Physician Reasoning on Diagnostic Cases With Large Language Models

Diagnosis Clinical Trial

Official title:

Diagnostic Reasoning With Large Language Model Chat Bots

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06157944
• Other study ID #: 71319
• Status: Completed
• Phase: N/A
• Start date: November 29, 2023
• Est. completion date: December 30, 2023

Study information

• Verified date: February 2024
• Source: Stanford University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 50
• Est. completion date: December 30, 2023
• Est. primary completion date: December 30, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Diagnosis
• Disease

Intervention

Other:
• GPT-4
OpenAI's GPT-4 large language model with chat interface.

Locations

Country	Name	City	State
United States	Stanford University	Palo Alto	California

Sponsors (3)

Lead Sponsor	Collaborator
Stanford University	Beth Israel Deaconess Medical Center, University of Minnesota

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Time Spent on Diagnosis	We will compare how much time (in minutes) participants spend per case between the two study arms.	During evaluation