Aurametrix/dermatology_longitudinal huggingface dataset:
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of GPT-4o (GPT series) — General dermatology questions
Condition/subject: Hidradenitis Suppurativa (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: How do you rate the severity of Hidradenitis Suppurativa? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of GPT-4o (GPT series) — General dermatology questions
Condition/subject: Pigmented Lesions/Melanoma (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What margin should be used for lentigo maligna (or severely dysplastic nevus)? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of GPT-4o (GPT series) — General dermatology questions
Condition/subject: Psoriasis (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: Which is the best biologic for psoriasis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of GPT-4o (GPT series) — General dermatology questions
Condition/subject: Skin Cancer (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What is the skin cancer screening recommendation for the general population? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of GPT-4o (GPT series) — General dermatology questions
Condition/subject: Dermatopathology (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What dermatoses should be included in the differential diagnosis of an eczematous reaction pattern? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of GPT-4o (GPT series) — General dermatology questions
Condition/subject: Acne (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What medications are used to treat hormonally exacerbated acne? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Dermatopathology, Immunodermatology, inflammatory, infectious, and systemic skin diseases (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 285)
Comments: Validity is assumed 100% since overall results were satisfactory for both ChatGPT and DermGPT. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Psoriasis (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What is the best treatment for mild psoriasis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Dermatopathology (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What is the difference between a papule and a macule? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Immunodermatology (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What are the most reported side effects of IL-23 inhibitors? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Granulomatous Diseases (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What is the etiology of Granuloma Annulare? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Papulosquamous Disorders (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What dermatoses should be included in the differential diagnosis of a papulosquamous reaction pattern? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Autoimmune Blistering Diseases (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: Bullous Pemphigoid is caused by an autoantibody response directed against which antigens? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Acne (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What are the guidelines for prescribing isotretinoin? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Vascular Dermatology (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What is the classic clinical appearance of calciphylaxis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Hidradenitis Suppurativa (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: How do you rate the severity of Hidradenitis Suppurativa? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Pigmented Lesions/Melanoma (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What margin should be used for lentigo maligna (or severely dysplastic nevus)? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Psoriasis (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: Which is the best biologic for psoriasis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Skin Cancer (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What is the skin cancer screening recommendation for the general population? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Dermatopathology (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What dermatoses should be included in the differential diagnosis of an eczematous reaction pattern? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. 
Reference (2025): Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040

Evaluation of DermGPT (GPT series) — General dermatology questions
Condition/subject: Acne (English)
Category: Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes — Medical Education and Evaluation
Validity/accuracy: 100.00% (sample size: 19)
Comments: What medications are used to treat hormonally exacerbated acne? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT�s citation quality was preferred, whereas DermGPT�s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)
Dataset: 60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital�s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.
Reference (2025): Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180

Evaluation of ChatGPT-3.5, March 2023 (GPT series) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 48.40% (sample size: 60)
Comments: ChatGPT showed moderate complete agreement. 48.4% vs 60% for experienced pathologists

Evaluation of ChatGPT-3.5, March 2023 (GPT series) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 71.70% (sample size: 60)
Comments: Partial or Complete Agreement. Some interpretative discrepancies existed. Similar to 28% for experienced pathologists. Approximately 1 in 4 cases showed no match with the external pathologist

Evaluation of Gemini, March 2023 (Google's Family of LLMs) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 33.00% (sample size: 60)
Comments: Gemini showed 33% complete agreement vs 60% for experienced pathologists. Lower performance in complete agreement compared to ChatGPT.

Evaluation of Gemini, March 2023 (Google's Family of LLMs) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 48.00% (sample size: 60)
Comments: Significantly fewer partial agreements. Compare to 28% for experienced pathologists. 33% complete agreement vs 60% for experienced pathologists Over half of Gemini�s responses did not align with the external pathologist
Dataset: 60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital�s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.
Reference (2025): Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180

Evaluation of ChatGPT-3.5 (GPT series) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Pilar (trichilemmal) cyst (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 1)
Comments: Agreement: Complete agreement. Description: A 54-year-old male patient presented with a fixed nodule on the head. The lesion was excised. Histol...

Evaluation of Gemini (Google's Family of LLMs) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Pilar (trichilemmal) cyst (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 1)
Comments: Agreement: Complete agreement. Description: A 54-year-old male patient presented with a fixed nodule on the head. The lesion was excised. Histol...

Evaluation of ChatGPT-3.5 (GPT series) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Pilomatricoma with atypical features (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 1)
Comments: Agreement: Complete agreement. Description: A 46-year-old female patient presented with a right periauricular hard lesion that enlarged over the...

Evaluation of Gemini (Google's Family of LLMs) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Pilomatricoma with atypical features (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 1)
Comments: Agreement: Complete agreement. Description: A 46-year-old female patient presented with a right periauricular hard lesion that enlarged over the...
Dataset: 60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital�s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.
Reference (2025): Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180

Evaluation of ChatGPT-3.5 (GPT series) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Fibroepithelial polyp (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 0.00% (sample size: 1)
Comments: Agreement: None agreement. Description: A 68-year-old male patient presented with a chronic anal fissure and a perianal raised skin lesion. ...

Evaluation of Gemini (Google's Family of LLMs) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Fibroepithelial polyp (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 1)
Comments: Agreement: Complete agreement. Description: A 68-year-old male patient presented with a chronic anal fissure and a perianal raised skin lesion. ...

Evaluation of ChatGPT-3.5 (GPT series) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Epidermal inclusion cyst with overlying lentigo simplex (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 1)
Comments: Agreement: Complete agreement. Description: A 55-year-old male patient presented with a left infraorbital papillomatous growth for 2 years that ...

Evaluation of Gemini (Google's Family of LLMs) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Epidermal inclusion cyst with overlying lentigo simplex (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 50.00% (sample size: 1)
Comments: Agreement: Partial agreement. Description: A 55-year-old male patient presented with a left infraorbital papillomatous growth for 2 years that ...

Evaluation of ChatGPT-3.5 (GPT series) — Diagnostics from histopathologic report texts, Agreement with external pathologist
Condition/subject: Basal cell carcinoma, infiltrating type (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 1)
Comments: Agreement: Complete agreement. Description: A 61-year-old female patient presented with a lesion on the nose for one year with slight bleeding. ...