AI Article Synopsis
- Large language models (LLMs) are new types of artificial intelligence that could greatly change how doctors treat people with epilepsy.
- This review looks at how LLMs can help doctors by using information from patients' medical history and electronic health records.
- The text also talks about different LLM studies, their benefits, and the challenges they face, along with suggestions to make them better.
Article Abstract
The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/epi.17907 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFTwo-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study.
J Med Internet Res
January 2025
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.
Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.
The PDC30 Chatbot-Development of a Psychoeducational Resource on Dementia Caregiving Among Family Caregivers: Mixed Methods Acceptability Study.
JMIR Aging
January 2025
Department of Computing, Faculty of Computer and Mathematical Sciences, Hong Kong Polytechnic University, Hung Hom, China (Hong Kong).
Background: Providing ongoing support to the increasing number of caregivers as their needs change in the long-term course of dementia is a severe challenge to any health care system. Conversational artificial intelligence (AI) operating 24/7 may help to tackle this problem.
Objective: This study describes the development of a generative AI chatbot-the PDC30 Chatbot-and evaluates its acceptability in a mixed methods study.
Transforming literature screening: The emerging role of large language models in systematic reviews.
Proc Natl Acad Sci U S A
January 2025
Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany.
Systematic reviews (SR) synthesize evidence-based medical literature, but they involve labor-intensive manual article screening. Large language models (LLMs) can select relevant literature, but their quality and efficacy are still being determined compared to humans. We evaluated the overlap between title- and abstract-based selected articles of 18 different LLMs and human-selected articles for three SR.
View Article and Find Full Text PDFBurn Patient Education in the Modern Age: A Comparative Analysis of ChatGPT and Google Performance Answering Common Questions on Burn Injury and Management.
J Burn Care Res
January 2025
Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, United States.
Patients often use Google for their medical questions. With the emergence of artificial intelligence large language models, such as ChatGPT, patients may turn to such technologies as an alternative source of medical information. This study investigates the safety, accuracy, and comprehensiveness of medical responses provided by ChatGPT in comparison to Google for common questions about burn injuries and their management.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!