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In the realm of dermatological diagnostics, the advent of artificial intelligence (AI) has heralded a transformative era, particularly with the application of advanced object detection models like YOLO V8. This study delves into the nuanced capabilities of YOLO V8 in discerning and classifying nine distinct dermatological conditions, a task previously fraught with challenges due to the varied and complex nature of skin diseases. Our research harnesses the robust architecture of YOLO V8, meticulously trained on a diverse dataset comprising both public and private sources, augmented through sophisticated image processing techniques. The model's performance was rigorously evaluated, demonstrating remarkable accuracy and efficiency in real-time detection scenarios. This not only underscores the potential of AI in enhancing diagnostic precision but also paves the way for more accessible and timely dermatological care. As we stand on the precipice of a new horizon in medical technology, the implications of such advancements are both profound and promising, potentially revolutionizing the landscape of dermatological treatment and patient care.
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Huang et al. (2024) studied this question.
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