","authors":"Ravi Shankar PhD ,&nbsp;Anjali Bundele MPH ,&nbsp;Amartya Mukhopadhyay FRCP","doi":"10.1016/j.mcpdig.2025.100205","DOIUrl":"10.1016/j.mcpdig.2025.100205","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically evaluate the effectiveness and methodologic approaches of natural language processing (NLP) techniques for early detection of cognitive decline through speech and language analysis.</div></div><div><h3>Methods</h3><div>We conducted a comprehensive search of 8 databases from inception through August 31, 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were included if they used NLP techniques to analyze speech or language data for detecting cognitive impairment and reported diagnostic accuracy metrics. Two independent reviewers (R.S. and A.B.) screened articles and extracted data on study characteristics, NLP methods, and outcomes.</div></div><div><h3>Results</h3><div>Of 23,562 records identified, 51 studies met inclusion criteria, involving 17,340 participants (mean age, 72.4 years). Combined linguistic and acoustic approaches achieved the highest diagnostic accuracy (average 87%; area under the curve [AUC], 0.89) compared with linguistic-only (83%; AUC, 0.85) or acoustic-only approaches (80%; AUC, 0.82). Lexical diversity, syntactic complexity, and semantic coherence were consistently strong predictors across cognitive conditions. Picture description tasks were most common (n=21), followed by spontaneous speech (n=15) and story recall (n=8). Crosslinguistic applicability was found across 8 languages, although language-specific adaptations were necessary. Longitudinal studies (n=9) reported potential for early detection but were limited by smaller sample sizes (average n=159) compared with cross-sectional studies (n=42; average n=274).</div></div><div><h3>Conclusion</h3><div>Natural language processing techniques show promising diagnostic accuracy for detecting cognitive impairment across multiple languages and clinical contexts. Although combined linguistic-acoustic approaches appear most effective, methodologic heterogeneity and small sample sizes in existing studies suggest the need for larger, standardized investigations to establish clinical utility.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697822","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":"Comparison of Participant and Site Perceptions of Decentralized Clinical Trials in the USA","authors":"Roland Barge PhD ,&nbsp;Patrick Floody MBA","doi":"10.1016/j.mcpdig.2025.100201","DOIUrl":"10.1016/j.mcpdig.2025.100201","url":null,"abstract":"<div><h3>Objective</h3><div>To define potential participant and site perceptions of decentralized clinical trials (DCTs).</div></div><div><h3>Participants and Methods</h3><div>Two qualitative surveys were conducted between January 2022 and August 2022 to assess current awareness of, and perceptions about, DCTs. The first survey received 141 responses from staff at our clinical trial sites; the second survey received 481 responses from US-based healthy individuals or those living with an illness.</div></div><div><h3>Results</h3><div>There was a difference in perceptions and willingness between participants and sites toward DCTs. Participants expressed more comfort with hybrid and fully remote trials than did the sites. Site staff were more concerned and less trusting than participants of DCTs; participants’ main concerns were regarding practicality and medical safety, whereas the focus for sites was on burden, trust, and security. Both sites and participants expressed confidence in fully remote clinical study activities when they have appropriate support; sites were less tolerant of fully remote clinical study activities if professional support was not provided. Overall, sites were more willing to manage the use of DCT-related technologies than were participants. It is highly likely that participants’ willingness to manage DCT technologies relates to the perceived burden of use (ie, willingness decreases as burden or impact on daily life increases). Sponsors, contract research organizations, and DCT vendors generally had positive views on DCTs. However, different stakeholders had different concerns.</div></div><div><h3>Conclusion</h3><div>These results highlight the need for collaborative research and development of DCTs, as well as a clear DCT framework and regulatory guidance.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696654","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}