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Leveraging artificial intelligence for decision-making in pediatric progressive and refractory solid tumors.

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Clinical Decision Support

Background:

  • Pediatric patients with refractory solid tumors have poor prognoses despite advanced treatments.
  • Complex cases require multidisciplinary approaches, including precision medicine and clinical trials.
  • Artificial intelligence (AI), especially large language models (LLMs), may enhance clinical reasoning in pediatric oncology.

Purpose of the Study:

  • To evaluate the decision-making capabilities of five AI tools (ChatGPT, Gemini, Claude, Perplexity, OpenEvidence).
  • To assess AI's ability to generate and justify treatment options for hypothetical pediatric refractory solid tumor cases.

Main Methods:

  • Six hypothetical cases of refractory pediatric solid tumors were presented to five AI tools.
  • Each AI tool received two queries: generating treatment options and selecting/justifying the best option.
  • AI-generated recommendations were analyzed for frequency and type.

Main Results:

  • AI tools generated 124 treatment recommendations, averaging 24.8 per tool.
  • Clinical trial enrollment was the most frequent "best option" (55.2%), followed by targeted therapy (17.2%).
  • AI tools displayed varied decision-making tendencies, some favoring aggressive treatment, others supportive care.

Conclusions:

  • AI tools show promise in assisting complex pediatric oncology treatment decisions, particularly in identifying clinical trials.
  • Variability in AI recommendations highlights the need for human oversight to ensure alignment with clinical evidence and patient goals.
  • Future research should focus on refining AI for personalized care and addressing the psychosocial impact of AI-assisted decision-making.