Developing an AI-powered wound assessment tool: a methodological approach to data collection and model optimization.

Journal: BMC medical informatics and decision making
Published Date: Aug 9, 2025

Abstract

BACKGROUND: Chronic wounds (CWs) represent a significant and growing challenge in healthcare due to their prolonged healing times, complex management, and associated costs. Inadequate wound assessment by healthcare professionals (HCPs), often due to limited training and high clinical workload, contributes to suboptimal treatment and increased risk of complications. This study aimed to develop an artificial intelligence (AI)-powered wound assessment tool, integrated into a mobile application, to support HCPs in diagnosis, monitoring, and clinical decision-making.

Authors

  • Alessio Stefanelli
    Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts, Western Switzerland, Avenue Champel 47, Geneva, CH-1206, Switzerland.
  • Sofia Zahia
    Department of Computer Engineering and Computer Science, Duthie Center for Engineering, University of Louisville, Louisville, KY 40292, United States; eVida research laboratory, University of Deusto, Bilbao 48007, Spain.
  • Guillaume Chanel
    Social Intelligence and MultiSensing (SIMS) lab, University of Geneva, Geneva, Switzerland.
  • Rania Niri
    Computer Science Department, Faculty of Sciences, University of Geneva, Route de Drize 7, Battelle Bat. A, Carouge, Geneva, 1227, Switzerland.
  • Swann Pichon
    Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts, Western Switzerland, Avenue Champel 47, Geneva, CH-1206, Switzerland.
  • Sebastian Probst
    Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts, Western Switzerland, Avenue Champel 47, Geneva, CH-1206, Switzerland. sebastian.probst@hesge.ch.

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