Automated Dermatological Diagnosis: Hype or Reality?

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works

Can we open the black box of AI?

Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773 · see citing works

Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition

Sharif, M., Bhagavatula, S., Bauer, L., & Reiter, M. K. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978392 · Cited by 1,530 · see citing works

Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

Han, S. S., Kim, M. S., Lim, W., Park, G., Park, I., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.01.028 · Cited by 715 · see citing works

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

Marchetti, M. A., Codella, N., Dusza, S. W., Gutman, D. A., Helba, B., Kalloo, A., Mishra, N. K., Carrera, C., Celebi, M. E., DeFazio, J., et al. (2017) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2017.08.016 · Cited by 329 · see citing works

The Performance of SolarScan

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Computer-aided classification of melanocytic lesions using dermoscopic images

Ferris, L. K., Harkes, J., Gilbert, B., Winger, D. G., Golubets, K., Akilov, O. E., & Satyanarayanan, M. (2015) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2015.07.028 · Cited by 104 · see citing works

Potential Overdiagnosis of Basal Cell Carcinoma in Older Patients With Limited Life Expectancy

Linos, E., Schroeder, S. A., & Chren, M. (2014) · JAMA · doi:10.1001/jama.2014.9655 · Cited by 49 · see citing works

Human-computer collaboration for skin cancer recognition

Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0942-0 · Cited by 1,024 · see citing works

Fairness of artificial intelligence in healthcare: review and recommendations

Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607 · see citing works

AI recognition of patient race in medical imaging: a modelling study

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00063-2 · Cited by 611 · see citing works

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Brinker, T. J., Hekler, A., Utikal, J. S., Grabe, N., Schadendorf, D., Klode, J., Berking, C., Steeb, T., Enk, A. H., & Kalle, C. V. (2018) · Journal of Medical Internet Research · doi:10.2196/11936 · Cited by 504 · see citing works

What is AI? Applications of artificial intelligence to dermatology

Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020) · British Journal of Dermatology · doi:10.1111/bjd.18880 · Cited by 355 · see citing works

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan, S. Y., Reddy, V. D., Myers, B. A., Thibodeaux, Q. G., Brownstone, N. D., & Liao, W. J. (2020) · Dermatology and Therapy · doi:10.1007/s13555-020-00372-0 · Cited by 275 · see citing works

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.06.049 · Cited by 267 · see citing works

Artificial Intelligence in Dermatology: A Primer

Young, A. T., Xiong, M., Pfau, J., Keiser, M. J., & Wei, M. L. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.02.026 · Cited by 249 · see citing works

Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., Chae, J. B., Huh, C. H., & Na, J.-I. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.01.019 · Cited by 248 · see citing works

Melanoma diagnosis using deep learning techniques on dermatoscopic images

Jojoa, M., Caballero, L., García-Zapirain, B., & Percybrooks, W. (2021) · BMC Medical Imaging · doi:10.1186/s12880-020-00534-8 · Cited by 244 · see citing works

Characteristics of publicly available skin cancer image datasets: a systematic review

Wen, D., Khan, S. M., Ji Xu, A., Ibrahim, H., Smith, L., Caballero, J., Zepeda, L., de Blas Perez, C., Denniston, A. K., Liu, X., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00252-1 · Cited by 243 · see citing works

Artificial Intelligence in Dermatology: Challenges and Perspectives

Liopyris, K., Gregoriou, S., Dias, J., & Stratigos, A. (2022) · Dermatology and Therapy · doi:10.1007/s13555-022-00833-8 · Cited by 195 · see citing works

Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review

Hogarty, D. T., Su, J. C., Phan, K. H., Attia, M., Hossny, M., Nahavandi, S., Lenane, P., Moloney, F. J., & Yazdabadi, A. (2019) · American Journal of Clinical Dermatology · doi:10.1007/s40257-019-00462-6 · Cited by 186 · see citing works

Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty, D. T., Mackey, D. A., & Hewitt, A. W. (2018) · Clinical and Experimental Ophthalmology · doi:10.1111/ceo.13381 · Cited by 178 · see citing works

Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

Bajwa, M. N., Muta, K., Malik, M. I., Siddiqui, S. A., Braun, S. A., Homey, B., Dengel, A. R., & Ahmed, S. (2020) · Applied Sciences · doi:10.3390/app10072488 · Cited by 183 · see citing works

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

Salinas, M. P., Sepúlveda, J., Hidalgo, L., Peirano, D., Morel-Marambio, M., Uribe, P., Rotemberg, V. M., Briones, J., Mery, D., & Navarrete‐Dechent, C. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01103-x · Cited by 165 · see citing works

Artificial intelligence applications in dermatology: Where do we stand?

Gomolin, A., Netchiporouk, E., Gniadecki, R., & Litvinov, I. V. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00100 · Cited by 142 · see citing works

Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope

Dascalu, A., & David, E. O. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.04.055 · Cited by 126 · see citing works

Transparent medical image AI via an image–text foundation model grounded in medical literature

Kim, C., Gadgil, S. U., DeGrave, A. J., Omiye, J. A., Cai, Z. R., Daneshjou, R., & Lee, S.-I. (2024) · Nature Medicine · doi:10.1038/s41591-024-02887-x · Cited by 125 · see citing works

Use of artificial intelligence in dermatology

De, A., Sarda, A., Gupta, S., & DAS, S. K. (2020) · Indian Journal of Dermatology · doi:10.4103/ijd.ijd_418_20 · Cited by 119 · see citing works

Tackling prediction uncertainty in machine learning for healthcare

Chua, M., Kim, D., Choi, J., Lee, N. G., Deshpande, V., Schwab, J., Lev, M. H., Gonzalez, R. G., Gee, M. S., & Do, S. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00988-x · Cited by 114 · see citing works

Buruli Ulcer: a Review of the Current Knowledge

Yotsu, R. R., Suzuki, K., Simmonds, R. E., Bedimo, R., Ablordey, A. S., Yeboah‐Manu, D., Phillips, R. O., & Asiedu, K. B. (2018) · Current Tropical Medicine Reports · doi:10.1007/s40475-018-0166-2 · Cited by 116 · see citing works

Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

Saba, T. (2021) · Microscopy Research and Technique · doi:10.1002/jemt.23686 · Cited by 109 · see citing works

Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017

Marchetti, M. A., Liopyris, K., Dusza, S. W., Codella, N., Gutman, D. A., Helba, B., Kalloo, A., Halpern, A. C., Soyer, H. P., Curiel‐Lewandrowski, C. N., et al. (2019) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2019.07.016 · Cited by 106 · see citing works

The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers

Fujisawa, Y., Inoue, S., & Nakamura, Y. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00191 · Cited by 84 · see citing works

Evaluation of Artificial Intelligence–Assisted Diagnosis of Skin Neoplasms: A Single-Center, Paralleled, Unmasked, Randomized Controlled Trial

Han, S. S., Kim, Y. J., Moon, I. J., Jung, J. M., Lee, M. Y., Lee, W. J., Won, C. H., Lee, M. W., Kim, S. H., Navarrete‐Dechent, C., et al. (2022) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2022.02.003 · Cited by 77 · see citing works

Artificial intelligence in dermatology: advancements and challenges in skin of color

Fliorent, R., Fardman, B., Podwójniak, A., Javaid, K., Tan, I. J., Ghani, H., Truong, T. M., Rao, B. K., & Heath, C. R. (2024) · International Journal of Dermatology · doi:10.1111/ijd.17076 · Cited by 69 · see citing works

Performance of a deep neural network in teledermatology: a single‐centre prospective diagnostic study

Muñoz‐López, C., Ramírez‐Cornejo, C., Marchetti, M. A., Han, S. S., Del Barrio‐Díaz, P., Jaque, A., Uribe, P., Majerson, D., Curi, M., Del Puerto, C., et al. (2021) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.16979 · Cited by 70 · see citing works

Skin Cancer diagnosis with Yolo Deep Neural Network

Aishwarya, N., Prabhakaran, K. M., Debebe, F. T., Reddy, M. S. S. A., & Pranavee, P. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.03.083 · Cited by 66 · see citing works

Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach

Rezk, E., Eltorki, M., & El-Dakhakhni, W. W. (2022) · JMIR Dermatology · doi:10.2196/39143 · Cited by 69 · see citing works

Position statement of the EADV Artificial Intelligence ( AI ) Task Force on AI ‐assisted smartphone apps and web‐based services for skin disease

Sangers, T. E., Kittler, H., Blum, A., Braun, R. P., Barata, C., Cartocci, A., Combalia, M., Esdaile, B. A., Guitera, P., Haenssle, H. A., et al. (2023) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.19521 · Cited by 64 · see citing works

Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study

Han, S. S., Moon, I. J., Kim, S. H., Na, J., Kim, M. S., Park, G., Park, I., Kim, K., Lim, W., Lee, J. H., et al. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003381 · Cited by 61 · see citing works

The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World

Felmingham, C. M., Adler, N. R., Ge, Z., Morton, R. L., Janda, M., & Mar, V. J. (2021) · American Journal of Clinical Dermatology · doi:10.1007/s40257-020-00574-4 · Cited by 57 · see citing works

Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies

Walker, B., Rehg, J. M., Kalra, A., Winters, R. M., Drews, P., Dascalu, J., David, E. O., & Dascalu, A. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.01.028 · Cited by 54 · see citing works

Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

Young, A. T., Fernandez, K., Pfau, J., Reddy, R., Cao, N. A., von Franque, M., Johal, A., Wu, B., Wu, R. R., Chen, J. Y., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00380-6 · Cited by 53 · see citing works

From data to diagnosis: skin cancer image datasets for artificial intelligence

Wen, D., Soltan, A., Trucco, E., & Matin, R. N. (2024) · Clinical and Experimental Dermatology · doi:10.1093/ced/llae112 · Cited by 50 · see citing works

Building an Otoscopic screening prototype tool using deep learning

Livingstone, D., Talai, A. S., Chau, J., & Forkert, N. D. (2019) · Journal of Otolaryngology - Head & Neck Surgery · doi:10.1186/s40463-019-0389-9 · Cited by 42 · see citing works

Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study

Rezk, E., Eltorki, M., & El-Dakhakhni, W. W. (2022) · JMIR Research Protocols · doi:10.2196/34896 · Cited by 43 · see citing works

Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare

Kleinberg, G., Diaz, M. J., Batchu, S., & Lucke-Wold, B. P. (2022) · Journal of Biomed Research · doi:10.46439/biomedres.3.025 · Cited by 41 · see citing works

Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images

Cullell-Dalmau, M., Otero-Viñas, M., & Manzo, C. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2019.12.029 · Cited by 41 · see citing works

Implementation of artificial intelligence and non-contact infrared thermography for prediction and personalized automatic identification of different stages of cellulite

Bauer, J., Hoq, M. N., Mulcahy, J., Tofail, S. A. M., Gulshan, F., Silien, C., Podbielska, H., & Akbar, M. M. (2020) · The EPMA Journal · doi:10.1007/s13167-020-00199-x · Cited by 39 · see citing works

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects

Lyakhova, U. A., & Lyakhov, P. A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108742 · Cited by 41 · see citing works

Artificial Intelligence in Dermatology: A Comprehensive Review of Approved Applications, Clinical Implementation, and Future Directions

Nahm, W. J., Sohail, N., Burshtein, J. G., Goldust, M., & Tsoukas, M. M. (2025) · International Journal of Dermatology · doi:10.1111/ijd.17847 · Cited by 42 · see citing works

Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis

Jairath, N. K., Pahalyants, V., Shah, R., Weed, J. G., Carucci, J. A., & Criscito, M. C. (2024) · Dermatologic Surgery · doi:10.1097/dss.0000000000004223 · Cited by 31 · see citing works

Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting

Felmingham, C. M., MacNamara, S., Cranwell, W. C., Williams, N., Wada, M., Adler, N. R., Ge, Z., Sharfe, A., Bowling, A., Haskett, M., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-050203 · Cited by 32 · see citing works

Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population

Wang, S., Zhang, X.-Y., Liu, J., Tao, C., Zhu, C., Shu, C., Xu, T., & Jin, H. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001023 · Cited by 30 · see citing works

Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement

Navarrete‐Dechent, C., Liopyris, K., & Marchetti, M. A. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.06.040 · Cited by 29 · see citing works

Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration

Navarrete‐Dechent, C., Liopyris, K., MOLENDA, M. A., Braun, R. P., Curiel‐Lewandrowski, C. N., Dusza, S. W., Guitera, P., Hofmann‐Wellenhof, R., Kittler, H., Lallas, A., et al. (2020) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.16855 · Cited by 26 · see citing works

The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks

Veronese, F., Branciforti, F., Zavattaro, E., Tarantino, V., Romano, V. C., Meiburger, K. M., Salvi, M., Seoni, S., & Savoia, P. (2021) · Diagnostics · doi:10.3390/diagnostics11030451 · Cited by 27 · see citing works

Race-Specific and Skin of Color Dermatoscopic Characteristics of Skin Cancer: A Literature Review

Karampinis, E., Lallas, A., Lazaridou, E., Errichetti, E., & Apalla, Z. (2023) · Dermatology Practical & Conceptual · doi:10.5826/dpc.1304s1a311s · Cited by 25 · see citing works

Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD

Mabrouk, M. S., Sayed, A. Y., Afifi, H. M., Sheha, M. A., & Sharwy, A. (2020) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00067-3 · Cited by 24 · see citing works

Interpretation of the Outputs of a Deep Learning Model Trained with a Skin Cancer Dataset

Han, S. S., Lim, W., Kim, M. S., Park, I., Park, G., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.05.014 · Cited by 24 · see citing works

The Past, the Present and the Future of Teledermatology: A Narrative Review

Tommasino, N., Megna, M., Cacciapuoti, S., Villani, A., Martora, F., Ruggiero, A., Genco, L., & Potestio, L. (2024) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s462799 · Cited by 23 · see citing works

Dermoscopy and skin imaging light sources: a comparison and review of spectral power distribution and color consistency

Hanlon, K., Wei, G., Correa‐Selm, L., & Grichnik, J. M. (2022) · Journal of Biomedical Optics · doi:10.1117/1.jbo.27.8.080902 · Cited by 23 · see citing works

Deep Convolutional Neural Network for Melanoma Detection using Dermoscopy Images

Kaur, R., GholamHosseini, H., & Sinha, R. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175391 · Cited by 21 · see citing works

Adversarial attack on deep learning-based dermatoscopic image recognition systems

Allyn, J., Allou, N., Vidal, C., Renou, A., & Ferdynus, C. (2020) · Medicine · doi:10.1097/md.0000000000023568 · Cited by 20 · see citing works

Review: a comparative study of state-of-the-art skin image segmentation techniques with CNN

Nasreen, G., Haneef, K., Tamoor, M., & Irshad, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13756-5 · Cited by 20 · see citing works

Artificial intelligence for melanoma diagnosis

Tschandl, P. (2021) · Italian Journal of Dermatology and Venereology · doi:10.23736/s2784-8671.20.06753-x · Cited by 19 · see citing works

Risks and benefits of dermatological machine learning health care applications—an overview and ethical analysis

Willem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 18 · see citing works

Revolutionizing dermatology: The role of artificial intelligence in clinical practice

Salam, A., & Abhinesh, N. (2024) · IP Indian Journal of Clinical and Experimental Dermatology · doi:10.18231/j.ijced.2024.021 · Cited by 19 · see citing works

Artificial Intelligence Approach in Melanoma

Curiel‐Lewandrowski, C. N., Novoa, R. A., Berry, E., Celebi, M. E., Codella, N., Giuste, F., Gutman, D. A., Halpern, A. C., Leachman, S. A., Liu, Y., et al. (2019) · Springer eBooks · doi:10.1007/978-1-4614-7322-0_43-1 · Cited by 20 · see citing works

Informing a position statement on the use of artificial intelligence in dermatology in Australia

Caffery, L. J., Janda, M., Miller, R. E., Abbott, L. M., Arnold, C., Caccetta, T. P., Guitera, P., Shumack, S. P., Fernández‐Peñas, P., Mar, V., et al. (2022) · Australasian Journal of Dermatology · doi:10.1111/ajd.13946 · Cited by 18 · see citing works

Marking the Path Toward Artificial Intelligence–Based Image Classification in Dermatology

Novoa, R. A., Gevaert, O., & Ko, J. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.1633 · Cited by 17 · see citing works

Künstliche Intelligenz und Smartphone-Programm-Applikationen (Apps)

Blum, A., Bosch, S., Haenssle, H. A., Fink, C., Hofmann‐Wellenhof, R., Zalaudek, I., Kittler, H., & Tschandl, P. (2020) · Der Hautarzt · doi:10.1007/s00105-020-04658-4 · Cited by 16 · see citing works

Inteligencia artificial en dermatología: ¿amenaza u oportunidad?

Martorell, A., Martín-Gorgojo, A., Ríos‐Viñuela, E., Rueda-Carnero, J. M., Alfageme, F., & Taberner, R. (2021) · Actas Dermo-Sifiliográficas · doi:10.1016/j.ad.2021.07.003 · Cited by 18 · see citing works

Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet

Cho, S. I., Navarrete‐Dechent, C., Daneshjou, R., Cho, H. S., Chang, S. E., Kim, S. H., Na, J., & Han, S. S. (2023) · JAMA Dermatology · doi:10.1001/jamadermatol.2023.3521 · Cited by 16 · see citing works

Assessing Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists

Oloruntoba, A. I., Nguyen, T. D., Ge, Z., Vestergaard, T., Mar, V., Betz-Stablein, B., Soyer, H. P., Ge, Z., & Mar, V. (2021) · JMIR Dermatology · doi:10.2196/35150 · Cited by 14 · see citing works

The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search

Han, S. S., Navarrete‐Dechent, C., Liopyris, K., Kim, M. S., Park, G. H., Woo, S. S., Park, J., Shin, J. W., Kim, B. R., Kim, M. J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-20632-7 · Cited by 13 · see citing works

Harnessing of real-world data and real-world evidence using digital tools: utility and potential models in rheumatology practice

Kataria, S., & Ravindran, V. K. (2021) · Rheumatology · doi:10.1093/rheumatology/keab674 · Cited by 12 · see citing works

Best Practices for Clinical Skin Image Acquisition in Translational Artificial Intelligence Research

Phung, M., Muralidharan, V., Rotemberg, V. M., Novoa, R. A., Chiou, A. S., Sadée, C., Rapaport, B. R., Yekrang, K., Bitz, J., Gevaert, O., et al. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.02.035 · Cited by 12 · see citing works

Localization and Risk Stratification of Thyroid Nodules in Ultrasound Images Through Deep Learning

Wang, Z., Wang, X., Wang, T., Qiu, J., & Lu, W. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.02.013 · Cited by 11 · see citing works

Machine learning and human learning: a socio-cultural and -material perspective on their relationship and the implications for researching working and learning

Guile, D., & Popov, J. (2024) · AI & Society · doi:10.1007/s00146-024-01891-6 · Cited by 11 · see citing works

Generalizability and usefulness of artificial intelligence for skin cancer diagnostics: An algorithm validation study

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COVID ‐19 and artificial intelligence: Experts and dermatologists perspective

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How well can we predict Mohs micrographic surgery number of stages and reconstruction type? Reply to Shoham et al.

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Planet-wide performance of a skin disease AI algorithm validated in Korea

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Artificial Intelligence in Dermatology: A Review of Literature and Application to Pediatric Dermatology

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The Impact of Melanoma Imaging Biomarker Cues on Detection Sensitivity and Specificity in Melanoma versus Clinically Atypical Nevi

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Artificial intelligence for computer assistance in endoscopic procedures and training

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Melanoma Prevention and Screening

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A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers

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AI in Dermatology: Bridging the Gap Between Potential and Practice

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Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017

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Melanoma Skin Cancer Detection with Deep learning and Artificial Intelligence: A Systematic Review

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Machine versus man in skin cancer diagnosis

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Computerassistierte Hautkrebsdiagnose

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