98%
921
2 minutes
20
Artificial intelligence (AI) in surgery literature typically encompasses decision support models that aim to help clinicians make better decisions. Many studies report developing and validating models, yet few models are implemented at the bedside. Exceedingly few models achieve their intended goal upon implementation. While the TRIPOD-AI and DECIDE-AI guidelines outline separate reporting standards for the development/validation, and staged implementation of AI models, respectively, this article outlines how future implementation should be considered at the outset before model development. Building on lessons from high-performing AI decision support models that faced challenges upon implementation, we will discuss study design consideration for building trustworthy and actionable AI clinical decision support models that can cross the database-to-bedside gap and become successfully implemented.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/TA.0000000000004725 | DOI Listing |
Health Inf Manag
September 2025
Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: The success of disease registry systems (DRSs) depends on developing software that aligns with the registry's specific needs.
Objective: This study focuses on localising the Checklist with Items for Patient Registry sOftware Systems (CIPROS) to facilitate the DRS assessment.
Method: This applied and cross-sectional study was carried out in 2023 in six phases.
Mol Ther Methods Clin Dev
June 2025
Precision Safety, Pharma Product Development, Roche Innovation Center Basel, CH-4070 Basel, Switzerland.
Adeno-associated virus (AAV) vectors are widely used in gene therapy, particularly for liver-targeted treatments. However, predicting human-specific outcomes, such as transduction efficiency and hepatotoxicity, remains challenging. Reliable models are urgently needed to bridge the gap between preclinical studies and clinical applications.
View Article and Find Full Text PDFFront Rehabil Sci
August 2025
Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
Introduction: Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.
Methods: We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes.
Front Immunol
September 2025
Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
In the last decades, immunotherapy has revolutionized cancer treatment. Despite its success, a significant number of patients fail to respond, and the underlying causes of ineffectiveness remain poorly understood. Factors such as nutritional status and body composition are emerging as key predictors of immunotherapy outcomes.
View Article and Find Full Text PDFFront Immunol
September 2025
Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
NSG-SGM3 humanized mouse models are well-suited for studying human immune physiology but are technically challenging and expensive. We previously characterized a simplified NSG-SGM3 mouse, engrafted with human donor CD34 hematopoietic stem cells without receiving prior bone marrow ablation or human secondary lymphoid tissue implantation, that still retains human mast cell- and basophil-dependent passive anaphylaxis responses. Its capacities for human antibody production and human B cell maturation, however, remain unknown.
View Article and Find Full Text PDF