Predicting Wound Healing Outcomes: A Comparative Accuracy Analysis of AI-driven Indices and Percent Area Reduction

Goldstone, L, Mohammed, HT, Gupta, R, Mustafa, S, Wang, S, Fraser, RDJ, Wynn, M ORCID: 0000-0001-9021-4747 and Allport, J Predicting Wound Healing Outcomes: A Comparative Accuracy Analysis of AI-driven Indices and Percent Area Reduction. BMJ Digital Health &AI. (Accepted)

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Abstract

Background: Wounds represent a major global health and economic burden, with chronic wounds affecting millions annually and costing medical care providers over $126 billion in the US alone. Current assessment tools, such as Percent Area Reduction (PAR), are widely used but limited by subjectivity and suboptimal predictive accuracy, particularly for complex wound types. The growing integration of artificial intelligence (AI) into healthcare provides a unique opportunity to enhance wound assessment and prognostic capabilities, potentially enabling earlier and more precise interventions.

Methods: This retrospective study evaluated the performance of an AI-powered Healing Index (HI) in predicting delayed healing for pressure injuries, venous ulcers, diabetic foot ulcers, and arterial ulcers. Using a clinically validated dataset of 173,816 wounds collected via a digital wound care solution, we compared the HI model’s predictive accuracy to PAR. The HI incorporated objective wound characteristics, such as tissue composition and exudate, to forecast healing trajectories.

Findings: By week 3, the HI achieved a balanced accuracy of 65%, surpassing PAR, which reached the same level only in week 4. This earlier prediction enables more timely treatment adjustments, facilitating improved outcomes and reducing healthcare costs.

Interpretation: The AI-powered HI demonstrates significant potential for transforming wound care by providing more accurate, objective, and earlier identification of non-healing wounds. Its integration into clinical practice could enhance resource allocation, optimise treatment strategies, and reduce the economic burden of chronic wounds. Further validation across diverse healthcare settings is warranted to ensure equitable implementation.

Item Type:	Article
Subjects:	R Medicine > RL Dermatology R Medicine > RT Nursing
Divisions:	Nursing and Advanced Practice
Date of acceptance:	18 February 2026
Date of first compliant Open Access:	10 March 2026
Date Deposited:	10 Mar 2026 15:46
Last Modified:	10 Mar 2026 15:46
DOI or ID number:	10.1136/bmjdh-2026-000069
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28226

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