{"resourceId":"mdpi-ai-medicine-critical-review-ai-decision-support-emergency-prehospital-20261029","versions":[{"version":"external-f0dbbab6381472ee0ca7a79760ef1df3f0f0365ac05548e1c8fe894742dfdd41","resource":{"id":"mdpi-ai-medicine-critical-review-ai-decision-support-emergency-prehospital-20261029","title":"Critical review: AI in emergency and prehospital care earns 'cautious optimism', with dispatch AI that beat dispatchers alone failing to improve live recognition in a trial","organization":"Ramalhete, Oliveira and Quintas; affiliations include NOVA Medical School and the Blood and Transplantation Center of Lisbon (Portugal)","sector":"EMS, dispatch and emergency medicine research","geography":"International literature (authors in Portugal); dispatch trial cited from Sweden","publishedAt":"2026-10-29","publicationDate":"2026-10-29","eventDate":null,"sourceName":"AI in Medicine (MDPI)","sourceLabel":"AI in Medicine: Beyond the Hype: Artificial Intelligence as Decision Support in Emergency and Prehospital Medicine—A Critical Narrative Review","sourceUrl":"https://www.mdpi.com/3042-6707/1/4/28","evidenceClass":"academic-research","outcomeClass":"mixed","topics":["governance-procurement","operating-model","data-security"],"finding":"A critical narrative review (searches of PubMed, Scopus and Web of Science, Jan. 2018 to Sept. 15, 2026) concludes the most credible near-term AI uses in emergency and prehospital care are bounded decision support: triage, stroke imaging, operational forecasting, dispatch support, destination decisions and supervised documentation. It finds the field constrained by retrospective designs, heterogeneous endpoints, limited external validation and a gap between model performance and usefulness. On dispatch, it notes a randomized trial where a machine-learning cardiac arrest alert recognized more arrests than dispatchers alone but did not significantly improve dispatcher recognition in live calls. It recommends local validation, real override, named owners, logging and post-deployment monitoring, and says current evidence supports cautious optimism.","sledRelevance":"Gives EMS and 911 buyers an up-to-date, sceptical synthesis to set evidence expectations for dispatch, triage and destination AI, including the lesson that algorithm accuracy does not equal better human performance.","evidence":"Narrative (not systematic) review with a structured search; no pooled estimates; maturity ratings are the authors' qualitative synthesis. Inspected via WebFetch of the full article page because curl returned 403; tables 1-2 were not rendered in the fetched text.","architectureImplications":"Favours narrow tools embedded at defined decision points, with local validation and drift monitoring, over broad platforms.","governanceImplications":"Calls for named owners, update governance, override and adverse-event logs, subgroup evaluation and frameworks such as DECIDE-AI, TRIPOD+AI and PROBAST+AI.","securityPrivacyImplications":"Flags call audio, geolocation and linked outcomes as highly sensitive; local processing is easier to defend than external systems with unclear data handling.","caveats":"Narrative review in a new journal; selection of evidence is interpretive; most cited evidence predates this edition; review is about clinical and dispatch AI broadly, with limited fire or disaster coverage.","streamIds":["emergency-services"],"roles":{"sales":"For sales, the framing point is that a new critical review tells EMS and dispatch leaders to expect cautious optimism, not transformation, and cites a trial where a cardiac arrest alert beat dispatchers alone yet did not improve live recognition. That helps position evaluation-first work. A bounded engagement is an evidence and readiness review of a proposed dispatch or triage AI tool. The review cannot support claims of improved patient outcomes.","engineering":"For pre-sales engineering, the review's checklist is practical: local validation, calibration, subgroup checks, override design, logging and drift monitoring. Prerequisites are local historical data and outcome linkage. Constraints are that most evidence is retrospective and site-specific, so vendor results may not transfer. Proof of value should test the tool on local calls or patients and measure the human-plus-AI decision, not just model accuracy.","delivery":"For delivery, the work is governance before go-live: an intended-use statement, a named owner, override and adverse-event logging and a monitoring plan. Ownership sits with the EMS medical director or dispatch quality lead. Acceptance should include local validation and a prospective check of dispatcher or crew decisions. Risks include automation bias, alert fatigue, subgroup underperformance and silent drift after workflow changes."},"roleDetails":{"sales":{"customerProblem":"EMS and dispatch leaders face vendor AI claims without a clear way to judge evidence maturity.","stakeholders":["EMS medical director","Dispatch center director","Quality and safety lead","Procurement","Hospital partners"],"discoveryQuestions":["Is the vendor's evidence prospective and externally validated?","Has the tool been tested on your own calls or patients?","How will dispatchers or crews override it, and is that logged?"],"valueHypothesis":"An evaluation-first approach can prevent buying tools that look accurate but do not improve live decisions (interpretation).","potentialEngagement":"An evidence and readiness review of one proposed dispatch, triage or destination AI tool against the review's criteria.","unsupportedClaims":["Improved patient outcomes from dispatch AI","Transferability of any cited result","Pooled effect sizes"]},"engineering":{"fit":"Fits any EMS or 911 agency evaluating clinical or dispatch decision support.","architectureIntegration":"Narrow decision-support tools at defined decision points with override, logging and monitoring.","prerequisites":["Local historical call or patient data","Outcome linkage to hospitals","Logging of AI outputs and user actions"],"constraints":["Mostly retrospective evidence","Site-specific performance","Narrative, not systematic, synthesis"],"security":"Prefer local or tightly governed processing of call audio, geolocation and clinical data.","proofOfValue":["Local validation on agency data","Subgroup performance check","Prospective measure of human-plus-AI decisions"]},"delivery":{"work":"Set intended use, ownership, override logging and monitoring before go-live.","dependencies":["Vendor access to model documentation","Data-sharing with hospitals","Quality team capacity"],"ownership":"EMS medical director or dispatch quality lead.","skillsAdoption":"Dispatchers and crews learn when to challenge AI output; quality staff learn to monitor drift.","governanceCheckpoints":["Intended-use statement approved","Local validation report","Post-deployment monitoring review"],"acceptanceCriteria":["Proposed: local validation meets agreed thresholds across subgroups","Proposed: prospective check shows no decline in dispatcher recognition"],"risks":["Automation bias","Alert fatigue","Subgroup underperformance","Drift after workflow change"]}},"retrievedAt":"2026-10-30T21:59:24-05:00","enrichedAt":"2026-10-30T22:02:41-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Highlights automation bias, alert fatigue and trust as human-factors issues for dispatchers and EMS crews.","procurementImplications":"Buyers should ask vendors for prospective, externally validated and subgroup-level evidence, not retrospective accuracy.","operatingModelImplications":"AI augments dispatchers and crews; organisations must have capacity to act on forecasts and alerts.","peerReview":"peer-reviewed","sourceVerification":{"responseId":"webfetch-mdpi-2026-10-30","openedUrl":"https://www.mdpi.com/3042-6707/1/4/28","referenceExcerpt":"Current evidence supports cautious optimism rather than technological inevitability.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}