THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE EARLY DIAGNOSIS OF GYNECOLOGICAL DISEASES

Abstract

Introduction: Artificial intelligence (AI) has stood out as a revolutionary tool in the field of health, particularly in the early diagnosis of gynecological diseases, such as cervical cancer, endometriosis, and polycystic ovary syndrome. AI's ability to identify patterns in large volumes of clinical data and medical images offers new perspectives for overcoming challenges related to late diagnosis. However, its implementation faces technical, ethical, and social barriers, such as external validation, the generalization of models, and inequalities in access to these technologies. Objective: To explore the role of artificial intelligence in the early diagnosis of gynecological diseases, highlighting its practical applications, benefits, challenges, and future implications. Methodology: This is a narrative review of the literature conducted in databases such as PubMed, Scopus, Web of Science and SciELO, using keywords such as "artificial intelligence", "early diagnosis" and "gynecological diseases". Studies published between 2015 and 2025 in English, Portuguese, and Spanish were included. The selection considered articles that addressed the use of AI in gynecology, focusing on its benefits and limitations. The extracted data were analyzed qualitatively and organized into thematic categories. Results and Discussion: The most notable advances include the use of AI in the interpretation of imaging tests and in the screening of gynecological diseases. Machine learning-based models achieve high accuracy, outperforming traditional methods in a variety of contexts. Despite this, challenges such as the lack of external validation and the need for generalization of the models limit the global implementation. In addition, ethical issues, such as data privacy and algorithm transparency, need to be addressed. AI complements, but does not replace, human expertise, and interdisciplinary collaboration is essential to maximize its positive impact. Conclusion: AI represents a promising tool in the early diagnosis of gynecological diseases, with the potential to transform clinical practice. However, their integration requires efforts to overcome technical, ethical, and social barriers, ensuring that their benefits are accessible to all patients in an equitable and sustainable way. Future research should focus on model validation and increasing technological accessibility.