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Models for pure tone audiometry enabling computational evaluation: Introduction to Japanese extensive experiences.
Auris Nasus Larynx
January 2025
Department of Otolaryngology, Faculty of Medicine, Teikyo University, Tokyo, Japan. Electronic address:
Pure tone audiometry including "masking" is the most basic test in audiological medicine. Masking is based on theoretical models of sound perception and propagation and has been widely discussed since the 1950s. In Japan, such discussion has been conducted extensively, starting from early periods up to recent times, with success to enable mathematical simulation, but the achievements have little been disclosed to the English-speaking world.
View Article and Find Full Text PDFMultiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.
ACS Sens
January 2025
Department of Physics and Astronomy, Franklin College of Arts and Sciences, The University of Georgia, Athens, Georgia 30602, United States.
Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.
View Article and Find Full Text PDFThe Clinicians' Guide to Large Language Models: A General Perspective With a Focus on Hallucinations.
Interact J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
View Article and Find Full Text PDFUse of Augmented Reality for Training Assistance in Laparoscopic Surgery: Scoping Literature Review.
J Med Internet Res
January 2025
I3A, LoUISE Research Group, University of Castilla-La Mancha, Albacete, Spain.
Background: Laparoscopic surgery training is a demanding process requiring technical and nontechnical skills. Surgical training has evolved from traditional approaches to the use of immersive digital technologies such as virtual, augmented, and mixed reality. These technologies are now integral to laparoscopic surgery training.
View Article and Find Full Text PDFThe Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets.
JMIR Med Inform
January 2025
Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
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