Resumen

Objectives: ARTIFICIAL INTELLIGENCE APPLIED TO DERMATOLOGICAL DIAGNOSIS: WHERE ARE WE? Introduction: Due to its visual nature, dermatology has proven to be a particularly favorable field for the application of artificial intelligence (AI) in the diagnosis of skin lesions. Since 2017, advances in deep learning and neural networks have enabled algorithms to achieve performance comparable to that of specialists in recognizing neoplasms such as melanoma and basal cell carcinoma. Currently, AI is applied across multiple imaging modalities. Results: Artificial intelligence is widely applied in dermatology, particularly for melanoma and other pigmented lesions. Convolutional Neural Networks (CNNs), such as ResNet and Inception, are the most commonly used and demonstrate high performance, ranging from 74% to 98%. Practical AI applications include software integrated with digital dermoscopy, with good sensitivity (80%) and specificity (78%). Conclusion: Artificial intelligence has emerged as a valuable tool in dermatology, with advances in deep learning and neural networks enabling performance comparable to that of physicians. Its applications range from digital dermoscopy to mobile applications, expanding diagnostic support. Nevertheless, challenges such as limited representativeness in databases, lack of standardized guidelines, and the need for multicenter validation still limit its clinical implementation. Therefore, AI should be viewed as a complement to medical practice, capable of optimizing screening and accelerating clinical.