Image Based Artificial Intelligence in Wound Assessment: A Systematic Review
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Abstract (arXiv)
Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population. While artificial intelligence (AI) has found wide applications in health-related sciences and technology, AI-based systems remain to be developed clinically and computationally for high-quality wound care. To this end, we have carried out a systematic review of intelligent image-based data analysis and system developments for wound assessment. Specifically, we provide an extensive review of research methods on wound measurement (segmentation) and wound diagnosis (classification). We also reviewed recent work on wound assessment systems (including hardware, software, and mobile apps). More than 250 articles were retrieved from various publication databases and online resources, and 115 of them were carefully selected to cover the breadth and depth of most recent and relevant work to convey the current review to its fulfillment.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 5.
10 papers.
- 2024SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videoscited by 4
- 2023Development of a deep learning-based tool to assist wound classificationcited by 3
- 2025Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Datasetcited by 2
- 2022Wound Severity Classification using Deep Neural Networkcited by 1
- 2023Advancing Wound Filling Extraction on 3D Faces: Auto-Segmentation and Wound Face Regeneration Approachcited by 0
- 2026Non-Invasive 3D Wound Measurement with RGB-D Imagingcited by 0
- 2026Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classificationcited by 0
- 2026WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentationcited by 0
- 2026Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoringcited by 0
- 2026In-Context Learning for Wound Classification with Small Multimodal Language Modelscited by 0
- 2026In-Context Learning for Wound Classification with Small Multimodal Language Models21 Jul 2026
- 2026Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring6 Jul 2026
- 2026WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentation19 May 2026
- 2026Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification28 Mar 2026
- 2026Non-Invasive 3D Wound Measurement with RGB-D Imaging26 Jan 2026
- 2025Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Dataset15 Feb 2025
- 2024SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos29 Jul 2024
- 2023Advancing Wound Filling Extraction on 3D Faces: Auto-Segmentation and Wound Face Regeneration Approach4 Jul 2023
- 2023Development of a deep learning-based tool to assist wound classification29 Mar 2023
- 2022Wound Severity Classification using Deep Neural Network17 Apr 2022