Observational assessments in the clinical context are a cornerstone of evaluation in medical education. Leniency bias, described in performance management in the business arena appears to widely impact these assessments with medical training. Natural language processing provides a potential tool that medical educators may leverage to decipher underlying meaning in narrative assessment. A "proof-of-concept" study at the Cumming School of Medicine supports this notion and suggests further work would be a worthwhile pursuit in this field.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.amjsurg.2023.11.028 | DOI Listing |
Lung Cancer
January 2025
Dept. of Medical Oncology, Princess Margaret Cancer Center, Toronto, ON, Canada.
Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.
View Article and Find Full Text PDFJ Speech Lang Hear Res
January 2025
Department of Psychology, University of Western Ontario, London, Canada.
Purpose: Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification of language disorders in children.
Method: We first describe the current barriers to clinicians' use of language sample analysis, narrative language sampling approaches, and the data processing stages that precede analysis.
PLoS One
January 2025
Department of English and Communication, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
This study aims to provide an LLM (Large Language Model)-based method for the discourse analysis of media attitudes, and thereby investigate media attitudes towards China in a Hong Kong-based newspaper. Analysis of attitudes in large amounts of media data is crucial for understanding public opinions, market trends, social dynamics, etc. However, corpus-based approaches have traditionally focused on explicit linguistic expressions of attitudes, leaving implicit expressions unconsidered.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Pediatrics and Child Health, Makerere University, College of Health Sciences, Kampala, Uganda.
Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, it has introduced new challenges and threats to medical education. We assessed the use of ChatGPT and other AI tools among medical students in Uganda.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Kennewick, WA 99338, United States.
Objective: This study evaluates the utility of word embeddings, generated by large language models (LLMs), for medical diagnosis by comparing the semantic proximity of symptoms to their eponymic disease embedding ("eponymic condition") and the mean of all symptom embeddings associated with a disease ("ensemble mean").
Materials And Methods: Symptom data for 5 diagnostically challenging pediatric diseases-CHARGE syndrome, Cowden disease, POEMS syndrome, Rheumatic fever, and Tuberous sclerosis-were collected from PubMed. Using the Ada-002 embedding model, disease names and symptoms were translated into vector representations in a high-dimensional space.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!