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Artificial intelligence (AI), particularly large language models (LLMs), is revolutionizing clinical cancer research by enabling scalable data analysis for precision oncology. Careful implementation is crucial to address challenges like data privacy and model accuracy.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • AI is increasingly utilized in clinical cancer research to advance precision oncology.
  • Domain-specific AI models have been used, but general-purpose large language models (LLMs) offer scalable data extraction and analysis without extensive labeled datasets.

Purpose of the Study:

  • To explore the applications of LLMs in clinical cancer research.
  • To identify the benefits and challenges associated with using LLMs in this field.

Main Methods:

  • LLMs enable scalable data extraction and analysis.
  • Applications include building clinico-omic databases, patient-trial matching, and developing multimodal foundation models.
  • LLMs can streamline research workflows and accelerate clinical decision-making.

Main Results:

  • LLMs facilitate the integration of diverse data types (text, imaging, molecular).
  • LLMs support advanced applications like patient stratification and clinical trial optimization.
  • LLMs offer potential for automating documentation and improving research efficiency.

Conclusions:

  • LLMs present significant opportunities for advancing cancer research and precision oncology.
  • Key challenges include data privacy, hallucination risks, computational costs, and regulatory hurdles.
  • Strategic implementation of AI tools is essential for future cancer research endeavors.