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Completed

NCT Number: NCT06774612

The Impact of Large Language Models on Diagnostic Reasoning Among LLM-Trained Medical Doctors

This study aims to evaluate whether large language model-trained medical doctors demonstrate enhanced diagnostic reasoning performance when utilizing ChatGPT-4o alongside conventional resources compared to using conventional resources alone.

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Key information

Conditions

Diagnosis Disease Pathologic Processes Pathological Conditions, Signs and Symptoms

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Lahore University of Management Sciences

Lahore, Punjab Province, 54792, Pakistan

About this study

Diagnostic errors are a major source of preventable patient harm. Recent advances in Large Language Models (LLM), particularly ChatGPT-4o, have shown promise in enhancing medical decision-making. However, little is known about their impact on medical doctors' (e.g., physicians' and surgeons') diagnostic reasoning.

Diagnostic accuracy relies on complex clinical reasoning and careful evaluation of patient data. While AI assistance could potentially reduce errors and improve efficiency, ChatGPT-4o lacks medical validation and could introduce new risks through incorrect information generation (also known as hallucinations). To mitigate these risks, doctors need adequate training in understanding ChatGPT-4o's capabilities, limitations, and proper usage. Given these uncertainties and the importance of proper AI training, systematic evaluation is essential before clinical implementation.

This randomized study will assess whether ChatGPT-4o access improves LLM-trained medical doctors' diagnostic performance compared to conventional resources (e.g., textbooks, online medical databases) alone. All participating doctors will have completed at least a 10-hour training program covering ChatGPT-4o usage, prompt engineering techniques, and output evaluation strategies. Participants will provide differential diagnoses with supporting evidence and recommended next steps for clinical cases, with responses evaluated by blinded reviewers.

Who can participate

Healthy volunteers accepted: Yes

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

Inclusion criteria

  • Full or Provisionally Registered Medical Practitioners with the Pakistan Medical and Dental Council (PMDC).
  • Completed Bachelor of Medicine, Bachelor of Surgery (MBBS) Exam. The equivalent degree of MBBS in US and Canada is called Doctor of Medicine (MD).
  • Participants must have completed a structured training program on the use of ChatGPT (or a comparable large language model), totaling at least 10 hours of instruction. The program must include hands-on practice related to LLM's aspects, specifically prompt engineering and content evaluation.

Exclusion criteria

  • Any other Registered Medical Practitioners (Full or Provisional) with PMDC (e.g., Professionals with Bachelor of Dental Surgery or BDS).

Treatment and study plan

ChatGPT-4o

Other

OpenAI's ChatGPT-4o large language model with chat interface.

Primary outcomes

  1. Diagnostic reasoning

    Time frame: Assessed at a single time point for each case, during the scheduled diagnostic reasoning evaluation session, which takes place between 0-4 days after participant enrollment.

    The primary outcome will be the percent correct for each case (range: 0 to 100). For each case, participants will be asked for three top diagnoses, findings from the case that support that diagnosis, and findings from the case that oppose that diagnosis. For each plausible diagnosis, participants will receive 1 point. Findings supporting the diagnosis and findings opposing the diagnosis will also be graded based on correctness, with 1 point for partially correct and 2 points for completely correct responses. Participants will then be asked to name their top diagnosis, earning one point for a reasonable response and two points for the most correct response. Finally participants will be asked to name up to 3 next steps to further evaluate the patient with one point awarded for a partially correct response and two points for a completely correct response. The primary outcome will be compared on the case-level by the randomized groups.

Secondary outcomes

  1. Time Spent on Diagnosis

    Time frame: Assessed at a single time point for each case, during the scheduled diagnostic reasoning evaluation session, which takes place between 0-4 days after participant enrollment.

    We will compare how much time (in seconds) participants spend per case between the two study arms.

Sponsors and collaborators

Lead sponsor

Lahore University of Management Sciences

Other

Collaborators

  • King Edward Medical University

Registry information

Official study title

Diagnostic Reasoning With and Without AI Support: A Randomized Controlled Trial of LLM-Trained Medical Doctors

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Jan 14, 2025
Registry last updated
Jul 17, 2025

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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