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Improving AI-Assisted Medical Diagnosis and Triage by the General Public

NCT07728513 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 220

Last updated 2026-07-27

No results posted yet for this study

Summary

This study is a randomized controlled trial (RCT) investigating whether access to a new LLM interface can improve medical triage and diagnostic accuracy for laypeople compared to access to a standard LLM interface. It addresses previous findings where laypeople using standard LLMs performed worse than those using conventional methods (e.g., web search) due to incomplete symptom sharing and poor interpretation of AI advice. To address this, the research tests a structured LLM system that proactively asks clinical history questions before providing a standardized, easy-to-read diagnostic output.

Conditions

  • AI-Assisted Diagnosis
  • Large Language Models

Interventions

DEVICE

new GPT-4o Interface

Participants can access any assistance methods they would typically employ (e.g., web search or health portals) in addition to a new LLM (GPT-4o) interface to complete medical scenarios. The new LLM interface uses a fixed system prompt that (a) instructs the model to ask targeted clarifying questions before providing any diagnostic or triage suggestions, and (b) requires all final responses to follow a structured template listing: possible conditions, approximate likelihood of each, and a recommended triage with brief reasoning.

OTHER

Control

Participants use any assistance methods they would typically employ at home (e.g., Google or health portals) in addition to a standard LLM (GPT-4o) to complete medical scenarios. AI-overview in web searches will be disabled via an extension. They would not be allowed to access any LLMs other than the standard LLM interface.

Sponsors & Collaborators

  • Lahore University of Management Sciences

    lead OTHER

Principal Investigators

  • Ihsan Ayyub Qazi, PhD · Lahore University of Management Sciences (LUMS)

  • Ayesha Ali, PhD · Lahore University of Management Sciences (LUMS)

  • Zafar Ayyub Qazi, PhD · Lahore University of Management Sciences (LUMS)

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-07-25
Primary Completion
2027-07-01
Completion
2027-07-01

Countries

  • Pakistan

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07728513 on ClinicalTrials.gov