Journal of the American Medical Informatics Association : JAMIA
39836495
OBJECTIVE: Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimic...
BACKGROUND: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting...
BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) like ChatGPT have transformed information retrieval, including in healthcare. ChatGPT, trained on diverse datasets, can provide medical advice but faces ethical and accuracy co...
OBJECTIVES: We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records.
Retrieval-augmented generation (RAG) involves a solution by retrieving knowledge from an established database to enhance the performance of large language models (LLM). , these models retrieve information at the sentence or paragraph level, potential...
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 th...
International journal of surgery (London, England)
39903558
The advancement of large language models (LLMs) presents promising opportunities to enhance evidence synthesis efficiency, particularly in data extraction processes, yet existing prompts for data extraction remain limited, focusing primarily on commo...
Journal of the American Medical Informatics Association : JAMIA
39823371
OBJECTIVE: Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of P...
Journal of the American Medical Informatics Association : JAMIA
39798153
OBJECTIVE: To develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records.
Journal of the American Medical Informatics Association : JAMIA
39812777
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.