Background: Surgery demands long hours and intense exertion raising ergonomic concerns. We piloted a sensorless artificial intelligence (AI)-assisted ergonomics analysis app to determine its feasibility for use with residents.
Methods: Surgery residents performed simulated laparoscopic tasks before and after a review of the SCORE ergonomics curriculum while filmed with a sensorless app from Kinetica Labs that calculates joint angles as a metric of ergonomics. A survey was completed before the session and a focus group was conducted after.
Results: Thirteen surgical residents participated in the study. The brief intervention took little time and residents improved their ergonomic scores in neck and right shoulder angles. Residents expressed increased awareness of ergonomics based on the session content and AI information. All trainees desired more training in ergonomics.
Conclusions: Ergonomic assessment AI software can provide immediate feedback to surgical trainees to improve ergonomics. Additional studies using sensorless AI technology are needed.
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http://dx.doi.org/10.1016/j.amjsurg.2023.07.028 | DOI Listing |
J Med Internet Res
January 2025
Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Background: Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Smith School of Business, Queen's University, Kingston, ON, Canada.
Background: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Medical Information Department, Civil Hospices of Lyon, Lyon, France.
J Med Internet Res
January 2025
School of Computer Science, University of Technology Sydney, Sydney, Australia.
The integration of artificial intelligence (AI) into health communication systems has introduced a transformative approach to public health management, particularly during public health emergencies, capable of reaching billions through familiar digital channels. This paper explores the utility and implications of generalist conversational artificial intelligence (CAI) advanced AI systems trained on extensive datasets to handle a wide range of conversational tasks across various domains with human-like responsiveness. The specific focus is on the application of generalist CAI within messaging services, emphasizing its potential to enhance public health communication.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
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