{
  "id": "AIBD-0030",
  "title": "Machine-learning cardiac-arrest recognition on Copenhagen 112 calls: capability study and null randomized trial",
  "summary": "Copenhagen EMS call model beat dispatchers on sensitivity (84.1% vs 72.5%); RCT found no significant gain in dispatcher recognition.",
  "date_observed": "2018-09-01",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "Copenhagen Emergency Medical Services used a machine-learning framework to listen to 112 calls and flag suspected out-of-hospital cardiac arrest for dispatchers. The European Emergency Number Association names it as Corti's. A retrospective study of 108,607 calls from 2014 found the model recognized cardiac arrest with higher sensitivity than dispatchers (84.1% versus 72.5%). It was also faster (median 44 versus 54 seconds). A double-masked randomized trial of 169,049 calls ran from 1 September 2018 to 31 December 2019. It found dispatcher recognition of confirmed arrests of 93.1% with alerts versus 90.5% without, a non-significant difference. Alerts alone were more sensitive than dispatchers (85.0% versus 77.5%). No individual patient outcome attributable to an alert is documented.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "The model was built to recognize out-of-hospital cardiac arrest during emergency calls so dispatchers can start telephone CPR sooner. The measured capability is that intended function, but no discrete changed outcome is documented.",
  "system": "Corti machine-learning call-analysis framework for out-of-hospital cardiac arrest recognition (the peer-reviewed papers describe it only as 'a machine learning framework'; the EENA report names Corti)",
  "model_version": null,
  "developer": "Corti (Copenhagen, Denmark)",
  "deployer": "Copenhagen Emergency Medical Services, Emergency Medical Dispatch Center Copenhagen",
  "location": "Copenhagen, Denmark",
  "domain": "Emergency medical dispatch / public safety",
  "segment": "112 emergency call handling",
  "benefit_type": "Improved Task Accuracy",
  "taxonomy_note": "The accuracy gain is the model's alone; the randomized trial found no significant gain in dispatcher recognition.",
  "registry_ids": {},
  "ratings": [
    {
      "group": "personal",
      "depth": null,
      "reason": "The evidence is aggregate (sensitivity, time-to-recognition, trial-level recognition rates) with no individually attributable changed outcome. The randomized trial found no significant improvement in dispatcher recognition."
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    "status": "assessed",
    "statement": "The AI listened to live calls and alerted dispatchers. In the randomized trial its alerts were more sensitive than dispatchers alone (85.0% vs 77.5%). They did not significantly change dispatcher recognition (93.1% vs 90.5%, P = .15). The benefit is thus a demonstrated detection capability, not a verified change in any patient's course."
  },
  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "Blomberg SN, Christensen HC, Lippert F, et al. (2021). Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial. JAMA Network Open 4(1):e2032320.",
      "title": "Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial",
      "url": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774644",
      "date": "2021-01-05",
      "written_by": "deployer",
      "quote": "Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P < .001)"
    },
    {
      "kind": "peer-reviewed study",
      "citation": "Blomberg SN, Folke F, Ersboll AK, Christensen HC, Torp-Pedersen C, Sayre MR, Counts CR, Lippert FK (2019). Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation 138:322-329. doi:10.1016/j.resuscitation.2019.01.015.",
      "title": "Machine learning as a supportive tool to recognize cardiac arrest in emergency calls",
      "url": "https://doi.org/10.1016/j.resuscitation.2019.01.015",
      "date": null,
      "written_by": "deployer",
      "quote": "Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001)."
    },
    {
      "kind": "report",
      "citation": "European Emergency Number Association and Corti (2020). Detecting out-of-hospital cardiac arrest using artificial intelligence: project report. EENA, Brussels.",
      "title": "Detecting out-of-hospital cardiac arrest using artificial intelligence: project report",
      "url": "https://eena.org/wp-content/uploads/2020_01_13_Corti_Report.pdf",
      "date": "2020-01-13",
      "written_by": "makers",
      "quote": "This document is authored by EENA and Corti staff members with contributions from individual members of EENA and Corti."
    }
  ],
  "replication": {
    "produced_by": [
      "Access to recorded 112 call audio with linked outcome data (7,211 hours of Danish training data per the EENA report)",
      "Speech-recognition pipeline running in real time on live calls",
      "A dispatch center whose baseline recognition was already high (90.5% in the control arm)"
    ],
    "must_hold": [
      "Dispatchers act on alerts. The trial suggests alerts did not change their behavior enough to shift recognition",
      "Call language and acoustics match the training data (EENA reports weaker performance on smaller French and Italian datasets)"
    ],
    "echoes": [
      {
        "date": "2018-09-01",
        "setting": "Randomized trial at Copenhagen EMS, 1 September 2018 to 31 December 2019: the model's sensitivity advantage recurred (85.0% vs 77.5%) but improved dispatcher recognition did not materialize (93.1% vs 90.5%, P = .15)",
        "model_version": null,
        "outcome": "recurred",
        "source": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774644"
      }
    ]
  },
  "paired_incident": {
    "database": "AI Incident Database",
    "identifier": "446",
    "title": "ShotSpotter Failed to Alert Authorities of Mass Shooting in North Carolina",
    "url": "https://incidentdatabase.ai/cite/446",
    "severity": null,
    "group": "organizational",
    "basis": "Machine listening for emergencies to alert responders: Corti flags cardiac arrest on 112 calls, while ShotSpotter's acoustic classifier missed a Durham drive-by shooting, leaving police unalerted and five victims hospitalized."
  },
  "headline": "Corti model beats dispatchers on sensitivity; RCT shows no gain",
  "metric": "higher sensitivity than dispatchers (84.1% versus 72.5%)",
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    "status": "persisted",
    "date": "2021-01-04",
    "note": "The randomized trial of 169,049 calls confirmed the model's sensitivity advantage over dispatchers (85.0% vs 77.5%) but found no significant improvement in dispatcher recognition when alerts were shown (93.1% vs 90.5%).",
    "quote": "Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P < .001) but lower specificity (97.4% vs 99.6%; P < .001) and positive predictive value (17.8% vs 55.8%; P < .001). This randomized clinical trial did not find any significant improvement in dispatchers' ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition.",
    "url": "https://europepmc.org/article/PMC/PMC7788469"
  },
  "value": null,
  "developer_country": "Denmark",
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  "unrated_because": null,
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      "date": "2026-10-20",
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      "editor": "AIBD"
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      "date": "2026-10-20",
      "change": "Paired with AI Incident Database incident 446 (medium confidence); severity not given by the source.",
      "source": "https://incidentdatabase.ai/cite/446",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
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      "change": "Added what happened since from the cited sources.",
      "source": "https://europepmc.org/article/PMC/PMC7788469",
      "editor": "AIBD"
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      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://www.prnewswire.com/news-releases/dcai-and-corti-launch-sovereign-ai-control-layer-for-european-enterprises---trifork-first-to-adopt-as-implementation-partner-302856390.html; https://da.wikipedia.org/wiki/Region_Hovedstadens_Akutberedskab",
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      "date": "2026-10-21",
      "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
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  "confidence": "claimed",
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        },
        "review_state": "record_mapped",
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      "title": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "title": "Machine-learning cardiac-arrest recognition on Copenhagen 112 calls: capability study and null randomized trial",
            "headline": "Corti model beats dispatchers on sensitivity; RCT shows no gain"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
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        },
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          "role": "SHA-256 of normalized original case data"
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        "provenance": {
          "kind": "curatorial",
          "basis": "Deterministic content version assigned by the local migration.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "workflow": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "Copenhagen Emergency Medical Services used a machine-learning framework to listen to 112 calls and flag suspected out-of-hospital cardiac arrest for dispatchers. The European Emergency Number Association names it as Corti's. A retrospective study of 108,607 calls from 2014 found the model recognized cardiac arrest with higher sensitivity than dispatchers (84.1% versus 72.5%). It was also faster (median 44 versus 54 seconds). A double-masked randomized trial of 169,049 calls ran from 1 September 2018 to 31 December 2019. It found dispatcher recognition of confirmed arrests of 93.1% with alerts versus 90.5% without, a non-significant difference. Alerts alone were more sensitive than dispatchers (85.0% versus 77.5%). No individual patient outcome attributable to an alert is documented.",
            "replication": {
              "produced_by": [
                "Access to recorded 112 call audio with linked outcome data (7,211 hours of Danish training data per the EENA report)",
                "Speech-recognition pipeline running in real time on live calls",
                "A dispatch center whose baseline recognition was already high (90.5% in the control arm)"
              ],
              "must_hold": [
                "Dispatchers act on alerts. The trial suggests alerts did not change their behavior enough to shift recognition",
                "Call language and acoustics match the training data (EENA reports weaker performance on smaller French and Italian datasets)"
              ],
              "echoes": [
                {
                  "date": "2018-09-01",
                  "setting": "Randomized trial at Copenhagen EMS, 1 September 2018 to 31 December 2019: the model's sensitivity advantage recurred (85.0% vs 77.5%) but improved dispatcher recognition did not materialize (93.1% vs 90.5%, P = .15)",
                  "outcome": "recurred",
                  "source": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774644"
                }
              ]
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
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        },
        "review_state": "record_mapped",
        "coverage": "partial"
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    "summary": {
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      "addressed": 91,
      "known": 30,
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      "complete": false,
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  }
}