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AI vs. Doctor's intuition: which leads to better diagnoses?

NCT ID NCT07760051

Knowledge-focused Sponsor: Second Affiliated Hospital, Zhejiang University, School of Medicine 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 This study
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
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 Results may not be published yet.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This status has not been confirmed recently, so it may be out of date.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 Aug 12, 2026 · Last updated Aug 19, 2026 · Updated 3 times

Summary

This trial investigates whether AI-assisted workflows can improve how doctors make admission diagnoses. It compares three approaches: a traditional method using standard resources, a chatbot-style AI that doctors can ask questions, and a more advanced 'agent' that automatically reads patient records and provides structured summaries. Doctors from 15 hospitals in China will be randomly assigned to one of these methods and will work through six simulated patient cases, with their diagnostic accuracy and speed measured. The goal is to see if AI tools, especially the agent, can help doctors make more accurate and efficient diagnoses.

What this could mean

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

Active substance
AI-assisted diagnostic workflows (agent-based and LLM-based tools) compared to traditional methods
What this could lead to
If effective, AI-assisted workflows could help doctors make more accurate diagnoses, potentially reducing errors and improving patient care.
What could go wrong
The trial uses simulated cases, not real patients, and results may not translate to real-world settings. AI tools may also introduce new errors or over-reliance.

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.

Phase

Not a phased trial

Phase numbers describe drug development. The registry uses this when they do not apply, as it does for trials of devices, procedures or behaviour changes, and for observational studies.

Participants

About 180 people

The number the study aims to enrol. It can still change while the study runs.

Expected to start

Aug 2026

An estimate. Start dates often move.

Expected to finish

Dec 2026

An estimate. End dates often move.

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.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Accepted

You do not need to have the condition being studied to take part.

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 * Hold a Medical Practitioner Qualification Certificate and/or Medical License, or be a recognized standardized resident physician; able to independently read electronic medical records, laboratory and imaging reports on an HIS. * Currently engaged in clinical work in internal medicine or surgery at one of the 15 participating hospitals. * Able to complete the case assessment in one continuous hour without breaks. * Able to participate remotely under video proctoring, with a stable internet connection and a working camera. * Voluntarily agree to participate and sign the informed consent form, including the declaration not to use unauthorized AI tools during the assessment. * Have not participated in case drafting, review, rubric development, or any activity that may leak the reference standard. Exclusion Criteria * Have previously accessed the official test cases or reference standard of this study. * Unable to complete the training module, qualification test, or all experimental tasks. * Have conflicts of interest, e.g. participation in developing core algorithms of the tested system. * Unwilling to comply with remote proctoring, including keeping the camera on throughout. * Judged unsuitable by the investigators.

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As listed by the trial registrant

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

Artificial intelligence (AI) Clinical decision support systems Diagnostic reasoning

How to take part

Only the study team decides who joins. These are the ways to reach them.

  1. The places running it

    1 site. The list below names each one and where it is.

  2. The official record

    ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.

    Open the record ↗

  3. A doctor treating you

    A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.

Contacts and locations

Locations

  • 2nd Affiliated Hospital, School of Medicine, Zhejiang University

    Hangzhou, Zhejiang, 310009, China

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