Large Language Models (LLMs) are a type of artificial intelligence that has been revolutionizing various fields, including biomedicine. They have the capability to process and analyze large amounts of data, understand natural language, and generate new content, making them highly desirable in many biomedical applications and beyond. In this workshop, we aim to introduce the attendees to an in-depth understanding of the rise of LLMs in biomedicine, and how they are being used to drive innovation and improve outcomes in the field, along with associated challenges and pitfalls.
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Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models.
J Pathol Clin Res
January 2025
Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
Causal language and inferences in observational rotator cuff database studies published from 2013 to 2022.
J Orthop
July 2025
Department of Orthopaedic Surgery, Virginia Commonwealth University, 1250 E. Marshall Street, Richmond, VA, 23219, USA.
Background: The use of large multi-institutional databases in rotator cuff repair (RCR) research is expanding, but these studies are observational and cannot establish causation. This study examines the prevalence of causal language in clinical RCR database studies published from 2013 to 2022.
Methods: Administrative database and clinical registry studies on RCR published in eight orthopaedic journals from 2013 to 2022 were systematically identified and graded by two reviewers for the presence, absence, or inconsistent use of causal language in both the title/abstract and the full text.
Evaluation of ChatGPT-4 Performance in Answering Patients' Questions About the Management of Type 2 Diabetes.
Sisli Etfal Hastan Tip Bul
December 2024
Department of Endocrinology and Metabolic Diseases, Ankara Training and Research Hospital, Ankara, Türkiye.
Objectives: Type 2 diabetes mellitus is a disease with a rising prevalence worldwide. Person-centered treatment factors, including comorbidities and treatment goals, should be considered in determining the pharmacological treatment of type 2 diabetes. ChatGPT-4 (Generative Pre-trained Transformer), a large language model, holds the potential performance in various fields, including medicine.
View Article and Find Full Text PDFIdentification of Behavioral, Clinical, and Psychological Antecedents of Acute Stimulant Poisoning: Development and Implementation of a Mixed Methods Psychological Autopsy Study.
JMIR Form Res
January 2025
Center on Substance Use and Health, San Francisco Department of Public Health, San Francisco, CA, United States.
Background: Despite increasing fatal stimulant poisoning in the United States, little is understood about the mechanism of death. The psychological autopsy (PA) has long been used to distinguish the manner of death in equivocal cases, including opioid overdose, but has not been used to explicitly explore stimulant mortality.
Objective: We aimed to develop and implement a large PA study to identify antecedents of fatal stimulant poisoning, seeking to maximize data gathering and ethical interactions during the collateral interviews.
Use of generative large language models for patient education on common surgical conditions: a comparative analysis between ChatGPT and Google Gemini.
Updates Surg
January 2025
Alluri Sitarama Raju Academy of Medical Sciences, Eluru, India.
There is a growing importance for patients to easily access information regarding their medical conditions to improve their understanding and participation in health care decisions. Artificial Intelligence (AI) has proven as a fast, efficient, and effective tool in educating patients regarding their health care conditions. The aim of the study is to compare the responses provided by AI tools, ChatGPT and Google Gemini, to assess for conciseness and understandability of information provided for the medical conditions Deep vein thrombosis, decubitus ulcers, and hemorrhoids.
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