Prehospital AI review finds uneven evidence and no low-income-country studies

EMS leaders and dispatch planners need evidence that a proposed tool fits their population and service constraints. Ask about the existing baseline, missing records, rural coverage, language mix and who can independently assess a vendor's study. A bounded engagement could build an evidence inventory and identify the smallest locally testable use case. The value hypothesis is avoiding unsupported scope while finding a measurable operational problem. Include clinical leadership, analysts, frontline staff and procurement. This review does not support a uniform savings estimate or a claim that AI always beats simpler methods. International findings should inform questions, with explicit limits on transfer to U.S. agencies.

Select one task, such as demand forecasting or record classification, and create a reproducible baseline before integrating a new model. Prerequisites include trustworthy timestamps, defined missing-data handling, lawful access and a representative temporal holdout. Compare model performance with the existing process and an appropriate simpler method using identical inputs. Audit data leakage, calibration and subgroup errors; report excluded cases and system outages. Avoid pooling incompatible accuracy measures into a product score. A proof of value should establish whether the complete local workflow improves its prespecified endpoint under realistic staffing and connectivity constraints. Secure the evaluation dataset and document what remains untested before any operational integration.

Appoint an evaluation lead alongside the operational EMS owner and a data steward. Inventory data sources, resolve definitions, train staff in consistent recording and agree on baseline and stopping rules. Dependencies include outcome linkage, access approvals and sufficient analyst time. Review representativeness and methodological quality before a pilot, then review adoption and unintended workload during it. Proposed acceptance requires a reproducible comparison, explicit missingness counts, results for agreed population groups and a documented disposition for every material limitation. Thresholds should be agreed locally rather than borrowed from the review. Maintain the existing service when evidence is inconclusive; improvement work may appropriately end with better data rather than a deployed model.