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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 PDFInvisible Monitoring for Athlete Health and Performance: A Call for a Better Conceptualization and Practical Recommendations.
Int J Sports Physiol Perform
January 2025
Department of Sport and Physical Activity, Faculty of Arts and Sciences, Edge Hill University, Ormskirk, United Kingdom.
Background: Practices to routinely monitor athletes are rapidly changing. With the concurrent exponential rise in wearable technologies and advanced data analysis, tracking training exposures and responses is widespread and more frequent in the athlete-coach decision-making process. Within this scenario, the concept of invisible monitoring emerged, which was initially vaguely defined as testing athletes without testing them.
View Article and Find Full Text PDFElegant and Innovative Recoding Strategies for Advancing Vaccine Development.
Vaccines (Basel)
January 2025
Department of Veterinary Microbiology and Immunology, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E2, Canada.
Recoding strategies have emerged as a promising approach for developing safer and more effective vaccines by altering the genetic structure of microorganisms, such as viruses, without changing their proteins. This method enhances vaccine safety and efficacy while minimizing the risk of reversion to virulence. Recoding enhances the frequency of CpG dinucleotides, which in turn activates immune responses and ensures a strong attenuation of the pathogens.
View Article and Find Full Text PDFArtificial intelligence improves bronchoscopy performance: a randomised crossover trial.
ERJ Open Res
January 2025
Copenhagen Academy for Medical Education and Simulation, Rigshospitalet, The Capital Region of Denmark, Copenhagen, Denmark.
Rationale: Flexible bronchoscopy is an operator-dependent procedure. An automatic bronchial identification system based on artificial intelligence (AI) could help bronchoscopists to perform more complete and structured procedures through automatic guidance.
Methods: 101 participants were included from six different continents at the European Respiratory Society annual conference in Milan, 9-13 September 2023.
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