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AI vs. ER docs: who makes the right call?

NCT ID NCT07632859

Knowledge-focused Sponsor: Marmara University Pendik Training and Research Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Aug 14, 2026 · Updated 1 time

Summary

This study tests whether two AI models, GPT-4o and Claude 4.6 Sonnet, can correctly diagnose patients based on emergency department notes written in Turkish. Researchers will compare the AI's diagnoses to those made by the treating doctor and a panel of three specialists. The goal is to see if AI can help reduce diagnostic errors in emergency medicine.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
Large language models (GPT-4o and Claude 4.6 Sonnet) used to analyze patient notes
What this could lead to
If successful, this could show that AI can help emergency doctors make faster or more accurate diagnoses, potentially reducing errors.
What could go wrong
This is a retrospective study using written notes, not real-time patient care. The AI may not perform as well in a busy emergency room, and results may not apply to other languages or settings.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

600 people

The number who actually took part.

Started

May 2026

Finished

Aug 2026

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

The study population comprises consecutive adult patients (aged 18 years and older) evaluated in the ambulatory (green/yellow triage) area of the emergency department of a tertiary care training and research hospital, and whose encounters were documented in the hospital information system (HBYS). Patients triaged to the high-acuity resuscitation area (Emergency Severity Index level 1) were excluded a priori; no Emergency Severity Index level 1 or level 2 presentation occurred within the sampling window, so resuscitation-area presentations are absent from the study population altogether. The findings do not extend to high-acuity emergency care.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

INCLUSION CRITERIA: Adult patients (aged 18 years and older) presenting to the emergency department, evaluated in the ambulatory (green/yellow triage) area. A free-text electronic anamnesis note entered at presentation in the hospital information system (HBYS). No minimum note length and no "sufficient information for diagnosis" requirement was applied, because such a criterion preferentially retains more readily classifiable cases; note length was treated as a covariate rather than as an eligibility threshold. A note was excluded only if all three of the following were absent: any symptom statement, any duration or onset information, and a non-empty anamnesis field. An ICD-10 code entered by the treating emergency physician at case closure. Cases in which this entry was absent or did not form a valid ICD-10 code were retained in the analysis set and counted in the denominator of the closure-code analyses. EXCLUSION CRITERIA: Notes lacking all three of the following: any symptom statement, any duration or onset information, and a non-empty anamnesis field. Pediatric cases (age under 18 years). Patients critically ill and triaged to high-acuity resuscitation areas (Emergency Severity Index \[ESI\] level 1). Clinical notes containing residual identifying information that cannot be fully de-identified, preventing compliance with data privacy regulations. Non-independent clinical notes consisting solely of a brief cross-reference to a prior hospital visit without a new history entry.

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Conditions

The condition(s) this trial relates to.

Emergencies

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Artificial intelligence (AI) in diagnosis Diagnostic errors Emergency medicine

Contacts and locations

Locations

  • Marmara University Pendik Training and Research Hospital

    Istanbul, Istanbul, 34899, Turkey (Türkiye)

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