Physician Reasoning on Diagnostic Cases With Large Language Models

Study Overview
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Brief Summary
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This study will evaluate the effect of providing access to GPT-4, a large language model, compared to traditional diagnostic decision support tools on performance on case-based diagnostic reasoning tasks.

Detailed Description
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Artificial intelligence (AI) technologies, specifically advanced large language models like OpenAI's ChatGPT, have the potential to improve medical decision-making. Although ChatGPT-4 was not developed for its use in medical-specific applications, it has demonstrated promise in various healthcare contexts, including medical note-writing, addressing patient inquiries, and facilitating medical consultation. However, little is known about how ChatGPT augments the clinical reasoning abilities of clinicians. Clinical reasoning is a complex process involving pattern recognition, knowledge application, and probabilistic reasoning. Integrating AI tools like ChatGPT-4 into physician workflows could potentially help reduce clinician workload and decrease the likelihood of missed diagnoses. However, ChatGPT-4 was not developed for the purpose of clinical reasoning nor has it been validated for this purpose. Further, it may be subject to disinformation, including convincing confabulations that may mislead clinicians. If clinicians misuse this tool, it may not improve diagnostic reasoning and could even cause harm. Therefore, it is important to study how clinicians use large language models to augment clinical reasoning prior to routine incorporation into patient care. In this study, we will randomize participants to answer diagnostic cases with or without access to ChatGPT-4. The participants will be asked to give three differential diagnoses for each case, with supporting and opposing findings for each diagnosis. Additionally they will be asked to provide their top diagnosis along with next diagnostic steps. Answers will be graded by independent reviewers blinded to treatment assignment.

Official Title
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Diagnostic Reasoning With Large Language Model Chat Bots

Conditions
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Diagnosis

Intervention / Treatment
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* Other: GPT-4


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Participants must be licensed physicians and have completed at least post-graduate year 2 (PGY2) of medical training. Training in Internal medicine, family medicine, or emergency medicine. Exclusion Criteria: Not currently practicing clinically.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: The trial will be designed as a randomized, two-arm, single-blind parallel group study.
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: GPT-4<br>Group will be given access to GPT-4. | Other: GPT-4<br>* OpenAI's GPT-4 large language model with chat interface.<br>|
| No Intervention: Usual resources<br>Group will not be given access to GPT-4 but will be encouraged to use any resources they wish besides large language models (UpToDate, Dynamed, google, etc). | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic reasoning | The primary outcome will be the percent correct (range: 0 to 100) for each case. For each case, participants will be asked for three top diagnoses and findings from the case that support that diagnosis and oppose that diagnosis. Participants will receive 1 point for each plausible diagnosis. 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. | During evaluation | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time Spent on Diagnosis | We will compare how much time (in minutes) participants spend per case. between the two study arms. | During evaluation | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Computer-assisted diagnosis, Large language models, clinical reasoning