Artificial Intelligence in Wilderness Search and Rescue: A Narrative Review

Artificial intelligence (AI) is transforming wilderness Search and Rescue (SAR), where time constraints, austere conditions, and limited personnel have historically defined outcomes. This narrative review synthesizes the current evidence for AI applications in SAR, drawing on parallel developments in prehospital Emergency Medical Services (EMS) to illuminate the field's trajectory. This article is the result of searching PubMed, IEEE Xplore, Scopus, and Google Scholar from 2018 through March 2026 using combinations of "artificial intelligence," "machine learning," "search and rescue," "unmanned aerial vehicle," "wilderness medicine," and "prehospital care." Operational reports and trade publications were included when peer-reviewed sources were unavailable for deployed SAR technologies. Findings were organized under 3 temporal frameworks: operationally deployed, demonstrated capability approaching scale, and credible near-to-medium-term projection. AI-enabled unmanned aerial vehicles with thermal imaging and computer vision are operationally deployed in wilderness SAR, with field-validated rescues demonstrating detection through canopy, darkness, and adverse weather. Deep reinforcement learning algorithms for autonomous search-path optimization achieve more than 160% improvement over conventional coverage methods. However, the prehospital EMS literature provides an essential cautionary lesson: The Blomberg randomized, controlled trial showed that even technically superior AI may fail to improve outcomes without careful attention to human-AI interaction design and workflow integration. AI appears to be contributing to successful SAR outcomes and has been associated with several documented live rescues. Near-term priorities include prospective outcome validation, swarm-drone coordination, large language model-assisted wilderness medical protocols, and sustainable funding models for volunteer SAR organizations.