Rapid development of disease detection models using computer vision is crucial in responding to medical emergencies. Traditional data collection methods are often too slow, requiring innovative approaches for quick, reliable model generation from minimal data. This study introduces a novel approach by constructing a computer vision model to detect Mpox lesions using only synthetic data generated by diffusion models. The model achieved a 97% accuracy rate, with 96% precision and recall for Mpox cases, and similarly high metrics for normal and other skin disorder cases. The SynthVision methodology indicates the potential to develop accurate computer vision models with minimal data input for future medical emergencies.
