At Clinicgram, we believe in innovation supported by evidence. Below you can find a selection of peer-reviewed scientific publications related to digital health, computer vision, artificial intelligence and clinical follow-up tools applied to wound assessment and patient monitoring.
Clinical validation of computer vision and AI algorithms for wound measurement
Evaluation of Two Digital Wound Area Measurement Methods Using a Non-Randomized, Single-Center, Controlled Clinical Trial
Decision support system for the diagnosis of chronic wounds using Machine Learning algorithms with images
Tissue segmentation for automatic chronic wound assessment
Mobile Application and Advanced Analytics in Wound Care: Variable Predictors of Wound Evolution in a Public Intermediate Hospital
Interactive Medical Image Labeling Tool to Construct a Robust Convolutional Neural Network Training Data Set – Development and Validation Study
New Superpixels for Chronic Ulcers Segmentation
Color Normalization Through a Simulated Color Checker Using
Assessment of frailty in elderly patients attending a multidisciplinary wound care centre – a cohort study
Asynchronous teleconsultation by WhatsApp chatbot in controlled axial spondyloarthritis patients under biological therapy – patients’ perspective
These publications provide scientific context and clinical evidence related to the use of digital tools, AI and computer vision in healthcare settings. Clinicgram does not replace the clinical judgment of healthcare professionals, who remain ultimately responsible for diagnostic and therapeutic decision-making.