TITLE: Development of Artificial Intelligence Applications for Identifying Physiological Changes in the Skin: A Multidisciplinary Project in the Health and Information Technology Fields
author(s): Rezende, R.A.E., Schulman, M., Santos, B.J. et al.
ABSTRACT: This article presents an innovative project conducted by a multidisciplinary team composed of students and professors from the fields of health and information technology. The project's objective was to develop artificial intelligence applications focused on the identification of physiological changes in the skin, aiming to improve the early detection of dermatological conditions and provide a more efficient and accurate approach to skincare. The team combined specialized knowledge in dermatology, medicine, computer science, and engineering to create a technological solution capable of analyzing skin images and physiological data. Using advanced artificial intelligence techniques such as machine learning and image processing, algorithms were developed to identify patterns and specific characteristics associated with common dermatological conditions, such as skin aging, melanoma, acne, and psoriasis. The application development process involved fundamental steps, including the collection and preparation of clinical and image data, the definition of analysis parameters, algorithm training using reference datasets, and validation of the results obtained. Collaboration among the multidisciplinary team was essential to ensure the proper integration of medical and technological knowledge, as well as to fine-tune and optimize the algorithms throughout the project. The results achieved so far are promising. The developed applications demonstrated a high accuracy rate in detecting physiological skin changes, surpassing human identification capabilities in some cases. Furthermore, they proved to be efficient in analyzing large datasets, enabling rapid and accurate screening. The artificial intelligence applications developed in this project have the potential to contribute to dermatological clinical practice, offering an additional tool for users, physicians, and other healthcare professionals in the early detection and monitoring of skin conditions. They also assist in reducing diagnostic errors, improving healthcare efficiency, and guiding more appropriate and personalized treatments.
KEYWORDS: Artificial intelligence, Machine learning, Skin, Early detection, Dermatological conditions.