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| Name | Class |
|---|---|
| King Edward Medical University | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| ChatGPT-4o | Active Comparator | Group will be given access to ChatGPT-4o. |
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| Conventional resources | No Intervention | Group will not be given access to ChatGPT-4o but will be encouraged to use any resources they wish besides large language models (PubMed, Google without AI Overviews, etc). |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| ChatGPT-4o | Other | OpenAI's ChatGPT-4o large language model with chat interface. |
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| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic reasoning | 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. | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Time Spent on Diagnosis | We will compare how much time (in seconds) participants spend per case between the two study arms. | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Ihsan Ayyub Qazi, PhD | Lahore University of Management Sciences | Principal Investigator |
| Muhammad Asadullah Khawaja, MBBS | King Edward Medical University | Principal Investigator |
| Ayesha Ali, PhD | Lahore University of Management Sciences | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Lahore University of Management Sciences | Lahore | Punjab Province | 54792 | Pakistan |
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| ID | Term |
|---|---|
| D004194 | Disease |
| ID | Term |
|---|---|
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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The trial will be designed as a randomized, two-arm, single-blind parallel group study.
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Single (Outcomes Assessor)
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