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| http://dx.doi.org/10.3389/fphar.2024.1396454 | DOI Listing |
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Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research.
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International Health Policy Program (IHPP), Ministry of Public Health, Nonthaburi, Thailand.
Introduction: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital.
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Department of Heart, Vascular & Thoracic, Division of Cardiology & Cardiovascular Medicine - Pediatric Cardiology, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA.
As the population of adults with congenital heart disease (ACHD) grows, there also grows an expanded need for non-invasive surveillance methods to guide management and intervention. A multimodal imaging approach layers complementary insights from echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and other modalities into a clinician's view of patient physiology. Merely applying strategies from acquired adult cardiac disease would be inadequate and potentially misleading.
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Cardiology, Vascular Medicine & Pneumology, GRN Hospital Weinheim, Weinheim, Germany.
Intelligent biology and medicine: Accelerating innovative computational approaches.
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Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
In this editorial, we summarize the 2023 International Conference on Intelligent Biology and Medicine (ICIBM 2023) conference which was held on July 16-19, 2023 in Tampa, Florida, USA. We then briefly describe the nine research articles included in this special issue. ICIBM 2023 scientific program included four tutorials and workshops, four keynote lectures, four eminent scholars' presentations, 11 concurrent scientific sessions, and a poster session.
View Article and Find Full Text PDFThe TRIPOD-LLM reporting guideline for studies using large language models.
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Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.
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