ArticleDigital health
Validity of evidence-based recommendations by a large language model for interdisciplinary board decisions in neurooncology: An explorative study and critical evaluation.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aims to evaluate the stylistic and structural equivalence of Artificial Intelligence (AI)-generated summaries, particularly those by Large Language Models (LLMs) like ChatGPT, compared to traditional human-generated case summaries in neuro-oncological board decisions. The primary goal is to explore the stylistic alignment between AI-generated and human-authored summaries from board meeting audio recordings. Methods: The study compares 30 traditional human-generated case summaries with 30 AI-generated summaries based on board meeting audio recordings. Two expert raters, blinded to the source of the summaries, evaluated a total of 60 cases. A Likert scale was used to assess the plausibility, linguistic style, evidence adherence, and reference accuracy of the summaries. Results: The results indicated that both LLM-generated and human-reviewed summaries demonstrated consistently high performance across all criteria evaluated. The general plausibility ratings were comparable (LLM: 4.7, Human: 4.73, Conclusion: The study finds that LLM-generated summaries can effectively emulate the style and structure of human-authored ones, indicating their promise as an additional tool in neuro-oncology. These AI models can enhance documentation quality and serve as valuable support in clinical settings. While further research is necessary to explore broader applications, LLMs offer exciting potential as a complement to traditional decision-making processes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.