Artificial intelligence (AI) is transforming the diagnosis and management of cutaneous infections by improving accuracy, efficiency, and accessibility of care. Leveraging advanced machine learning (ML) algorithms, particularly convolutional neural networks (CNNs), AI can analyse clinical images to identify and classify skin infections caused by bacterial, viral, fungal, or parasitic pathogens with high sensitivity and specificity. For instance, AI models like MPXV-CNN have demonstrated remarkable accuracy in detecting viral infections such as Mpox. Similarly, DL architectures such as Inception-v3 and ResNet-50 are widely employed to diagnose leprosy, and fungal infections while assessing disease severity. AI also plays a critical role in epidemiological surveillance, utilizing platforms like EPIWATCH to monitor outbreaks of cutaneous infections in real time. These tools predict infection trends and identify high-risk populations, enabling timely public health interventions. AI aids in personalized treatment planning by predicting therapeutic responses and minimizing adverse drug reactions. This ensures optimized patient care while improving adherence to treatment regimens. In addition to diagnostics and management, AI accelerates drug discovery for cutaneous infections by streamlining compound screening and vaccine development. Collaborative approaches integrating AI with clinician expertise significantly enhance diagnostic accuracy, even among non-specialists. By addressing challenges such as overlapping clinical presentations and limited access to dermatological expertise, AI offers scalable solutions for global healthcare systems. While promising, the integration of AI into dermatology requires addressing ethical concerns, data privacy issues, and the need for robust validation studies to ensure equitable and reliable outcomes.
BT - Current Dermatology Reports DO - 10.1007/s13671-025-00468-w IS - 1 LA - eng M3 - Research Article N2 -Artificial intelligence (AI) is transforming the diagnosis and management of cutaneous infections by improving accuracy, efficiency, and accessibility of care. Leveraging advanced machine learning (ML) algorithms, particularly convolutional neural networks (CNNs), AI can analyse clinical images to identify and classify skin infections caused by bacterial, viral, fungal, or parasitic pathogens with high sensitivity and specificity. For instance, AI models like MPXV-CNN have demonstrated remarkable accuracy in detecting viral infections such as Mpox. Similarly, DL architectures such as Inception-v3 and ResNet-50 are widely employed to diagnose leprosy, and fungal infections while assessing disease severity. AI also plays a critical role in epidemiological surveillance, utilizing platforms like EPIWATCH to monitor outbreaks of cutaneous infections in real time. These tools predict infection trends and identify high-risk populations, enabling timely public health interventions. AI aids in personalized treatment planning by predicting therapeutic responses and minimizing adverse drug reactions. This ensures optimized patient care while improving adherence to treatment regimens. In addition to diagnostics and management, AI accelerates drug discovery for cutaneous infections by streamlining compound screening and vaccine development. Collaborative approaches integrating AI with clinician expertise significantly enhance diagnostic accuracy, even among non-specialists. By addressing challenges such as overlapping clinical presentations and limited access to dermatological expertise, AI offers scalable solutions for global healthcare systems. While promising, the integration of AI into dermatology requires addressing ethical concerns, data privacy issues, and the need for robust validation studies to ensure equitable and reliable outcomes.
PB - Springer Science and Business Media LLC PY - 2025 T2 - Current Dermatology Reports TI - The Role of Artificial Intelligence in Diagnosis and Management of Cutaneous Infections UR - https://link.springer.com/article/10.1007/s13671-025-00468-w#citeas VL - 14 SN - 2162-4933 ER -