Accurate and reproducible chronic wound assessment remains challenging in community healthcare, where environmental variability and subjective visual evaluation may introduce substantial measurement errors. Although multi-sensor technologies, including RGB–D imaging, mobile Light Detection and Ranging (LiDAR), thermal infrared imaging, and hyperspectral sensing, as well as artificial intelligence (AI)-based analytics, have advanced considerably, real-world adoption remains limited because of workflow misalignment, insufficient interpretability, and regulatory complexity. This study presents NURSE-AI, a Nurse-by-Design methodological framework for evaluating and preparing multi-sensor, AI-enabled wound assessment systems for deployment in community healthcare. NURSE-AI is proposed as a pre-implementation methodological framework supported by a feasibility study based on a synthetic dataset; therefore, it is not a clinical validation study, and no patient data were used. The framework integrates: (i) a GDPR-compliant synthetic multimodal dataset including RGB, depth, thermal, and hyperspectral-proxy layers; (ii) workflow-embedded acquisition modeling tailored to Family and Community Nurses (FCNs); (iii) a Wound Bed Preparation (WBP)-aligned interpretability layer; and (iv) a governance-by-design checklist addressing interoperability, metadata traceability, and regulatory readiness under Regulation (EU) 2017/745. A mixed-method feasibility evaluation was conducted with community nurses within AUSL Toscana Centro (Italy). The System Usability Scale (SUS) yielded a mean score of 74.5 ± 6.2, indicating good usability. Synthetic multimodal evaluation demonstrated promising segmentation performance under controlled synthetic conditions, with Intersection over Union (IoU) values ranging from 0.87 to 0.93, and simulated Intraclass Correlation Coefficient (ICC) values ≥ 0.90 for wound area estimation. Agreement between AI-generated WBP mappings and nurse interpretation ranged from κ = 0.80 to κ = 0.84. The NURSE-AI framework proposes a structured and reproducible pathway connecting sensor innovation, AI interpretability, nursing workflow integration, and regulatory preparedness, thereby providing structured groundwork for future clinical validation and scalable deployment in community healthcare.
Barchielli et al. (Fri,) studied this question.