Aurametrix/dermatology_longitudinal huggingface dataset:
Dataset: Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.
Reference (2024): Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: General dermatological conditions (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 53.70% (sample size: 54)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Acne (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 83.33% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Psoriasis (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 66.67% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Basal cell carcinoma (BCC) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 66.67% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.
Dataset: Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.
Reference (2024): Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Eczema (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 16.67% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Actinic keratosis (AK) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 33.33% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Rosacea (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 100.00% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Squamous cell carcinoma (SCC) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 16.67% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.
Dataset: Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.
Reference (2024): Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Superficial spreading melanoma (SSM) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 66.67% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based diagnosis, Top diagnosis
Condition/subject: Vitiligo (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 50.00% (sample size: 6)
Comments: Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: General dermatological conditions (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 50.00% (sample size: 54)
Comments: Validity reflects accuracy but not completness.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Acne (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 33.33% (sample size: 6)
Comments: Validity reflects accuracy but not completness. In some diseases (e.g., eczema or melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Psoriasis (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 33.33% (sample size: 6)
Comments: Validity reflects accuracy but not completness.
Dataset: Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.
Reference (2024): Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Basal cell carcinoma (BCC) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 83.33% (sample size: 6)
Comments: Validity reflects accuracy but not completness.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Eczema (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 66.67% (sample size: 6)
Comments: Validity reflects accuracy but not completness. In some diseases (e.g., eczema or melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Actinic keratosis (AK) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 16.67% (sample size: 6)
Comments: Validity reflects accuracy but not completness.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Rosacea (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 50.00% (sample size: 6)
Comments: Validity reflects accuracy but not completness. In some diseases (e.g., eczema or melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Squamous cell carcinoma (SCC) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 66.67% (sample size: 6)
Comments: Validity reflects accuracy but not completness.

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Superficial spreading melanoma (SSM) (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 83.33% (sample size: 6)
Comments: Validity reflects accuracy but not completness. In some diseases (e.g., eczema or Superficial spreading melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.
Dataset: Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.
Reference (2024): Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743

Evaluation of GPT-4V (GPT series) — Image-based differential diagnosis
Condition/subject: Vitiligo (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 16.67% (sample size: 6)
Comments: Validity reflects accuracy but not completness.

Evaluation of GPT-4V (GPT series) — Diagnostic accuracy (text-based), Text-based diagnosis, Top diagnosis, Scenario, Primary Diagnosis, Correct primary diagnosis
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 89.00% (sample size: 9)
Comments: Clinical scenarios reviewed by dermatologists

Evaluation of GPT-4V (GPT series) — Diagnostic accuracy (multimodal, image + text). Both image and scenario-based. Top diagnosis. Primary Diagnosis, Correct primary diagnosis
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 89.00% (sample size: 9)
Comments: Combination of images and clinical scenarios

Evaluation of GPT-4V (GPT series) — Image, Primary Diagnosis, Correct primary diagnosis
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 54.00% (sample size: 54)
Comments: Combination of images and clinical scenarios

Evaluation of GPT-4V (GPT series) — Image, Differential Diagnosis, Correct diagnosis identified in differential
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 50.00% (sample size: 54)
Comments: Combination of images and clinical scenarios
Dataset: Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.
Reference (2024): Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743

Evaluation of GPT-4V (GPT series) — Scenario-based diagnosis, Differential Diagnosis, Correct diagnosis identified in differential
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 56.00% (sample size: 9)
Comments: Combination of images and clinical scenarios

Evaluation of GPT-4V (GPT series) — Scenario-based diagnosis, Treatment Entrustment, Average treatment Entrustment Score
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 78.66% (sample size: 9)
Comments: Combination of images and clinical scenarios

Evaluation of GPT-4V (GPT series) — Multimodal diagnostics, Both (Image-based,  Scenario-based), Differential Diagnosis, Correct diagnosis identified in differential
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 44.00% (sample size: 9)
Comments: Combination of images and clinical scenarios

Evaluation of GPT-4V (GPT series) — Multimodal diagnostics, Both (Image-based,  Scenario-based), Treatment Entrustment, Average treatment Entrustment Score
Condition/subject: Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo. (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 81.34% (sample size: 9)
Comments: Combination of images and clinical scenarios
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 Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Acne (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Atopic dermatitis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Alopecia (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Psoriasis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards
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 Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Rosacea (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: 10.4 (outside 5�8 range); FRET: 50.6 (below 60); FKGLT: 8.4 (within 8�9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: SCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: 11.0 (outside 5�8 range); FRET: 52.3 (below 60); FKGLT: 8.5 (within 8�9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: BCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: 11.1 (outside 5�8 range); FRET: 53.0 (below 60); FKGLT: 8.6 (within 8�9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility

Evaluation of Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Hidradenitis suppurativa (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: 10.8 (outside 5�8 range); FRET: 48.4 (below 60); FKGLT: 9.0 (within 8�9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility
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 Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Vitiligo (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dr. Dermatology  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: c (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Atopic dermatitis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: 10.1 (outside 5�8 range); FRET: 45.1( below 60); FKGLT: 8.7 (within 8�9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility
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 Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Alopecia (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Psoriasis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Rosacea (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: SCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards
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 Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: BCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Hidradenitis suppurativa (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Vitiligo (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Dermatology Adviser  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.
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 Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Acne (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Atopic dermatitis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Alopecia (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Psoriasis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility
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 Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Rosacea (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: SCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: BCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility

Evaluation of Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Hidradenitis suppurativa (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility
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 Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Vitiligo (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of Chat with a Dermatologist  (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Acne (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Atopic dermatitis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards
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) — Readability of  dermatological patient information leaflets
Condition/subject: Alopecia (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Psoriasis (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Rosacea (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: SCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility
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) — Readability of  dermatological patient information leaflets
Condition/subject: BCC (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Hidradenitis suppurativa (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Vitiligo (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 0.00% (sample size: 3)
Comments: None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards

Evaluation of ChatGPT-4 (GPT series) — Readability of  dermatological patient information leaflets
Condition/subject: Melanoma (in situ) (English)
Category: Patient Education Materials and Readability Studies — Patient Education
Validity/accuracy: 33.33% (sample size: 3)
Comments: SMOG: outside 5�8 range; FRET: below 60; FKGLT: within 8�9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility