Artificial Intelligence in Wilderness Search and Rescue: A Narrative Review

• Author: Kenneth V. Iserson

• Publication date: 9 July 2026

• Original source: Wilderness & Environmental Medicine

Why it matters:
It provides a useful overview of which AI applications have reached operational use and which remain experimental. Particularly relevant are UAV image analysis, thermal detection, autonomous search routing and the effect of integrating AI recommendations into existing command processes.

Operational implications:

• AI output should be treated as another information source, not proof that an area is clear.

• Search managers must plan how alerts are reviewed, confirmed, recorded and converted into tasks.

• Technical improvements do not necessarily improve outcomes when interfaces, workload and operating procedures are poorly designed.

• Procurement should require operational validation, false-negative information and evidence from representative terrain.

Transferability:
Broadly applicable to wilderness and ground SAR, although aviation regulation, terrain, communications and organisational capability will affect implementation.

Limitations:
This is a narrative rather than systematic review. It incorporates trade publications and operational reports where peer-reviewed research is unavailable. Some claims concern simulation or individual rescue accounts rather than comparative field trials, so it does not establish general improvements in detection or survival.

Training sections informed:

• Technology selection and assurance

• UAV tasking

• POD and false-negative awareness

• Search Operations and progress monitoring

• Reassessment and replanning

• Human factors and automation bias

• Decision logging

Training decision: Add — introduce a short framework for evaluating and operationally integrating AI-assisted search technology.