Comprehensive Summary
This study examines how artificial intelligence (AI) is being used to detect, classify, and manage cutaneous infections. The authors conducted a comprehensive literature review summarizing machine learning tools, teledermatology, and AI-driven treatment systems. They highlight that AI tools can reliably analyze clinical images and predict treatment response. Systems like YOLO, which is a hyphae detection system, and Mpox trained CNNs demonstrate high diagnostic performance. AI-driven microbiomes were able to support early infection risk classification. Additional AI capabilities include surveilling outbreak, diagnosing in rural areas, and identifying new possible antibiotics. The authors emphasize that while AI has significant benefits, it must overcome challenges like dataset bias and limited representation of darker skin tones.
Outcomes and Implications
There are many skin infections that have similar clinical features. Using AI can reduce errors in diagnoses and delays in regions with less access to dermatology specialists. The findings suggest that AI tools can soon be used to support care providers by allowing them to rapidly assess patients. However, the authors address that AI will need to be monitored and needs larger datasets before becoming fully implemented into medical practice.