Soddu Marica, De Vito Andrea, Madeddu Giordano, Nicolosi Biagio, Provenzano Maria, Ivziku Dhurata, Curcio Felice
University Hospital of Sassari, Viale San Pietro 10, 07100 Sassari, Italy.
Department of Medicine, Surgery, and Pharmacy, University of Sassari, 07100 Sassari, Italy.
Nurs Rep. 2025 Apr 14;15(4):130. doi: 10.3390/nursrep15040130.
: The advent of large language models (LLMs), like platforms such as ChatGPT, capable of generating quick and interactive answers to complex questions, opens the way for new approaches to training healthcare professionals, enabling them to acquire up-to-date and specialised information easily. In nursing, they have proven to support clinical decision making, continuing education, the development of care plans and the management of complex clinical cases, as well as the writing of academic reports and scientific articles. Furthermore, the ability to provide rapid access to up-to-date scientific information can improve the quality of care and promote evidence-based practice. However, their applicability in clinical practice requires thorough evaluation. This study evaluated the accuracy, completeness and safety of the responses generated by ChatGPT-4 on pressure injuries (PIs) in infants. : In January 2025, we analysed the responses generated by ChatGPT-4 to 60 queries, subdivided into 12 main topics, on PIs in infants. The questions were developed, through consultation of authoritative documents, based on their relevance to nursing care and clinical potential. A panel of five experts, using a 5-point Likert scale, assessed the accuracy, completeness and safety of the answers generated by ChatGPT. Overall, over 90% of the responses generated by ChatGPT-4o received relatively high ratings for the three criteria assessed with the most frequent value of 4. However, when analysing the 12 topics individually, we observed that Medical Device Management and Technological Innovation were the topics with the lowest accuracy scores. At the same time, Scientific Evidence and Technological Innovation had the lowest completeness scores. No answers for the three criteria analysed were rated as completely incorrect. ChatGPT-4 has shown a good level of accuracy, completeness and safety in addressing questions about pressure injuries in infants. However, ongoing updates and integration of high-quality scientific sources are essential for ensuring its reliability as a clinical decision-support tool.
像ChatGPT这样的大语言模型（LLMs）的出现，能够对复杂问题生成快速且交互式的答案，为培训医疗保健专业人员开辟了新途径，使他们能够轻松获取最新的专业信息。在护理领域，这些模型已被证明可支持临床决策、继续教育、护理计划制定以及复杂临床病例管理，还能用于撰写学术报告和科学文章。此外，能够快速获取最新科学信息可提高护理质量并促进循证实践。然而，它们在临床实践中的适用性需要全面评估。本研究评估了ChatGPT - 4针对婴儿压力性损伤（PIs）所生成回答的准确性、完整性和安全性。2025年1月，我们分析了ChatGPT - 4对60个关于婴儿压力性损伤的问题所给出的回答，这些问题分为12个主要主题。这些问题是通过查阅权威文件，依据其与护理和临床潜力的相关性而制定的。一个由五名专家组成的小组使用5级李克特量表评估了ChatGPT生成答案的准确性、完整性和安全性。总体而言，ChatGPT - 4生成的回答中，超过90%在评估的三个标准上获得了相对较高的评分，最常见的值为4。然而，在单独分析这12个主题时，我们发现医疗器械管理和技术创新是准确性得分最低的主题。同时，科学证据和技术创新的完整性得分最低。在分析的三个标准中，没有答案被评为完全错误。ChatGPT - 4在回答有关婴儿压力性损伤的问题时显示出了较好的准确性、完整性和安全性。然而，持续更新和整合高质量科学资源对于确保其作为临床决策支持工具的可靠性至关重要。
