Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup's findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence-focused research and applications in anesthesiology. Anesthesiology's progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup's recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence-based tools for perioperative care.
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http://dx.doi.org/10.1097/ALN.0000000000005326 | DOI Listing |
JMIR Med Inform
March 2025
LynxCare Inc, Leuven, Belgium.
Background: Processing data from electronic health records (EHRs) to build research-grade databases is a lengthy and expensive process. Modern arthroplasty practice commonly uses multiple sites of care, including clinics and ambulatory care centers. However, most private data systems prevent obtaining usable insights for clinical practice.
View Article and Find Full Text PDFJMIR Med Inform
March 2025
Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan, 81 432262372.
This study demonstrated that while GPT-4 Turbo had superior specificity when compared to GPT-3.5 Turbo (0.98 vs 0.
View Article and Find Full Text PDFJ Med Internet Res
March 2025
Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia.
Background: Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown.
Objective: This work stems from the Coordinating Health care with AI-supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program.
Methods: Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling.
JMIR Res Protoc
March 2025
Institute for Data Science and Informatics, University of Missouri, Columbia, MO, United States.
Background: Amyotrophic lateral sclerosis (ALS) leads to rapid physiological and functional decline before causing untimely death. Current best-practice approaches to interdisciplinary care are unable to provide adequate monitoring of patients' health. Passive in-home sensor systems enable 24×7 health monitoring.
View Article and Find Full Text PDFOncotarget
March 2025
Worldwide Innovative Network (WIN) Association - WIN Consortium, Chevilly-Larue, France.
The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology.
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