Aurametrix/dermatology_longitudinal huggingface dataset:
Dataset: 72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). 
Reference (2023): Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.

Evaluation of ViT Scratch, homogenous dataset (ViT) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 87.50% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.

Evaluation of Fed-BEiT: A Transformer-based architecture within the same framework, pre-training models using masked image modeling (MIM) based on the BEiT (BERT Pre-training for Image Transformers) method; heterogenous dataset (Google's Family of LLMs) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 89.57% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.
Dataset: 72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). 
Reference (2023): Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.

Evaluation of Fed-MAE: A Transformer-based architecture within a generalized federated self-supervised learning framework, pre-training models using masked image modeling (MIM) based on the MAE (Masked Auto-Encoder) method; heterogenous dataset (Transformer) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 89.96% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.

Evaluation of BEiT ImageNet, homogenous dataset (Google's Family of LLMs) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 95.00% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.
Dataset: 72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). 
Reference (2023): Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.

Evaluation of ViT ImageNet, homogenous dataset (ViT) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 96.00% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.

Evaluation of Fed-BEiT: A Transformer-based architecture within the same framework, pre-training models using masked image modeling (MIM) based on the BEiT (BERT Pre-training for Image Transformers) method; homogenous dataset (Google's Family of LLMs) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 97.40% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.
Dataset: 72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). 
Reference (2023): Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.

Evaluation of Fed-MAE: A Transformer-based architecture within a generalized federated self-supervised learning framework, pre-training models using masked image modeling (MIM) based on the MAE (Masked Auto-Encoder) method; homogenous dataset (Transformer) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 97.40% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.

Evaluation of MAE ImageNet, homogenous dataset (NOT LLM/Transformer) — Image-based diagnostics, Melanoma
Condition/subject: Melanoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 97.40% (sample size: 5000)
Comments: Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.
Dataset: ISIC 2019 Challenge Dataset: Primarily contains images of white skin 21,000 dermoscopic images across five diagnostic categories:        Basal Cell Carcinoma (BCC)        Dermatofibroma (DF)        Melanoma (MEL)        Melanocytic Nevus (NV)        Squamous Cell Carcinoma (SCC);  AtlasDerm: Focuses on brown skin tones. Subset Used: 618 images across the same five diagnostic categories. Dermnet: Contains images spanning 23 types of dermatological diseases.    Subset Used: 276 images within the five targeted diagnostic categories. DarkDerm: Established by the authors of this study to represent dark skin tones. Subset Used: 216 images across the five diagnostic categories.
Reference (2022): Wu Y, Zeng D, Wang Z, Sheng Y, Yang L, James AJ, Shi Y, Hu J. Federated self-supervised contrastive learning and masked autoencoder for dermatological disease diagnosis. arXiv preprint arXiv:2208.11278. 2022 Aug 24. doi: 10.48550/arxiv.2208.11278

Evaluation of FedCLF (CNN-based), Local Fine-Tuning // Federated Contrastive Learning with Feature Sharing (FedCLF) is built upon Contrastive Learning (CL). It enables effective learning from decentralized unlabeled data by sharing features rather than raw data, thereby preserving privacy. (NOT LLM/TRANSFORMER) — Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)
Condition/subject: Squamous Cell Carcinoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 63.38% (sample size: 22000)
Comments: FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.

Evaluation of FedMAE (ViT-based), Local Fine-Tuning //FedMAE is based on Masked Autoencoders (MAE) and utilizes Vision Transformers (ViT) as the backbone architecture (VISION TRANSFORMERS) — Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)
Condition/subject: Squamous Cell Carcinoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 64.80% (sample size: 22000)
Comments: FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.
Dataset: ISIC 2019 Challenge Dataset: Primarily contains images of white skin 21,000 dermoscopic images across five diagnostic categories:        Basal Cell Carcinoma (BCC)        Dermatofibroma (DF)        Melanoma (MEL)        Melanocytic Nevus (NV)        Squamous Cell Carcinoma (SCC);  AtlasDerm: Focuses on brown skin tones. Subset Used: 618 images across the same five diagnostic categories. Dermnet: Contains images spanning 23 types of dermatological diseases.    Subset Used: 276 images within the five targeted diagnostic categories. DarkDerm: Established by the authors of this study to represent dark skin tones. Subset Used: 216 images across the five diagnostic categories.
Reference (2022): Wu Y, Zeng D, Wang Z, Sheng Y, Yang L, James AJ, Shi Y, Hu J. Federated self-supervised contrastive learning and masked autoencoder for dermatological disease diagnosis. arXiv preprint arXiv:2208.11278. 2022 Aug 24. doi: 10.48550/arxiv.2208.11278

Evaluation of FedCLF (CNN-based), Federated Fine-Tuning // Federated Contrastive Learning with Feature Sharing (FedCLF) is built upon Contrastive Learning (CL). It enables effective learning from decentralized unlabeled data by sharing features rather than raw data, thereby preserving privacy. (NOT LLM/TRANSFORMER) — Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)
Condition/subject: Squamous Cell Carcinoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 70.72% (sample size: 22000)
Comments: FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.

Evaluation of FedMAE (ViT-based), Federated Fine-Tuning//FedMAE is based on Masked Autoencoders (MAE) and utilizes Vision Transformers (ViT) as the backbone architecture (VISION TRANSFORMERS) — Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)
Condition/subject: Squamous Cell Carcinoma (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 74.77% (sample size: 22000)
Comments: FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.
Dataset: Not discussed
Reference (2024): Willow D. Pastard, Willow Pastard, Zane Sejdiu, Alexis Arza, James Cross, Razmig Garabet, Anna Chacon, Ellen N. Pritchett, A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones, Journal of the National Medical Association, Volume 116, Issue 4, 2024, Page 426, ISSN 0027-9684, https://doi.org/10.1016/j.jnma.2024.07.035. (https://www.sciencedirect.com/science/article/pii/S0027968424001160)

Evaluation of GPT-4V, ChatGPT (GPT series) — Image-based diagnostics
Condition/subject: Traction alopecia, dark skin  (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: 75.52% (sample size: N/A)
Comments: ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.

Evaluation of GPT-4V, ChatGPT (GPT series) — Image-based diagnostics
Condition/subject: Alopecia, Alopecia areata (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: Better than other forms of alopecia (sample size: N/A)
Comments: ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.

Evaluation of GPT-4V, ChatGPT (GPT series) — Image-based diagnostics
Condition/subject: Alopecia, Androgenetic alopecia (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: Better than traction alopecia and central centrifugal cicatrical alopecia (sample size: N/A)
Comments: ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.
Dataset: Not discussed
Reference (2024): Willow D. Pastard, Willow Pastard, Zane Sejdiu, Alexis Arza, James Cross, Razmig Garabet, Anna Chacon, Ellen N. Pritchett, A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones, Journal of the National Medical Association, Volume 116, Issue 4, 2024, Page 426, ISSN 0027-9684, https://doi.org/10.1016/j.jnma.2024.07.035. (https://www.sciencedirect.com/science/article/pii/S0027968424001160)

Evaluation of GPT-4V, ChatGPT (GPT series) — Image-based diagnostics
Condition/subject: Alopecia, Central centrifugal cicatricial alopecia in dark skin tones (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: Worse than most other forms of alopecia (sample size: N/A)
Comments: ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.

Evaluation of GPT-4V, ChatGPT (GPT series) — Image-based diagnostics
Condition/subject: Alopecia, Traction alopecia in dark skin tones (English)
Category: Medical Records and Diagnostic Processes — Clinical Practice
Validity/accuracy: Worse than other forms of alopecia (sample size: N/A)
Comments: ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — psoriasis: Objectives Clear and Achieved
Condition/subject: Psoriasis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — discoid lupus erythematosus: Objectives Clear and Achieved
Condition/subject: Discoid lupus erythematosus (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — a clavus: Objectives Clear and Achieved
Condition/subject: Clavus (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — warts: Objectives Clear and Achieved
Condition/subject: Warts (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — malignant melanoma: Objectives Clear and Achieved
Condition/subject: Melanoma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — actinic keratosis: Objectives Clear and Achieved
Condition/subject: Actinic Keratosis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — chronic urticaria: Objectives Clear and Achieved
Condition/subject: Chronic urticaria (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — shingles: Objectives Clear and Achieved
Condition/subject: Shingles (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — fungal nail infections: Objectives Clear and Achieved
Condition/subject: Fungal nail infections (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — pityriasis versicolor: Objectives Clear and Achieved
Condition/subject: Pityriasis versicolor (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — tinea corporis: Objectives Clear and Achieved
Condition/subject: Tinea corporis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — impetigo: Objectives Clear and Achieved
Condition/subject: Impetigo (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — erythrasma: Objectives Clear and Achieved
Condition/subject: Erythrasma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — dermatomyositis: Objectives Clear and Achieved
Condition/subject: Dermatomyositis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — scleroderma: Objectives Clear and Achieved
Condition/subject: Scleroderma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — morphea: Objectives Clear and Achieved
Condition/subject: Morphea (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — atopic eczema:  information presented is balanced and unbiased
Condition/subject: Atopic eczema (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — psoriasis:  information presented is balanced and unbiased
Condition/subject: Psoriasis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — an abscess:  information presented is balanced and unbiased
Condition/subject: Abscess (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — discoid lupus erythematosus:  information presented is balanced and unbiased
Condition/subject: Discoid lupus erythematosus (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — a clavus:  information presented is balanced and unbiased
Condition/subject: Clavus (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — warts:  information presented is balanced and unbiased
Condition/subject: Warts (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — basal cell carcinoma:  information presented is balanced and unbiased
Condition/subject: Basal Cell Carcinoma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — malignant melanoma:  information presented is balanced and unbiased
Condition/subject: Melanoma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — actinic keratosis:  information presented is balanced and unbiased
Condition/subject: Actinic Keratosis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — chronic urticaria:  information presented is balanced and unbiased
Condition/subject: Chronic urticaria (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — pyoderma gangrenosum:  information presented is balanced and unbiased
Condition/subject: Pyoderma gangrenosum (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — shingles:  information presented is balanced and unbiased
Condition/subject: Shingles (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — fungal nail infections:  information presented is balanced and unbiased
Condition/subject: Fungal nail infections (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — pityriasis versicolor:  information presented is balanced and unbiased
Condition/subject: Pityriasis versicolor (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — tinea corporis:  information presented is balanced and unbiased
Condition/subject: Tinea corporis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — impetigo:  information presented is balanced and unbiased
Condition/subject: Impetigo (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — erythrasma:  information presented is balanced and unbiased
Condition/subject: Erythrasma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — dermatomyositis:  information presented is balanced and unbiased
Condition/subject: Dermatomyositis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — scleroderma:  information presented is balanced and unbiased
Condition/subject: Scleroderma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — morphea:  information presented is balanced and unbiased
Condition/subject: Morphea (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — atopic eczema: Additional Sources Listed
Condition/subject: Atopic eczema (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — psoriasis: Additional Sources Listed
Condition/subject: Psoriasis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — an abscess: Additional Sources Listed
Condition/subject: Abscess (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — discoid lupus erythematosus: Additional Sources Listed
Condition/subject: Discoid lupus erythematosus (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — a clavus: Additional Sources Listed
Condition/subject: Clavus (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — warts: Additional Sources Listed
Condition/subject: Warts (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — basal cell carcinoma: Additional Sources Listed
Condition/subject: Basal Cell Carcinoma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — malignant melanoma: Additional Sources Listed
Condition/subject: Melanoma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — actinic keratosis: Additional Sources Listed
Condition/subject: Actinic Keratosis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — chronic urticaria: Additional Sources Listed
Condition/subject: Chronic urticaria (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity
Dataset: 20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians
Reference (2023): Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.

Evaluation of bloomz (BLOOM series) — pyoderma gangrenosum: Additional Sources Listed
Condition/subject: Pyoderma gangrenosum (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — shingles: Additional Sources Listed
Condition/subject: Shingles (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — fungal nail infections: Additional Sources Listed
Condition/subject: Fungal nail infections (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — pityriasis versicolor: Additional Sources Listed
Condition/subject: Pityriasis versicolor (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — tinea corporis: Additional Sources Listed
Condition/subject: Tinea corporis (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — impetigo: Additional Sources Listed
Condition/subject: Impetigo (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity

Evaluation of bloomz (BLOOM series) — erythrasma: Additional Sources Listed
Condition/subject: Erythrasma (English)
Category: Medication Recommendations and Treatment Efficacy — Clinical Practice
Validity/accuracy: 20.00% (sample size: 3)
Comments: Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity