To develop an evidence-informed, clinically aligned multimodal workflow framework for chronic wound assessment through structured mapping and synthesis of the literature on image-based artificial intelligence (AI) applications.
Approach:
Key Findings:
The study identified major task types, imaging modalities, and technical design patterns in wound-AI research.
A clinically aligned conceptual workflow framework was developed, emphasizing integration of AI functions in wound assessment.
The framework links technical patterns to clinically meaningful stages of assessment.
Interpretation:
Limitations:
Many published models are based on small or homogeneous datasets, limiting generalizability.
Existing systems are often task-specific and not integrated into clinical workflows.
Heterogeneous evaluation metrics and inconsistent reporting hinder comparability across studies.
