This brief editorial describes an emerging area of machine learning technology called large language models (LLMs). LLMs, such as ChatGPT, are the technological disruptor of this decade. They are going to be integrated into search engines (Bing and Google) and into Microsoft products in the coming months. They will therefore fundamentally change the way patients and clinicians access and receive information. It is essential that telehealth clinicians are aware of LLMs and appreciate their capabilities and limitations.
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http://dx.doi.org/10.1177/1357633X231169055 | DOI Listing |
Eur Radiol Exp
January 2025
St Vincent's University Hospital, Dublin, Ireland.
Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).
Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.
J Am Med Inform Assoc
January 2025
Sinclair School of Nursing, University of Missouri, Columbia, MO 65211, United States.
Objective: This study aimed to explore the utilization of a fine-tuned language model to extract expressions related to the Age-Friendly Health Systems 4M Framework (What Matters, Medication, Mentation, and Mobility) from nursing home worker text messages, deploy automated mapping of these expressions to a taxonomy, and explore the created expressions and relationships.
Materials And Methods: The dataset included 21 357 text messages from healthcare workers in 12 Missouri nursing homes. A sample of 860 messages was annotated by clinical experts to form a "Gold Standard" dataset.
J Am Med Inform Assoc
January 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212, United States.
Objective: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.
Materials And Methods: We conducted a systematic literature review and a meta-analysis. The report was created in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 analysis.
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
Facial Plast Surg Aesthet Med
January 2025
Department of Otolaryngology-Head and Neck Surgery, University of California, Irvine, California, USA.
Various large language models (LLMs) can provide human-level medical discussions, but they have not been compared regarding rhinoplasty knowledge. To compare the leading LLMs in answering complex rhinoplasty consultation questions as evaluated by plastic surgeons. Ten open-ended rhinoplasty consultation questions were presented to ChatGPT-4o, Google Gemini, Claude, and Meta-AI LLMs.
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