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Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.
JMIR Form Res
January 2025
Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns.
View Article and Find Full Text PDFMoving Away from the Blame Culture: The Way Forward to Manage Medical Errors.
Malays J Med Sci
December 2024
Department of Medical Ethics and Law, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia.
When a medical error occurs, the instinct to blame healthcare professionals may seems like a way to ensure they learn from their mistakes. However, in today's healthcare landscape, the blame culture, coupled with the fear of litigation, proves detrimental to improving patient care. This culture fosters a reluctance among healthcare professionals to openly disclose mistakes, depriving them of valuable learning opportunities.
View Article and Find Full Text PDFDeep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis.
JHEP Rep
February 2025
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Background & Aims: Biliary abnormalities in autoimmune hepatitis (AIH) and interface hepatitis in primary biliary cholangitis (PBC) occur frequently, and misinterpretation may lead to therapeutic mistakes with a negative impact on patients. This study investigates the use of a deep learning (DL)-based pipeline for the diagnosis of AIH and PBC to aid differential diagnosis.
Methods: We conducted a multicenter study across six European referral centers, and built a library of digitized liver biopsy slides dating from 1997 to 2023.
A preliminary hospital-based study of a hybrid placement incorporating traditional and non-traditional placement and supervisory model elements: Perspectives of undergraduate speech-language pathology.
Int J Speech Lang Pathol
January 2025
Speech Pathologist, Western Sydney Local Health District, North Parramatta, NSW, USA.
Purpose: This preliminary study sought to explore speech-language pathology students' perspectives of a novel placement experience embedding traditional and non-traditional placement and supervisory model-elements in a hospital setting.
Method: A mixed-method sequential explanatory design was used, incorporating an online survey comprising of 26 questions and a focus group. Descriptive statistics were obtained and a reflexive thematic approach was used to analyse the transcripts.
The ChatGPT Fact-Check: Exploiting the Limitations of Generative AI to Develop Evidenced-Based Reasoning Skills in College Science Courses.
Adv Physiol Educ
January 2025
Department of Biological Sciences, San Jose State University, San Jose, CA 95192, USA.
Generative large language models (LLMs) like ChatGPT can quickly produce informative essays on various topics. However, the information generated cannot be fully trusted as artificial intelligence (AI) can make factual mistakes. This poses challenges for using such tools in college classrooms.
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