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Beyond Self-consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging
Conference proceeding   Open access   Peer reviewed

Beyond Self-consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging

Chia-Hsuan Chang, Mary M. Lucas, Yeawon Lee, Christopher C. Yang and Grace Lu-Yao
Artificial Intelligence in Medicine, PT I, AIME 2024, v 14844, pp 224-228
01 Jan 2024
DOI: https://doi.org/10.1007/978-3-031-66538-7_23
url
https://doi.org/10.48550/arXiv.2404.13149View
Submitted Open CC BY V4.0

Abstract

Computer Science, Artificial Intelligence Engineering, Biomedical Science & Technology Computer Science Engineering Technology
Pathologic cancer stage, crucial for treatment decisions, is often buried in unstructured pathology reports. This study investigates using pre-trained clinical LLMs for stage extraction, leveraging prompting techniques like chain-of-thought to enhance model transparency. While self-consistency methods further improve LLM performance, they can introduce inconsistencies in reasoning paths and predictions. We propose an ensemble reasoning approach, aiming for reliable cancer stage extraction. Utilizing an open-source clinical LLM on real-world reports, we demonstrate that the ensemble approach improves consistency and boosts performance, paving the way for utilizing LLMs in healthcare settings where reliability and interpretability are paramount.

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Engineering, Biomedical
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