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From innovation to implementation: a global scoping review of artificial intelligence applications, governance, and challenges in humanitarian contexts

Background: Artificial intelligence (AI) is increasingly being explored as a tool for supporting humanitarian action through hazards and crisis prediction, real time monitoring of evolving crisis conditions and humanitarian needs, resource allocation, and decision-making. However, evidence on how AI is implemented, evaluated, and governed across humanitarian contexts remains fragmented. In this scoping review, we mapped the global evidence on the application of AI in humanitarian settings, including its distribu…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1917· cite & verify
From innovation to implementation: a global scoping review of artificial intelligence applications, governance, and challenges in humanitarian contexts
American Humanitarian Aid in Kenya by Monica's Dad. CC BY 2.0 · https://creativecommons.org/licenses/by/2.0

In brief

A scoping review followed the Arksey and O'Malley framework. It searched six databases, Google Scholar, and humanitarian organisation webpages, and included 57 scholarly publications and 17 grey literature reports on AI in humanitarian contexts. Applications clustered around sudden-onset disasters such as floods and earthquakes, supporting monitoring, search-and-rescue, logistics, hazard prediction, and geospatial analysis.

The findings matter because humanitarian agencies are exploring AI for prediction and resource allocation, yet the evidence base is uneven. Few systems have moved beyond prototypes, recovery work is rarely covered, and governance is discussed more in grey literature than in scholarly papers. The review points to a need for implementation-focused evaluation, but it does not measure how well deployed tools perform.

Main points

  1. The review included 57 scholarly publications and 17 grey literature reports.
  2. Most applications addressed preparedness and response; recovery was rarely addressed.
  3. Deep learning and machine learning were the most common model families.
  4. Most evidence came from Asia, while research from the MENA and GCC regions remained limited.
  5. Governance was discussed more extensively in grey literature than in scholarly publications.

The problem

Most reported humanitarian AI systems are still prototypes or retrospectively validated models, few are deployed in the field, and community participation, equity, and governance frameworks are inconsistently reported.

The rundown

The review mapped AI use across the emergency management cycle and found it concentrated in sudden-onset natural disasters, particularly floods and earthquakes. Common uses included disaster monitoring, crisis informatics, search-and-rescue, logistics optimisation, hazard prediction, and geospatial analysis.

Geographic coverage was uneven. Most evidence originated from Asia, and the MENA region and GCC countries, which the authors examined with particular attention including Qatar, had limited research.

On governance, the authors conclude that greater emphasis is needed on real-world deployment, implementation-focused evaluation, participatory approaches, and governance frameworks supporting responsible, equitable, and context-sensitive adoption.

Sources

  1. Peer-reviewedJournal of Global Health2026-10-19

The debate