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View article: Evaluating clinical AI summaries with large language models as judges
Evaluating clinical AI summaries with large language models as judges Open
Electronic Health Records (EHRs) contain vast clinical data that are difficult for providers to synthesize. Generative AI with Large Language Models (LLMs) can summarize records to reduce cognitive burden, but ensuring accuracy requires re…
View article: Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge
Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge Open
Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize relevant details to their practice. To reduce cognitive load on providers, generative AI wi…
View article: Current and future state of evaluation of large language models for medical summarization tasks
Current and future state of evaluation of large language models for medical summarization tasks Open
Large Language Models have expanded the potential for clinical Natural Language Generation (NLG), presenting new opportunities to manage the vast amounts of medical text. However, their use in such high-stakes environments necessitate robu…
View article: Development and Validation of the Provider Documentation Summarization Quality Instrument for Large Language Models
Development and Validation of the Provider Documentation Summarization Quality Instrument for Large Language Models Open
As Large Language Models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementation. Existing instruments for provider documentation quality …
View article: Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review Open
Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine requires reliable evaluation, which remains a challenge. In …