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Area of Science:

  • Artificial Intelligence in Medicine
  • Oncology Informatics

Background:

  • Large language models (LLMs) exhibit advanced natural language processing capabilities.
  • Their integration into healthcare, particularly oncology, is gaining traction for data synthesis and decision support.

Purpose of the Study:

  • To review the current status of LLMs in medicine.
  • To explore LLM applications in oncology for clinicians, patients, and research.
  • To identify future research directions and challenges.

Main Methods:

  • This study is a narrative review of existing literature on LLMs in oncology.
  • It synthesizes information on current applications and potential future developments.

Main Results:

  • Clinician-facing LLMs can aid decision-making and automate data extraction.
  • Patient-facing LLMs may improve information dissemination and psychosocial support.
  • Key limitations include hallucinations, generalization issues, and ethical concerns.

Conclusions:

  • LLMs offer significant potential to augment oncologists' expertise and enhance patient care.
  • Integrating LLMs into compound AI systems can improve efficiency and adoption.
  • Active clinician involvement is crucial for developing and refining LLM technologies in oncology.