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<p>Neuroendocrine neoplasms (NENs) are rare and heterogeneous malignancies requiring multidisciplinary management. Large language models (LLMs) are emerging as decision-support tools, but their role in therapeutic decision-making is largely unexplored. ARTEMIS was a pilot cross-sectional study comparing three configurations—a baseline GPT, a customised GPT with static domain knowledge (GPTs), and a retrieval-augmented GPT (RAG)—against a panel of nine Italian NEN experts using twenty simulated, non-surgical cases. The primary endpoint was non-inferiority for systemic therapy recommendations; secondary endpoints included completeness, explicit uncertainty, parsimony of additional tests, costs, and variability metrics. RAG and GPTs achieved 70.0% agreement versus the expert benchmark (63.8%), meeting the exploratory –10% non-inferiority margin but not the stricter –5% threshold. Baseline GPT reached 60.0% and was not non-inferior. All AI systems consistently produced complete recommendations and expressed uncertainty more often than experts; RAG tended to propose fewer additional tests and lower associated costs. Experts showed greater variability than AI systems, and Ki-67 correlated with disagreement, indicating biological aggressiveness as a source of uncertainty. This exploratory study suggests that LLMs can approximate expert therapeutic reasoning under controlled conditions, but concordance remains limited and external validation in real-world settings is needed before clinical use.</p>

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ARTEMIS: a pilot study comparing AI-based and expert therapeutic decisions in simulated clinical cases of neuroendocrine neoplasms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • Giuseppe Lamberti,
  • Francesco Panzuto,
  • Sara Massironi,
  • Mauro Cives,
  • Anna La Salvia,
  • Francesca Spada,
  • Antongiulio Faggiano,
  • Sara Pusceddu,
  • Manuela Albertelli,
  • Salvatore Tafuto,
  • Elisa Andrini,
  • Claudio Ricci,
  • Davide Campana

摘要

Neuroendocrine neoplasms (NENs) are rare and heterogeneous malignancies requiring multidisciplinary management. Large language models (LLMs) are emerging as decision-support tools, but their role in therapeutic decision-making is largely unexplored. ARTEMIS was a pilot cross-sectional study comparing three configurations—a baseline GPT, a customised GPT with static domain knowledge (GPTs), and a retrieval-augmented GPT (RAG)—against a panel of nine Italian NEN experts using twenty simulated, non-surgical cases. The primary endpoint was non-inferiority for systemic therapy recommendations; secondary endpoints included completeness, explicit uncertainty, parsimony of additional tests, costs, and variability metrics. RAG and GPTs achieved 70.0% agreement versus the expert benchmark (63.8%), meeting the exploratory –10% non-inferiority margin but not the stricter –5% threshold. Baseline GPT reached 60.0% and was not non-inferior. All AI systems consistently produced complete recommendations and expressed uncertainty more often than experts; RAG tended to propose fewer additional tests and lower associated costs. Experts showed greater variability than AI systems, and Ki-67 correlated with disagreement, indicating biological aggressiveness as a source of uncertainty. This exploratory study suggests that LLMs can approximate expert therapeutic reasoning under controlled conditions, but concordance remains limited and external validation in real-world settings is needed before clinical use.