Mayo Clinic Proceedings. Digital health最新文献

{"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":"2026-03-01","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}

{"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":"2026-03-01","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}

{"title":"Maximizing Efficiency of Artificial Intelligence-Enabled Ambient Scribes in Outpatient Settings: A Pragmatic Approach to Structuring the Patient Appointment","authors":"Jason D. Greenwood MD, MS , Marc R. Matthews MD , Joshua D. Overgaard MD , Joshua W. Ohde PhD","doi":"10.1016/j.mcpdig.2026.100339","DOIUrl":"10.1016/j.mcpdig.2026.100339","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100339"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277940","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":"Computer-Aided Analysis of Photographed Chest X-Ray Films Performs Well Compared With Trained Radiologists","authors":"Zerubabel Desita MD ,&nbsp;Temesgen Tadesse MD ,&nbsp;Anders Solitander Bohlbro MD ,&nbsp;Armando Sifna MD ,&nbsp;Hikma Fekadu MD ,&nbsp;Segenet Bizuneh MD ,&nbsp;Sruti Sridhar ,&nbsp;Dennis Robert ,&nbsp;Manoj Tadepalli ,&nbsp;Christian Wejse ,&nbsp;Thomas Schön ,&nbsp;Frauke Rudolf MD, PhD","doi":"10.1016/j.mcpdig.2026.100338","DOIUrl":"10.1016/j.mcpdig.2026.100338","url":null,"abstract":"<div><h3>Objective</h3><div>To assess whether computer-aided detection (CAD) chest X-ray (CXR) software may aid physicians in low-resource, high tuberculosis (TB) endemic settings where radiologists are scarce.</div></div><div><h3>Patients and Methods</h3><div>A retrospective pilot study was conducted on CXR films taken between January 1, 2017, and March 30, 2018, in Guinea-Bissau and Ethiopia to compare the interpretation of CXRs regarding pulmonary TB (PTB) by CAD (qXR; Qure.ai) with that of 2 experienced Ethiopian radiologists (A and B). To improve the applicability of this method in low-resource settings, an analysis was performed on images of CXRs taken by mobile phones. Two reference standards were applied: final PTB diagnosis by clinical or laboratory findings (ie, Xpert MTB/RIF [Xpert]-confirmed PTB).</div></div><div><h3>Results</h3><div>We included 498 CXRs from patients seeking help for TB indicative symptoms. Radiologist A identified 50, radiologist B identified 99, and the software identified 81 as indicative of TB. The overall area under the curve for the receiver-operating characteristic curve of the software was 0.84 for Xpert-confirmed cases. At the prechosen cutoff value of 0.5, the sensitivity of CAD CXR was 76.5%, and the specificity was 85.9%. Radiologist A’s assessments were 64.7% sensitive and 91.9% specific, whereas radiologist B’s assessments were 76.5% sensitive and 82.3% specific for Xpert-confirmed cases. The agreement regarding TB-related findings between the radiologists combined (κ=0.45) and each radiologist and the software (κ=0.56) was moderate.</div></div><div><h3>Conclusion</h3><div>Our study revealed that CAD CXR performs comparably with experienced radiologists when it is applied to CXR films, photographed by mobile phones and a digital camera with similar sensor resolutions.</div></div><div><h3>Trial registration</h3><div>PACTR201611001838365.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100338"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357927","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 AENEAS Project: Intraoperative Anatomical Guidance Through Real-Time Landmark Detection Using Machine Vision","authors":"Simone Olei MD ,&nbsp;Gary Sarwin MSc ,&nbsp;Victor E. Staartjes MD, PhD ,&nbsp;Luca Zanuttini MD ,&nbsp;Seungjun Ryu MD ,&nbsp;Luca Regli MD ,&nbsp;Ender Konukoglu PhD ,&nbsp;Carlo Serra MD","doi":"10.1016/j.mcpdig.2025.100308","DOIUrl":"10.1016/j.mcpdig.2025.100308","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate the performance of a deep learning machine vision-based model in identifying anatomical landmarks in a complex microsurgical setting, such as the pterional trans-Sylvian approach.</div></div><div><h3>Patients and Methods</h3><div>We developed a deep learning object detection model (YOLOv7x) trained on 5307 labeled frames from 78 surgical videos of 76 patients undergoing pterional trans-Sylvian approach from January 1, 2020 to June 31, 2024. Surgical steps were standardized, and key anatomical targets—frontal/temporal dura, inferior frontal/superior temporal gyri, optic and olfactory nerves, and internal carotid artery—were annotated by specifically trained neurosurgical residents and verified by the operating surgeon. Bounding boxes derived from segmentation masks served as training inputs. Performance was evaluated using 5-fold cross-validation.</div></div><div><h3>Results</h3><div>The model achieved promising detection performance for deep structures, particularly the optic nerve (average precision at an intersection over union threshold of 0.50 [AP₅₀]: 0.73) and internal carotid artery (AP₅₀: 0.67). Superficial structures, like dura and cortical gyri, had lower precision (AP₅₀ range: 0.25-0.45), likely due to morphological similarity and optical variability. Performance variability across classes reflects the complexity of the anatomical setting along with data limitations.</div></div><div><h3>Conclusion</h3><div>Applying machine vision techniques for anatomical detection in a complex neurosurgical setting is feasible. Although challenges remain in detecting less distinctive structures, the high accuracy achieved for deep anatomical landmarks validates this approach. This study marks an essential step toward the development of machine vision-powered anatomical recognition tools, with the prospective goal of improving intraoperative orientation and reducing variability among surgeons.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"4 1","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841147","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":"A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery","authors":"Busisiwe Mlambo MD ,&nbsp;Mallory Shields PhD ,&nbsp;Simon Bach MD ,&nbsp;Armin Bauer PhD ,&nbsp;Andrew Hung MD ,&nbsp;Omar Yusef Kudsi MD ,&nbsp;Felix Neis MD ,&nbsp;John Lazar MD ,&nbsp;Daniel Oh MD ,&nbsp;Robert Perez MD ,&nbsp;Seth Rosen MD ,&nbsp;Naeem Soomro MD ,&nbsp;Michael Stany MD ,&nbsp;Mark Tousignant MD ,&nbsp;Christian Wagner MD ,&nbsp;Ken Whaler MS ,&nbsp;Lilia Purvis MS ,&nbsp;Benjamin Mueller BS ,&nbsp;Sadia Yousaf MD ,&nbsp;Casey Troxler BS ,&nbsp;Anthony Jarc PhD","doi":"10.1016/j.mcpdig.2025.100257","DOIUrl":"10.1016/j.mcpdig.2025.100257","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and share the first clinical temporal annotation guide library for 10 robotic procedures accompanied with a standardized ontology framework for surgical video annotation.</div></div><div><h3>Patients and Methods</h3><div>A standardized temporal annotation framework of surgical videos paired with consistent, procedure-specific annotation guides is critical to enable comparisons of surgical insights and facilitate large-scale insights for exceptional surgical practice. Existing ontologies and guidance not only provide foundational frameworks but also provide limited scalability in clinical settings. Building on these, we developed a temporal annotation framework with nested surgical phases, steps, tasks, and subtasks. Procedure-specific annotation resource guides consistent with this framework that define each surgical segment with formulaic start and stop parameters and surgical objectives were iteratively created across 7 years (January 1, 2018, to January 1, 2025) through global research collaborations with surgeon researchers and industry scientists.</div></div><div><h3>Results</h3><div>We provide the first resource library of annotation guides for 10 common robotic procedures consistent with our proposed temporal annotation framework, enabling consistent annotations for clinicians and large-scale data comparisons with computer-readable examples. These have been used in over 13,000 annotated surgical cases globally, demonstrating reproducibility and broad applicability.</div></div><div><h3>Conclusion</h3><div>This resource library and accompanying ontology framework provide critical structure for standardized temporal segmentation in robotic surgery. This framework has been applied globally in private studies examining surgical objective performance metrics, surgical education, workflow characterization, outcome prediction, algorithms for surgical activity recognition, and more. Adoption of these resources will unify clinical, academic, and industry efforts, ultimately catalyzing transformational advancements in surgical practice.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109429","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":"Barriers to Radiomics Adoption for Urological Cancer Diagnosis in Low-Income and Middle-Income Countries: A Perspective from Pakistan","authors":"Awais Ayub MBBS,&nbsp;Hanan Mudassar MBBS,&nbsp;Maida Rizwan MBBS","doi":"10.1016/j.mcpdig.2025.100262","DOIUrl":"10.1016/j.mcpdig.2025.100262","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222261","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":"A Comprehensive Approach to Responsible AI Development and Deployment","authors":"Sonya Makhni MD, MBA, MS ,&nbsp;Jose Rico MBA ,&nbsp;Paul Cerrato MA ,&nbsp;Brenton Hill JD, MHA ,&nbsp;Shauna Overgaard PhD ,&nbsp;Jeffrey Wu MPH ,&nbsp;Justin Fairbanks MS ,&nbsp;Rachelann Tripp MPH ,&nbsp;Justin Redziniak DPT ,&nbsp;Roberto Blundo MPH ,&nbsp;Clark Otley MD ,&nbsp;John Halamka MD, MS","doi":"10.1016/j.mcpdig.2025.100294","DOIUrl":"10.1016/j.mcpdig.2025.100294","url":null,"abstract":"<div><div>Integrating artificial intelligence (AI) into health care offers the potential to address critical challenges related to access to care, workforce burnout, and health inequities. Despite its promise, AI adoption remains limited due to safety, efficacy, and equity concerns. This paper presents a novel and comprehensive framework for responsible AI development, evaluation, and deployment in health care, encompassing four key phases: (1) AI Solution Design and Development, (2) AI Solution Qualification, (3) AI Solution Efficacy and Safety Evaluation, and (4) AI Solution Impact. By establishing rigorous standards for operational, clinical, and technical quality, the framework aims to guide AI developers and health care professionals toward creating AI solutions that are ethical, effective, and scalable. This structured approach fosters collaboration and mitigates risks to help AI achieve its full potential in improving patient outcomes and health care efficiency.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100294"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466484","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":"Associations Between Deep Learning–Derived Fat, Muscle, and Bone Measures From Abdominal Computed Tomography Scans and Fall Risk in Persons Aged 20 Years or Older","authors":"Jennifer L. St. Sauver PhD ,&nbsp;Brandon R. Grossardt MS ,&nbsp;Alexander D. Weston PhD ,&nbsp;Hillary W. Garner MD ,&nbsp;Alanna M. Chamberlain PhD ,&nbsp;Walter A. Rocca MD ,&nbsp;Perry J. Pickhardt MD ,&nbsp;Blake Thackeray ,&nbsp;Owen R. Keegan ,&nbsp;Andrew D. Rule MD","doi":"10.1016/j.mcpdig.2025.100299","DOIUrl":"10.1016/j.mcpdig.2025.100299","url":null,"abstract":"<div><h3>Objective</h3><div>To determine whether abdominal computed tomography (CT) measures of body composition are associated with fall risk in adults aged 20 to 89 years.</div></div><div><h3>Patients and Methods</h3><div>We identified persons who received an abdominal CT scan from 2010 to 2020 using the Rochester Epidemiology Project. We calculated subcutaneous adipose and visceral adipose tissue area, skeletal muscle area and density, and vertebral bone area and density using a validated deep learning algorithm applied to CT abdominal section. Sex-specific tertiles of body composition biomarkers were used for primary analyses. We identified falls using International Classification of Diseases codes and verified via chart review. Associations between body composition tertiles and falls were assessed using Cox proportional hazards models, and models were adjusted for body mass index and the presence of 18 chronic conditions.</div></div><div><h3>Results</h3><div>We included 3972 persons aged 20 to 89 years. Subcutaneous and visceral fat area, skeletal muscle area, bone area, and bone density were not associated with fall risk (all adjusted <em>P</em>&gt;.05). By contrast, lower muscle density was associated with an increased risk of falls (adjusted hazard ratio, for the lowest tertile vs the middle tertile: 2.31; 95% CI, 1.70-3.14). The association between low muscle density and an increased risk of falls was most sizable in persons aged 45 to 64 years (adjusted hazard ratio, 4.98; 95% CI, 2.80-8.85).</div></div><div><h3>Conclusion</h3><div>Muscle density measures from abdominal CT scans may be useful for understanding physiologic changes in the abdomen that place persons at an increased risk of falls as early as middle age.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519477","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":"Unbiased Artificial Intelligence: Addressing Bias in Computational Pathology","authors":"Diana Montezuma MD, PhD ,&nbsp;Rouven Porz PhD ,&nbsp;David Ameisen PhD ,&nbsp;Vincenzo L’Imperio MD ,&nbsp;Mircea-Sebastian Serbanescu MD, PhD ,&nbsp;Jordi Temprana-Salvador MD ,&nbsp;Norman Zerbe PhD ,&nbsp;Nadieh Khalili PhD ,&nbsp;Inti Zlobec PhD ,&nbsp;European Society of Digital and Integrative Pathology (ESDIP)","doi":"10.1016/j.mcpdig.2025.100302","DOIUrl":"10.1016/j.mcpdig.2025.100302","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100302"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519478","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}