Aurametrix/dermatology_longitudinal huggingface dataset:
Dataset: 30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility
Reference (2025): Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.

Evaluation of ChatGPT-4 (GPT series) — What does the condition feel like?/Symptoms, Symptomatology
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma.  (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 90.00% (sample size: 20)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.

Evaluation of ChatGPT-4 (GPT series) — How is the condition diagnosed?, Diagnostic Process
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma.  (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 30.00% (sample size: 20)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.
Dataset: 30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility
Reference (2025): Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.

Evaluation of ChatGPT-4 (GPT series) — Can the condition be cured?, Prognosis
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma.  (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 100.00% (sample size: 20)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.

Evaluation of ChatGPT-4 (GPT series) — What is the treatment?, Treatment Options
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma.  (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 100.00% (sample size: 20)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.
Dataset: 30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility
Reference (2025): Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.

Evaluation of ChatGPT-4 (GPT series) — Self-care advice, Patient Empowerment/Self-Help
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma.  (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 20)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.

Evaluation of ChatGPT-4 (GPT series) — Additional information for the patient, Supplementary Resources
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma.  (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 20)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.
Dataset: 30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility
Reference (2025): Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.

Evaluation of ChatGPT-4 (GPT series) — Accuracy in terms of no Misleading information generated
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 100.00% (sample size: 10)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.

Evaluation of ChatGPT-4 (GPT series) — Accuracy in terms of no Misleading information generated
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 100.00% (sample size: 10)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.

Evaluation of ChatGPT-4 (GPT series) — Accuracy in terms of no Misleading information generated
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 100.00% (sample size: 10)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.
Dataset: 30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility
Reference (2025): Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.

Evaluation of ChatGPT-4 (GPT series) — Misleading information generated
Condition/subject: Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 100.00% (sample size: 10)
Comments: Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.
Dataset: 40 routine-care dermatology allergology questions (out of 120 questions from routine allergological practice)
Reference (2025): Mathes S, Seurig S, Bluhme F, Beyer K, Heizmann F, Wagner M, Neug�rtner I, Biedermann T, Darsow U. ChatGPT performance on 120 interdisciplinary allergology questions - systematic evaluation with clinical error impact assessment for critical erroneous AI-guided chatbot-advice. J Allergy Clin Immunol Pract. 2025 Mar 27:S2213-2198(25)00280-6. doi: 10.1016/j.jaip.2025.03.030. Epub ahead of print. PMID: 40157421.

Evaluation of ChatGPT 3.5 (GPT series) — Allergic dermatology, accuracy of answers
Condition/subject: Fragrance allergy (English)
Category: AI performance evaluation — Healthcare AI
Validity/accuracy: 88.00% (sample size: 40)
Comments: one critical error: recommended natural fragrances despite allergy. Results for accuracy (ACC) were good with a mean Likert grade of 4.1 on a 5-step scale (SD = 0.78, range = 1-5, IQR:293 3-5) across all 3 specialties (dermatology: mean = 4.4 [range = 4-5, IQR: 4-5]; pediatrics mean = 3.8 [range294 = 1-5, IQR: 3-5]; pulmonology mean = 4.1 [range = 2-5, IQR: 4-5]) corresponding to an overall adequate accuracy of ChatGPT's answers, with no significant distinction. ; The mean completeness (CO) Likert grades (3-grade scale) were consistent across all three specialties (overall mean = 2.32; dermatology mean = 2.38; pediatrics mean = 2.25; pulmonology mean = 2.33).

Evaluation of ChatGPT 3.5 (GPT series) — Allergic dermatology, completeness of answers
Condition/subject: Fragrance allergy (English)
Category: AI performance evaluation — Healthcare AI
Validity/accuracy: 79.33% (sample size: 40)
Comments: one critical error: recommended natural fragrances despite allergy. Results for accuracy (ACC) were good with a mean Likert grade of 4.1 on a 5-step scale (SD = 0.78, range = 1-5, IQR:293 3-5) across all 3 specialties (dermatology: mean = 4.4 [range = 4-5, IQR: 4-5]; pediatrics mean = 3.8 [range294 = 1-5, IQR: 3-5]; pulmonology mean = 4.1 [range = 2-5, IQR: 4-5]) corresponding to an overall adequate accuracy of ChatGPT's answers, with no significant distinction. ; The mean completeness (CO) Likert grades (3-grade scale) were consistent across all three specialties (overall mean = 2.32; dermatology mean = 2.38; pediatrics mean = 2.25; pulmonology mean = 2.33).
Reference (2025):

Evaluation of ChatGPT 4 (GPT series) — Teledermatology consultations, exact match with the teledermatologist's diagnoses
Condition/subject: Top diagnosis, exact match with the teledermatologist's diagnoses (English)
Validity/accuracy: 70.80% (sample size: 154)
Comments: Out of 154 cases, ChatGPT?4 achieved a Top1 diagnostic concordance in 108 (70.8%), Top3 concordance in 137 (87.7%), partial concordance in four (2.6%), and was discordant in 15 (9.7%) cases. The quality of ChatGPT?4's image descriptions significantly surpassed teledermatologists in all five parameters. ChatGPT?4's descriptions were accurate in 130 (84.4%), partially accurate in 22 (14.3%), and inaccurate in two (1.3%) cases.
Reference (2025):

Evaluation of ChatGPT 4 (GPT series) — Teledermatology consultations, correct differential diagnosis (matches top three teledermatologist's diagnoses)
Condition/subject: Differential diagnosis (matches top three teledermatologist's diagnoses) (English)
Validity/accuracy: 87.70% (sample size: 154)
Comments: Out of 154 cases, ChatGPT?4 achieved a Top1 diagnostic concordance in 108 (70.8%), Top3 concordance in 137 (87.7%), partial concordance in four (2.6%), and was discordant in 15 (9.7%) cases. The quality of ChatGPT?4's image descriptions significantly surpassed teledermatologists in all five parameters. ChatGPT?4's descriptions were accurate in 130 (84.4%), partially accurate in 22 (14.3%), and inaccurate in two (1.3%) cases.
Reference (2025):

Evaluation of ChatGPT 4 (GPT series) — Teledermatology consultations, similar but not necessarily identical diagnoses
Condition/subject: Similar diagnoses (English)
Validity/accuracy: 90.26% (sample size: 154)
Comments: Out of 154 cases, ChatGPT?4 achieved a Top1 diagnostic concordance in 108 (70.8%), Top3 concordance in 137 (87.7%), partial concordance in four (2.6%), and was discordant in 15 (9.7%) cases. The quality of ChatGPT?4's image descriptions significantly surpassed teledermatologists in all five parameters. ChatGPT?4's descriptions were accurate in 130 (84.4%), partially accurate in 22 (14.3%), and inaccurate in two (1.3%) cases.
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): Murthy et al., 2025

Evaluation of ChatGPT v3.5, August 2023 (GPT series) — Medical Knowledge Assessment
Condition/subject: Dermatology, Venereology, and Leprosy - Multiple Topics (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 59.80% (sample size: 60)
Comments: Performed best on hard questions; struggled with inflammatory diseases and leprosy; 4 hallucinations

Evaluation of Microsoft Bing Chat, August 2023 (GPT series) — Medical Knowledge Assessment
Condition/subject: Dermatology, Venereology, and Leprosy - Multiple Topics (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 78.20% (sample size: 60)
Comments: Outperformed both ChatGPT and human respondents overall; no hallucinations; best on easy and medium questions
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): <0.001

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Basic sciences (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 70.00% (sample size: 3)
Comments: Total correct by ChatGPT , Mean � SD (%) 2.1 ± 0.2 (70%) vs Total correct by human respondents 1.7 ± 0.2 (56.7%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.024

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Infections (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 52.50% (sample size: 4)
Comments: Total correct by ChatGPT , Mean � SD (%) 2.1 ± 0.2 (52.5%) vs Total correct by human respondents 2.3 ± 0.4 (57.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): <0.001

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Inflammatory diseases (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 12.90% (sample size: 5)
Comments: Total correct by ChatGPT , Mean � SD (%) 0.4 ± 0.2 (12.9%) vs Total correct by human respondents 3.1 ± 0.5 (62%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.056

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Connective tissue disorders (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 85.00% (sample size: 4)
Comments: Total correct by ChatGPT , Mean � SD (%) 3.4 ± 0.2 (85%) vs Total correct by human respondents 2.0 ± 0.2 (50%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.047

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Genodermatoses (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 86.70% (sample size: 6)
Comments: Total correct by ChatGPT , Mean � SD (%) 5.2 ± 0.3 (86.7%) vs Total correct by human respondents 3.1 ± 0.7 (51.7%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.061

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Metabolic disorders (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 86.00% (sample size: 5)
Comments: Total correct by ChatGPT, 4.3 ± 0.2 (86%)  vs Total correct by human respondents 3.0 ± 0.3 (60%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.011

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Vascular diseases (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 87.50% (sample size: 4)
Comments: Total correct by ChatGPT, 3.5 ± 0.2 (87.5%)  vs Total correct by human respondents 1.9 ± 0.4 (47.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): <0.001

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Pilosebaceous disorders (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 64.00% (sample size: 5)
Comments: Total correct by ChatGPT, 3.2 ± 0.3 (64%)  vs Total correct by human respondents 2.5 ± 0.2 (50%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.01

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Cutaneous adverse drug reactions (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 52.50% (sample size: 4)
Comments: Total correct by ChatGPT, 2.1 ± 0.2 (52.5%)  vs Total correct by human respondents 1 ± 0.3 (25%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.058

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Benign and malignant tumours (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 55.00% (sample size: 4)
Comments: Total correct by ChatGPT, 2.2 ± 0.2 (55%)  vs Total correct by human respondents 2.1 ± 0.3 (52.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): <0.001

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Therapeutics (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 36.70% (sample size: 3)
Comments: Total correct by ChatGPT, 1.1 ± 0.3 (36.7%)  vs Total correct by human respondents 1.5 ± 0.2 (50%)

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Leprosy (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 22.00% (sample size: 5)
Comments: Total correct by ChatGPT, 1.1 ± 1.1 (22%)  vs Total correct by human respondents 3.4 ± 0.2 (68%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): 0.023

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Sexually transmitted diseases (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 77.50% (sample size: 4)
Comments: Total correct by ChatGPT, 3.1 ± 0.2 (77.5%)  vs Total correct by human respondents 2.1 ± 0.4 (52.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025): <0.001

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Aesthetic dermatology (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 52.50% (sample size: 4)
Comments: Total correct by ChatGPT, 2.1 ± 0.5 (52.5%)  vs Total correct by human respondents 1.4 ± 0.3 (35%)

Evaluation of ChatGPT 3.5 (GPT series) — Medical Knowledge Assessment
Condition/subject: Dermatology, Venereology, and Leprosy - Multiple Topics (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 35.90% (sample size: 60)
Comments: Total correct by ChatGPT, 35.9±0.5  vs Total correct by human respondents 25.8±11.0
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Basic sciences (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 73.30% (sample size: 3)
Comments: Total correct by Bing Chat, 2.2 ± 0.3 (73.3%)  vs Total correct by human respondents 1.7 ± 0.2 (56.7%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Infections (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 85.00% (sample size: 4)
Comments: Total correct by Bing Chat, 3.4 ± 0.1 (85%)  vs Total correct by human respondents 2.3 ± 0.4 (57.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Inflammatory diseases (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 44.00% (sample size: 5)
Comments: Total correct by Bing Chat, 2.2 ± 0.7 (44%)  vs Total correct by human respondents 3.1 ± 0.5 (62%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Connective tissue disorders (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 80.00% (sample size: 4)
Comments: Total correct by Bing Chat, 3.2 ± 0.3 (80%)  vs Total correct by human respondents 2.0 ± 0.2 (50%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Genodermatoses (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 86.70% (sample size: 6)
Comments: Total correct by Bing Chat, 5.2 ± 0.3 (86.7%)  vs Total correct by human respondents 3.1 ± 0.7 (51.7%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Metabolic disorders (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 88.00% (sample size: 5)
Comments: Total correct by Bing Chat, 4.4 ± 0.3 (88%)  vs Total correct by human respondents 3.0 ± 0.3 (60%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Vascular diseases (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 85.00% (sample size: 4)
Comments: Total correct by Bing Chat, 3.4 ± 0.4 (85%)  vs Total correct by human respondents 1.9 ± 0.4 (47.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Pilosebaceous disorders (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 84.00% (sample size: 5)
Comments: Total correct by Bing Chat, 4.2 ± 0.3 (84%)  vs Total correct by human respondents 2.5 ± 0.2 (50%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Cutaneous adverse drug reactions (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 55.00% (sample size: 4)
Comments: Total correct by Bing Chat, 2.2 ± 0.3 (55%)  vs Total correct by human respondents 1 ± 0.3 (25%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Benign and malignant tumours (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 80.00% (sample size: 4)
Comments: Total correct by Bing Chat, 3.2 ± 0.2 (80%)  vs Total correct by human respondents 2.1 ± 0.3 (52.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Therapeutics (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 76.70% (sample size: 3)
Comments: Total correct by Bing Chat, 2.3 ± 0.2 (76.7%)  vs Total correct by human respondents 1.5 ± 0.2 (50%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Leprosy (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 62.00% (sample size: 5)
Comments: Total correct by Bing Chat, 3.1 ± 0.2 (62%)  vs Total correct by human respondents 3.4 ± 0.2 (68%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Sexually transmitted diseases (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 80.00% (sample size: 4)
Comments: Total correct by Bing Chat, 3.2 ± 0.2 (80%)  vs Total correct by human respondents 2.1 ± 0.4 (52.5%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Aesthetic dermatology (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 77.50% (sample size: 4)
Comments: Total correct by Bing Chat, 3.1 ± 0.3 (77.5%)  vs Total correct by human respondents 1.4 ± 0.3 (35%)
Dataset: Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)
Reference (2025):

Evaluation of BingChat (GPT series) — Medical Knowledge Assessment
Condition/subject: Dermatology, Venereology, and Leprosy - Multiple Topics (English)
Category: Performance comparison — Medical education and evaluation
Validity/accuracy: 43.00% (sample size: 60)
Comments: Total correct by Bing Chat, 46.9±0.7  vs Total correct by human respondents 25.8±11.0
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, 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 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: 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 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 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 GPT-4o (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 GPT-4o (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 GPT-4o (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 GPT-4o (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 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 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 GPT-4o (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.)