Mayo Clinic Proceedings. Digital health最新文献

{"title":"Assessment of Positive Cardiac Remodeling in Hypertrophic Obstructive Cardiomyopathy Using an Artificial Intelligence–Based Electrocardiographic Platform in Patients Treated With Mavacamten","authors":"Mustafa Suppah MD , Kaitlin Roehl PA-C , Kathryn Lew APRN, NP, MSN , Reza Arsanjani MD , Steven Lester MD , Steve Ommen MD , Jeffrey Geske MD , Konstantinos C. Siontis MD , Hartzell Schaff MD , Said Alsidawi MD","doi":"10.1016/j.mcpdig.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.002","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 255-257"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000294/pdfft?md5=051e61d0b54747fe9d602ac3bd249683&pid=1-s2.0-S2949761224000294-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822696","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":"Social Media and Artificial Intelligence—Understanding Medical Misinformation Through Snapchat’s New Artificial Intelligence Chatbot","authors":"Clara E. Tandar , Simar S. Bajaj , Fatima Cody Stanford MD, MPH, MPA, MBA","doi":"10.1016/j.mcpdig.2024.04.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 252-254"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000324/pdfft?md5=fa80a8039bfb5008a0473d61e26f78b6&pid=1-s2.0-S2949761224000324-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818294","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":"AImedReport: A Prototype Tool to Facilitate Research Reporting and Translation of Artificial Intelligence Technologies in Health Care","authors":"Tracey A. Brereton MS , Momin M. Malik PhD, MS, MSc , Lauren M. Rost PhD, MS , Joshua W. Ohde PhD , Lu Zheng PhD, MS , Kristelle A. Jose MS , Kevin J. Peterson PhD, MS , David Vidal JD , Mark A. Lifson PhD , Joe Melnick BS , Bryce Flor BS , Jason D. Greenwood MD, MS , Kyle Fisher MPA , Shauna M. Overgaard PhD","doi":"10.1016/j.mcpdig.2024.03.008","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.008","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 246-251"},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000312/pdfft?md5=d79b5ee4ecae7f6058302e879e2b8af2&pid=1-s2.0-S2949761224000312-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806852","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":"Housing Characteristics of Areas With More Falls by Older Adults Living in Single-Family Detached Dwellings: A Cohort Study Using Geospatial Analysis","authors":"Paul Y. Takahashi MD, MPH ,&nbsp;Euijung Ryu PhD ,&nbsp;Katherine S. King MS ,&nbsp;Rachel E. Dixon BA ,&nbsp;Julie C. Porcher MS ,&nbsp;Philip H. Wheeler ,&nbsp;Chung Il Wi MD ,&nbsp;Young J. Juhn MD, MPH","doi":"10.1016/j.mcpdig.2024.04.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.001","url":null,"abstract":"<div><h3>Objective</h3><p>To identify geographic locations with high numbers of medically attended falls (ie, hotspots) by older adults and to test the associations between fall hotspots and resident/housing characteristics.</p></div><div><h3>Patients and Methods</h3><p>In this cohort study, we retrospectively reviewed adults who were 65 years or older, lived in a single-family detached dwelling, and had a medically attended fall in Olmsted County, MN, between April 1, 2012, and December 31, 2014. We identified medically attended falls by using billing codes and confirmed by manual review of the electronic health records. We performed geospatial analysis to identify fall hotspots and evaluated the association between fall hotspots and resident or housing characteristics with logistic regression models, adjusting for age, sex, socioeconomic status, chronic health conditions, and/or a history of falls.</p></div><div><h3>Results</h3><p>Among 12,888 residents living in single-family detached dwellings in our community, 587 residents (4.6%) had documented accidental falls. Falls were more common in older residents and in women. Residents who had more chronic diseases, lower socioeconomic status, and a history of falls also had higher odds of a fall. Geospatial analysis identified 2061 (16.0%) residents who lived in a fall hotspot. Houses in hotspots were more likely to have more stories with fewer stairs (split level) (odds ratio [OR], 1.75; 95% CI, 1.57-1.94, for split level vs 1-story houses), smaller square feet (OR, 0.29; 95% CI, 0.24-0.35, for largest vs smallest houses), and in the highest quartile for age (OR, 1.46; 95% CI, 1.26-1.70, for oldest built vs newest built houses).</p></div><div><h3>Conclusion</h3><p>Falls were more common in locations in our community that had older, smaller homes and lower housing-based socioeconomic status. These findings can be used by clinicians to identify residents who are at higher risk for falls.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 259-269"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000282/pdfft?md5=677afcbfea9f9ecd229c0c3ed4369544&pid=1-s2.0-S2949761224000282-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894928","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 Use of Artificial Intelligence to Detect Malignant Skin Lesions","authors":"Sofia Haddadin ,&nbsp;Latha Ganti MD, MS, MBA","doi":"10.1016/j.mcpdig.2024.04.003","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.003","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 241-245"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000300/pdfft?md5=10086ff1aae1aa26c957d2e5d87ac957&pid=1-s2.0-S2949761224000300-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806851","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":"Empowering Patients in the Digital Age: New Framework to Measure and Improve Patient Digital Experiences","authors":"Andrew Kucheriavy BCS, BEc","doi":"10.1016/j.mcpdig.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.001","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 181-185"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294976122400018X/pdfft?md5=8a1e0da33153f6f9b359407345dc3538&pid=1-s2.0-S294976122400018X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342099","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":"Integrating Clinical Guidelines With ChatGPT-4 Enhances Its’ Skills","authors":"Raseen Tariq MBBS,&nbsp;Elida Voth MD,&nbsp;Sahil Khanna MBBS, MS","doi":"10.1016/j.mcpdig.2024.02.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.02.004","url":null,"abstract":"<div><p>Navigating clinical guidelines can be complex for real-time health care decision making. Our study evaluates the chat generative prerained transformer (ChatGPT)-4 in improving responses to clinical questions by integrating guidelines on <em>Clostridioides difficile</em> infection and colon polyp surveillance. We assessed ChatGPT-4’s responses to questions before and after guideline integration, noting a clear improvement in accuracy. ChatGPT-4 provided guideline-aligned answers consistently. Further analysis showed its ability to summarize information from conflicting guidelines, highlighting its utility in complex clinical scenarios. The findings suggest that large language models such as ChatGPT-4 can enhance clinical decision making and patient education by providing quick, conversational, and accurate responses. This approach opens a path for using artificial intelligence to deliver reliable responses in health care, supporting clinicians in real-time decision making and improving patient care.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 177-180"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000178/pdfft?md5=7aaafaf14f2ffcccea9377d36db0cb44&pid=1-s2.0-S2949761224000178-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320827","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":"Foundation Models for Histopathology—Fanfare or Flair","authors":"Saghir Alfasly PhD ,&nbsp;Peyman Nejat MD ,&nbsp;Sobhan Hemati PhD ,&nbsp;Jibran Khan ,&nbsp;Isaiah Lahr ,&nbsp;Areej Alsaafin PhD ,&nbsp;Abubakr Shafique PhD ,&nbsp;Nneka Comfere MD ,&nbsp;Dennis Murphree PhD ,&nbsp;Chady Meroueh MD ,&nbsp;Saba Yasir MBBS ,&nbsp;Aaron Mangold MD ,&nbsp;Lisa Boardman MD ,&nbsp;Vijay H. Shah MD ,&nbsp;Joaquin J. Garcia MD ,&nbsp;H.R. Tizhoosh PhD","doi":"10.1016/j.mcpdig.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.02.003","url":null,"abstract":"<div><h3>Objective</h3><p>To assess the performance of the current foundation models in histopathology.</p></div><div><h3>Patients and Methods</h3><p>The assessment involves a comprehensive evaluation of some foundation models, such as the CLIP derivatives, namely PLIP and BiomedCLIP, which were fine-tuned on data scraped from the internet. The comparison is performed against simpler and nonfoundational histology models that are trained on well-curated data, eg, the cancer genome atlas. All models are evaluated on 8 datasets, 4 of which are internal histology datasets collected and curated at Mayo Clinic, and 4 well-known public datasets: PANDA, BRACS, CAMELYON16, and DigestPath. Evaluation metrics include accuracy and macro-averaged F1 score, using a majority vote among top-k (eg, MV@5) at the whole slide image/patch levels. Moreover, all models are evaluated in classification settings. This detailed analysis allows for a deep understanding of each model’s performance across various datasets.</p></div><div><h3>Results</h3><p>In various evaluation tasks, domain-specific (and nonfoundational) models like DinoSSLPath and KimiaNet outperform general-purpose foundation models. The DinoSSLPath excels in whole slide image-level retrieval for internal colorectal cancer and liver datasets with MV@5 macro-averaged F1 scores of 63% and 74%, respectively. The KimiaNet leads in breast and skin cancer tasks with respective Top-1 and MV@5 scores of 56% and 70%, respectively and scores 75% on the public CAMELYON16 dataset. Similar trends are observed in patch-level metrics, highlighting the advantage of using specialized datasets like the cancer genome atlas for histopathological analysis.</p></div><div><h3>Conclusion</h3><p>To enable effective vision-language foundation models in biomedicine, high-quality, multi-modal medical datasets are essential. These datasets serve as the substrate for training models capable of translating research into clinical practice. Of importance, the alignment (correspondence) between textual and visual data—often diagnostic—is critical and requires validation by domain experts. Thus, advancing foundation models in this field necessitates collaborative efforts in data curation and validation.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Pages 165-174"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000142/pdfft?md5=238256763b3157fe9ab69c42a61cae64&pid=1-s2.0-S2949761224000142-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140030354","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":"Artificial Intelligence Face Swapping: Promise and Peril in Health Care","authors":"Shankargouda Patil BDS, MDS, PhD","doi":"10.1016/j.mcpdig.2024.01.009","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.009","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Page 159"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000129/pdfft?md5=17ded9949a5f81f9bacbc3731943bf85&pid=1-s2.0-S2949761224000129-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139993281","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":"Reply to: Exercise Testing and Artificial Intelligence as Allies in Improving the Detection and Diagnosis of Long QT Syndrome","authors":"Negar Raissi Dehkordi MD,&nbsp;Nastaran Raissi Dehkordi MD,&nbsp;Kimia Karimi Toudeshki MD,&nbsp;Mohammad Hadi Farjoo MD, PhD","doi":"10.1016/j.mcpdig.2024.01.012","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.01.012","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 1","pages":"Page 164"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000166/pdfft?md5=568e945b8d3e4093fa58d3edbf68e68e&pid=1-s2.0-S2949761224000166-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140013999","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}