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Leveraging Large Language Models for Precision Monitoring of Chemotherapy-Induced Toxicities: A Pilot Study with Expert Comparisons and Future Directions

Medicine and Health

Leveraging Large Language Models for Precision Monitoring of Chemotherapy-Induced Toxicities: A Pilot Study with Expert Comparisons and Future Directions

O. R. Sarrias, M. P. M. D. Prado, et al.

This study evaluated the capacity of Large Language Models to classify chemotherapy toxicities, finding them to match the accuracy of expert oncologists in general toxicity ratings. The authors highlight the potential of LLMs for improving patient monitoring while alleviating the burden on medical professionals. The research was conducted by Oskitz Ruiz Sarrias and colleagues.

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Playback language: English
Abstract
This study evaluated the ability of Large Language Models (LLMs) to classify subjective toxicities from chemotherapy by comparing them with expert oncologists. Using fictitious cases, it was demonstrated that LLMs can achieve accuracy similar to that of oncologists in general toxicity categories, although they need improvement in specific categories. LLMs show great potential for enhancing patient monitoring and reducing the workload of doctors. Future research should focus on training LLMs specifically for medical tasks and validating these findings with real patients, always ensuring accuracy and ethical data management.
Publisher
Cancers
Published On
Jun 28, 2024
Authors
Oskitz Ruiz Sarrias, María Purificación Martínez del Prado, María Ángeles Sala Gonzalez, Josune Azcuna Sagarduy, Pablo Casado Cuesta, Covadonga Figaredo Berjano, Elena Galve-Calvo, Borja López de San Vicente Hernández, María López-Santillán, Maitane Nuño Escolástico, Laura Sánchez Togneri, Laura Sande Sardina, María Teresa Pérez Hoyos, María Teresa Abad Villar, Maialen Zabalza Zudaire, Onintza Sayar Beristain
DOI
https://doi.org/10.3390/cancers16162830
Tags
Large Language Models
chemotherapy
toxicities
oncologists
patient monitoring
accuracy
medical tasks

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