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Area of Science:

  • Computational Radiobiology
  • Artificial Intelligence in Medicine
  • Large Language Models

Background:

  • Large language models (LLMs) like ChatGPT-4o show promise in various fields.
  • Assessing their accuracy in specialized domains like radiobiology is crucial.
  • Radiobiology problems often require complex calculations and clinical reasoning.

Purpose of the Study:

  • To evaluate the accuracy of ChatGPT-4o in solving computational radiobiology problems.
  • To identify areas where ChatGPT-4o performs well and where it struggles.
  • To explore the impact of prompting strategies on performance.

Main Methods:

  • A problem set of 30 radiobiology questions was created by radiation oncology experts.
  • ChatGPT-4o (GPT-4o model) was queried via the OpenAI API to generate answers.
  • Answers were graded, and subgroup analyses were performed (zero-shot, prompting, alpha-beta ratio, question type).

Main Results:

  • ChatGPT-4o answered approximately 60% of questions correctly without specific prompting.
  • Performance was stable in structured calculation problems, especially those with alpha-beta ratios.
  • Limitations were observed in multi-step reasoning and clinical decision-making tasks.

Conclusions:

  • ChatGPT-4o shows potential but requires further refinement for practical radiobiology applications.
  • Integrating professional tools and optimizing prompting strategies are necessary.
  • Current LLM capabilities are insufficient for complex clinical decision support in radiobiology.