Leveraging Large Language Models for Precision Monitoring of Chemotherapy-Induced Toxicities: A Pilot Study with Expert Comparisons and Future Directions.
Oskitz Ruiz SarriasMaría Purificación Martínez Del PradoMaria Angeles SalaJosune Azcuna SagarduyPablo Casado CuestaCovadonga Figaredo BerjanoElena Galve-CalvoBorja López de San Vicente HernándezMaría López-SantillánMaitane Nuño EscolásticoLaura Sánchez TogneriLaura Sande SardinaMaría Teresa Pérez HoyosMaría Teresa Abad VillarMaialen Zabalza ZudaireOnintza Sayar BeristainPublished in: Cancers (2024)
This study concludes that LLMs can classify subjective toxicities from chemotherapy with accuracy comparable to expert oncologists. The LLM's performance in general toxicity categories is within the expert range, but there is room for improvement in specific categories. LLMs have the potential to enhance patient monitoring, enable early interventions, and reduce severe complications, improving care quality and efficiency. Future research should involve specific training of LLMs, validation with real patients, and the incorporation of interactive capabilities for real-time patient interactions. Ethical considerations, including data accuracy, transparency, and privacy, are crucial for the safe integration of LLMs into clinical practice.
Keyphrases
- clinical practice
- chemotherapy induced
- end stage renal disease
- case report
- current status
- newly diagnosed
- healthcare
- chronic kidney disease
- physical activity
- quality improvement
- oxidative stress
- peritoneal dialysis
- risk factors
- health information
- risk assessment
- pain management
- depressive symptoms
- human health
- social media
- patient reported
- locally advanced
- data analysis
- affordable care act