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OpenTrials
Completed

NCT Number: NCT07344792

Physician Diagnosis of Neurologic Cases With Large Language Models

This study is a prospective randomized controlled trial that aims to evaluate whether large language model (LLM) assistance improves physicians' diagnostic performance compared with conventional assistance.

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Key information

Conditions

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Beijing Luhe Hospital affiliated to Capital Medical University, Beijing, Beijing Municipality, China

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About this study

The study population consists of licensed physicians who have completed at least two years of neurology training. Participants are randomly assigned in a 1:1 ratio to the LLM assistance group or the conventional assistance group. In the LLM assistance group, physicians are assisted by an in-house LLM and are allowed to use conventional resources (e.g., search engines, UpToDate, or clinical guidelines) before submitting a final diagnosis. Access to any other LLMs is not permitted. In the conventional assistance group, physicians use only conventional resources and do not have access to the in-house LLM or any other LLMs at any point during the study. The primary outcome is top-1 diagnostic accuracy. Secondary outcomes include top-3 diagnostic accuracy, the time required to complete each case, and physicians' diagnostic confidence.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Licensed physicians who have completed at least two years of neurology training.
  • Willing to participate in the study and able to provide written informed consent.

Exclusion criteria

  • Physicians who have previously participated in the development of the in-house LLM or the construction of datasets used in this study.
  • Physicians who were involved in the clinical care of patients whose cases were used to construct the case dataset for this study.
  • Physicians who are known to have previously reviewed or been exposed to the specific cases used in this study.

Treatment and study plan

LLM assistance

Other

Physicians are assisted by an in-house large language model and are allowed to use conventional resources. Access to any other LLMs is not permitted.

Primary outcomes

  1. Top-1 diagnostic accuracy

    Time frame: Baseline

    Top-1 diagnosis is considered correct if the physician's final top-ranked diagnosis matches the reference diagnosis or a clinically similar diagnosis judged as equivalent by neurologists.

Secondary outcomes

  1. Top-3 diagnostic accuracy

    Time frame: Baseline

    Top-3 diagnosis is considered correct if the reference diagnosis or a clinically similar diagnosis judged as equivalent by neurologists appears among the physician's top three final diagnoses.

  2. Time spent on diagnosis

    Time frame: Baseline

    Time participants spend per case.

  3. Diagnostic confidence

    Time frame: Baseline

    Physicians' diagnostic confidence will be measured using a five-point Likert scale (1=very unsure, 5=very sure).

Sponsors and collaborators

Lead sponsor

Capital Medical University

Other

Registry information

Official study title

Physician Diagnosis of Neurologic Cases Assisted by Large Language Models: A Randomized Controlled Trial

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jan 15, 2026
Registry last updated
Jan 29, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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