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AI for all: bridging data gaps in machine learning and health.
Transl Behav Med
January 2025
Slone Epidemiology Center at Boston University, 72 E Concord St, Boston, MA, USA.
Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health through improved diagnoses, treatments, and patient care. However, the effectiveness of these technologies hinges on the quality and diversity of the data used to train them. Many datasets currently used in machine learning are inherently biased and lack diversity, leading to inaccurate predictions that may perpetuate existing health disparities.
View Article and Find Full Text PDFMUTUAL: Towards Holistic Sensing and Inference in the Operating Room.
Med Image Comput Comput Assist Interv
January 2025
Surgical & Interventional Engineering, King's College London, UK.
Embodied AI (E-AI) in the form of intelligent surgical robotics and other agents is calling for data platforms to facilitate its development and deployment. In this work, we present a cross-platform multimodal data recording and streaming software, MUTUAL, successfully deployed on two clinical studies, along with its ROS 2 distributed adaptation, MUTUAL-ROS 2. We describe and compare the two implementations of MUTUAL through their recording performance under different settings.
View Article and Find Full Text PDFCommentary: Increasing accessibility and adaptability of healthy lifestyle interventions for youth survivors of childhood cancer.
J Pediatr Psychol
January 2025
Psychiatry and Behavioral Medicine, Cancer and Blood Disorders Center, Seattle Children's Hospital, Seattle, WA, United States.
From Federal Policy to Bedside Care: A Canadian Perspective on Key Considerations for Nurse Executive Action for AI Adoption - A Commentary.
Nurs Leadersh (Tor Ont)
June 2025
Clinical Practice Leader Corporate Interprofessional Practice Lakeridge Health Durham Region, ON.
The integration of artificial intelligence (AI) into healthcare represents a paradigm shift with the potential to enhance patient care and streamline clinical operations. This commentary explores the Canadian perspective on key organizational considerations for nurse executives, emphasizing the critical role they play in fostering the establishment of AI governance structures and advancing the front-line adoption of AI in nursing practice. The discussion delves into five domains of consideration, analyzing recent developments and implications for nursing executives.
View Article and Find Full Text PDFStandardization for Data Generation and Collection in the Dairy Industry: Addressing Challenges and Charting a Path Forward.
Animals (Basel)
January 2025
Zinpro Corporation, Eden-Prairie, MN 55344, USA.
Standards for data generation and collection are important for integration and for achieving data-driven actionable insights in dairy farming. Data integration and analysis are critical for advancing the dairy industry, enabling better decision-making, and improving operational efficiencies. This commentary paper discusses the challenges of and proposes pathways for standardizing data generation and collection based on insights from a multidisciplinary group of stakeholders.
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