The reliability-clarity paradox in vascular digital health: A nurse-led evaluation of ChatGPT-4's accuracy in diabetic foot ulcer management.

BACKGROUND

Diabetic foot ulcers remain a leading cause of non-traumatic amputations worldwide, necessitating precise patient education and clinical management. As large language models become increasingly integrated into patient-facing platforms, evaluating their clinical safety within specialized nursing contexts is imperative. This study aimed to examine the performance of ChatGPT-4 in DFU management, focusing particularly on linguistic clarity and scientific accuracy.

METHODS

A cross-sectional evaluative design was employed in April 2025. An expert panel of six certified wound care nurses, each possessing over a decade of clinical experience, assessed AI-generated responses to seven core clinical inquiries (encompassing 31 subtopics). Evaluation was conducted using a 5-point Likert scale. Methodological rigor was established through Content Validity Index (CVI) and Intraclass Correlation Coefficients (ICC) to determine expert consensus.

RESULTS

ChatGPT-4 exhibited exceptional performance in linguistic clarity, scoring 152.0 out of 155 based on expert evaluation. This high clarity refers to the model's ability to translate complex medical data into accessible language. However, a significant decline in scientific accuracy was observed when addressing formal clinical protocols (6.67/10), particularly regarding the International Working Group on the Diabetic Foot (IWGDF) 2023 updates. Despite these inaccuracies, the expert panel demonstrated a high level of consensus (ICC = 0.937) regarding the model's limitations.

CONCLUSIONS

The study identifies a 'reliability-clarity paradox' where ChatGPT-4's high linguistic fluency (152/155) creates an authoritative tone that effectively masks underlying clinical inaccuracies. This is evidenced by a significant drop in scientific precision (6.67/10), particularly regarding the integration of IWGDF 2023 updates. The exceptional inter-rater consensus (ICC = 0.937) further confirms that these clinical risks are systematic rather than subjective. Consequently, while AI can assist in administrative drafting, the specialized nurse's role as a 'clinical gatekeeper' remains vital to provide a necessary safety filter and ensure evidence-based accuracy in DFU care.