AI tool designed and integrated into hospital workflows to classify skin lesions
· Medical Xpressedited by Gaby Clark, reviewed by Robert Egan
Gaby Clark
Scientific Editor
Meet our editorial team
Behind our editorial process
Robert Egan
Senior Editor
Meet our editorial team
Behind our editorial process Editors' notes
This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:
fact-checked
trusted source
proofread
The GIST Add as preferred source
Skin cancer is a global health challenge, with nearly 1.5 million new cases diagnosed in 2024, according to World Health Organization (WHO) data. In response, the University of Alicante (UA) and the Sant Joan d'Alacant University Hospital have collaborated to launch MEL-IA (MobilE skin Lesion dIAgnosis), an artificial intelligence system designed to automate the classification of skin lesions.
Early detection remains vital to improving clinical outcomes for skin cancer. This technology supports health care staff in evaluating lesions while integrating into the hospital's existing IT infrastructure.
The system incorporates a mobile app to capture images of skin lesions—such as moles or spots—and record clinical data, an AI model to perform the classification, and complex integration models that securely connect all data with hospital systems.
The authors of this study, published in the journal Journal of Medical Systems, are Alberto de Ramón, Daniel Ruiz and Marcelo Saval, lecturers in the Department of Computer Technology and Computation at the UA, together with Pablo Candela, all members of the Bio-inspired Engineering and Health Informatics (IBIS) research group at the UA.
The IBIS group has spent over a decade researching clinical decision-support systems for diagnosing skin lesions. Representing the Sant Joan d'Alacant University Hospital, José María Salinas and Diego Guijarro from the IT Service co-authored the work.
Beyond developing the classification algorithm, the team has delivered a comprehensive technology package capable of integrating directly into a hospital's IT network. The system links image capture, AI analysis, secure storage and the confidential exchange of clinical data.
Skin lesions and diagnostic performance
Unlike tools designed solely to distinguish between malignant and benign lesions, MEL-IA provides differential categorization across five major skin lesion types: melanoma, nevus, basal cell carcinoma, actinic keratosis and benign keratosis.
To train and validate the model, researchers integrated over 15,000 dermatoscopic images alongside clinical patient data, including age, sex and anatomical lesion location, to enhance classification accuracy.
Results indicate that the system achieves an overall accuracy of 86%. The UA researchers noted that MEL-IA achieved a sensitivity of 88% for melanoma detection and 92% for basal cell carcinoma detection, adding that the highest overall scores corresponded to nevi and basal cell carcinomas.
The technology moved beyond the experimental stage to deployment at the Sant Joan d'Alacant University Hospital to evaluate performance in a live health care environment. The team highlighted that they processed 980 dermatological studies with response times under one second.
Furthermore, the system maintains a longitudinal record of lesions, incorporating images, diagnoses and medical observations over time.
The researchers emphasize that MEL-IA serves as a clinical decision-support tool rather than an autonomous diagnostic system. Key next steps include conducting prospective studies with health care professionals, testing direct image acquisition via smartphones and expanding the range of lesion types the system can classify.
More information
Pablo Candela Córcoles et al, MEL-IA: An Interoperable AI System for Multimodal Skin Lesion Classification in Hospital Settings, Journal of Medical Systems (2026). DOI: 10.1007/s10916-026-02424-y
Key medical concepts
Clinical categories
Dermatology Provided by University of Alicante Who's behind this story?
Gaby Clark
MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile →
Robert Egan
Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile →
Citation: AI tool designed and integrated into hospital workflows to classify skin lesions (2026, October 5) retrieved 5 October 2026 from https://medicalxpress.com/news/2026-10-ai-tool-hospital-workflows-skin.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.