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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Generative artificial intelligence (AI), particularly large language models (LLMs), is increasingly relevant in oncology due to the field's reliance on text and data.
  • Current adoption involves stand-alone chat models, with a progression towards more sophisticated systems.

Purpose of the Study:

  • To review the current adoption of generative AI in oncology.
  • To outline a practical pathway for integrating AI tools, from basic chat models to advanced agentic assistants.
  • To identify concrete applications and constraints of AI in clinical oncology.

Main Methods:

  • Review of current generative AI adoption in oncology.
  • Conceptualization of a development trajectory for AI tools in healthcare.
  • Identification of potential clinical use cases and technical/ethical challenges.

Main Results:

  • Generative AI offers diverse applications including molecular tumor board synthesis, guideline-based grading, drafting radiology and pathology reports, and computable clinical trial matching.
  • Key constraints include fragmented IT infrastructure, privacy concerns, data provenance, domain variability, and AI hallucinations.
  • A phased adoption strategy is proposed, moving from drafting assistants to embedded tools and finally to event-driven agents.

Conclusions:

  • Generative AI, especially LLMs, holds significant potential to augment oncology care by improving efficiency and supporting clinical decision-making.
  • Successful integration requires addressing technical and ethical challenges and a carefully planned adoption trajectory.
  • The ultimate goal is AI as a supportive learning assistant, enhancing routine care without replacing clinician judgment.