ABSTRACT

Introduction Artificial intelligence (AI) has significant potential in medicine, especially in diagnostics and education. ChatGPT has achieved levels comparable to medical students on text-based USMLE questions, yet there’s a gap in its evaluation on image-based questions.

Methods This study evaluated ChatGPT-4’s performance on image-based questions from USMLE Step 1, Step 2, and Step 3. A total of 376 questions, including 54 image-based, were tested using an image-captioning system to generate descriptions for the images.

Results The overall performance of ChatGPT-4 on USMLE Steps 1, 2, and 3 was evaluated using 376 questions, including 54 with images. The accuracy was 85.7% for Step 1, 92.5% for Step 2, and 86.9% for Step 3. For image-based questions, the accuracy was 70.8% for Step 1, 92.9% for Step 2, and 62.5% for Step 3. In contrast, text-based questions showed higher accuracy: 89.5% for Step 1, 92.5% for Step 2, and 90.1% for Step 3. Performance dropped significantly for difficult image-based questions in Steps 1 and 3 (p=0.0196 and p=0.0020 respectively), but not in Step 2 (p=0.9574). Despite these challenges, the AI’s accuracy on image-based questions exceeded the passing rate for all three exams.

Conclusions ChatGPT-4 can handle image-based USMLE questions above the passing rate, showing promise for its use in medical education and diagnostics. Further development is needed to improve its direct image processing capabilities and overall performance.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

AbbreviationsAIArtificial IntelligenceChatGPTChat Generative Pre-trained TransformerUSMLEUnited States Medical Licensing ExaminationFSMBFederation of State Medical BoardsNBMENational Board of Medical ExaminersCNNConvolutional Neural NetworksGANGenerative Adversarial Networks