Abstrak:
Wound care in disaster settings presents significant challenges for healthcare providers, often constrained by limited resources and medical personnel. The emergence of Artificial Intelligence (AI) offers promising solutions to enhance wound management in disaster scenarios. Objective this scoping review aims to examine the potential applications and effectiveness of AI technologies in revolutionizing disaster wound care, while identifying implementation challenges and future research directions. Methods asystematic literature search was conducted across major databases including PubMed, Scopus, and IEEE . We reviewed articles published between 2015-2024 focusing on AI applications in wound care and disaster management. The analysis followed PRISMA-ScR guidelines, with data extraction focused on AI technologies, clinical outcomes, and implementation challenges. Results the review identified four primary AI applications in disaster wound care: (1) image-based diagnostic systems achieving 85-95% accuracy in wound classification, (2) predictive algorithms for wound healing trajectories, (3) clinical decision support systems optimizing treatment protocols, and (4) AI-powered telemedicine platforms enabling remote consultation. Implementation demonstrated a 60% reduction in assessment time and improved resource allocation efficiency. However, challenges persist regarding infrastructure requirements, data security, and clinical validation. Conclusion AI technologies show substantial promise in enhancing disaster wound care through improved diagnosis accuracy, treatment optimization, and remote support capabilities. Future research should address validation protocols, ethical considerations, and integration with existing disaster response frameworks. These findings suggest that AI could significantly transform disaster wound care, though successful implementation requires careful consideration of practical and ethical challenges.
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