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Introduction <p>Large Language Models (LLMs) are increasingly used in oncology, but their application in neuroendocrine neoplasms (NENs) is still unexplored.</p> Aim <p>To evaluate the accuracy, clarity, and completeness of LLM responses to clinically relevant NEN management questions.</p> Material and methods <p>A study was conducted from October to December 2024, during which a team of experts posed nine key NEN management questions to three LLMs: ChatGPT Plus, Microsoft Copilot, and Perplexity. Responses were assessed by 22 expert physicians across specialties using a 5-point Likert scale based on scientific accuracy, clarity, and completeness, and additionally evaluated by 24 NEN patients for clarity and relevance. Primary outcomes included LLM performance across evaluation criteria and factors influencing ratings, analyzed using a linear mixed-effects model.</p> Results <p>ChatGPT Plus scored highest (M = 3.72, SE = 0.19), followed by Copilot (M = 3.54, SE = 0.19) and Perplexity (M = 3.22, SE = 0.19), with clarity rating the highest. The chemotherapy indications question received the lowest scores, underscoring LLM challenges in handling complex clinical decisions.</p> Discussion <p>This study highlights LLMs’ potential in NEN management as informative tools with clear but variably accurate responses. Continuous improvement and clinician oversight are essential for their successful integration into patient communication.</p>

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Enhancing patient-centered care with AI: a study of responses to neuroendocrine neoplasms queries

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Francesco Panzuto,
  • Alessandro Gallo,
  • Marco Marini,
  • Maria Rinzivillo,
  • Manuela Albertelli,
  • Chiara Maria Grana,
  • Marco Maccauro,
  • Massimo Milione,
  • Salvatore Tafuto,
  • Simona Barbi,
  • Stefano Partelli,
  • Maria Luisa Appetecchia,
  • Giuseppe Badalamenti,
  • Mirco Bartolomei,
  • Rossana Berardi,
  • Francesca Bergamo,
  • Alexia Francesca Bertuzzi,
  • Alessandra Bracigliano,
  • Davide Campana,
  • Mauro Cives,
  • Jorgelina Coppa,
  • Giuseppina Della Vittoria Scarpati,
  • Massimo Falconi,
  • Giuseppe Fanciulli,
  • Fabio Gelsomino,
  • Luca Landoni,
  • Riccardo Laudicella,
  • Stefano Marcucci,
  • Sara Massironi,
  • Roberta Modica,
  • Alessandro Piovesan,
  • Monica Valente,
  • Simona Barbi,
  • Marcello Montanari,
  • Maria Luisa Draghetti,
  • Luciano Licciardello

摘要

Introduction

Large Language Models (LLMs) are increasingly used in oncology, but their application in neuroendocrine neoplasms (NENs) is still unexplored.

Aim

To evaluate the accuracy, clarity, and completeness of LLM responses to clinically relevant NEN management questions.

Material and methods

A study was conducted from October to December 2024, during which a team of experts posed nine key NEN management questions to three LLMs: ChatGPT Plus, Microsoft Copilot, and Perplexity. Responses were assessed by 22 expert physicians across specialties using a 5-point Likert scale based on scientific accuracy, clarity, and completeness, and additionally evaluated by 24 NEN patients for clarity and relevance. Primary outcomes included LLM performance across evaluation criteria and factors influencing ratings, analyzed using a linear mixed-effects model.

Results

ChatGPT Plus scored highest (M = 3.72, SE = 0.19), followed by Copilot (M = 3.54, SE = 0.19) and Perplexity (M = 3.22, SE = 0.19), with clarity rating the highest. The chemotherapy indications question received the lowest scores, underscoring LLM challenges in handling complex clinical decisions.

Discussion

This study highlights LLMs’ potential in NEN management as informative tools with clear but variably accurate responses. Continuous improvement and clinician oversight are essential for their successful integration into patient communication.