Artificial intelligence applications in dermatology: Where do we stand?

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

Artificial intelligence in healthcare: past, present and future

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works

Big Data and Machine Learning in Health Care

Beam, A. L., & Kohane, I. S. (2018) · JAMA · doi:10.1001/jama.2017.18391 · Cited by 2,506 · see citing works

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.02.004 · Cited by 2,119 · 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

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Li, Y., & Shen, L. (2018) · Sensors · doi:10.3390/s18020556 · Cited by 645 · see citing works

From genome-wide arrays to tailor-made biomarker readout – Progress towards routine analysis of skin sensitizing chemicals with GARD

Forreryd, A., Zeller, K. S., Lindberg, T., Johansson, H., & Lindstedt, M. (2016) · Toxicology in Vitro · doi:10.1016/j.tiv.2016.09.013 · Cited by 440 · see citing works

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works

Melanoma detection by analysis of clinical images using convolutional neural network

Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Jafari, M. H., Ward, K., & Najarian, K. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590963 · Cited by 387 · see citing works

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

Huang, M. L., Hung, Y., Lee, W. M., Li, R., & Jiang, B.-R. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/795624 · Cited by 377 · see citing works

Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition

Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., et al. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.1735 · Cited by 365 · 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

Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks

Bi, L., Kim, J., Ahn, E., Kumar, A., FULHAM, M. J., & Feng, D. D. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2712771 · Cited by 337 · 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

Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model

Xie, F., Fan, H., Li, Y., Jiang, Z., Meng, R., & Bovik, A. C. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2633551 · Cited by 326 · see citing works

Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model

Alderden, J., Pepper, G. A., Wilson, A., Whitney, J. D., Richardson, S., Butcher, R., Jo, Y., & Cummins, M. R. (2018) · American Journal of Critical Care · doi:10.4037/ajcc2018525 · Cited by 262 · see citing works

Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine?

Wang, F., Kaushal, R., & Khullar, D. (2020) · Annals of Internal Medicine · doi:10.7326/m19-2548 · Cited by 244 · see citing works

Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms

Premaladha, J., & Ravichandran, K. S. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0460-2 · Cited by 208 · see citing works

The rise of artificial intelligence and the uncertain future for physicians

Krittanawong, C. (2017) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2017.06.017 · Cited by 205 · see citing works

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification

Wang, L., Pedersen, P. C., Agu, E., Strong, D. M., & Tulu, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2632522 · Cited by 177 · see citing works

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

Burlina, P., Billings, S., Joshi, N., & Albayda, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184059 · Cited by 144 · see citing works

Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind

Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.11.013 · Cited by 137 · see citing works

Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients

Patrick, M. T., Stuart, P. E., Raja, K., Gudjonsson, J. E., Tejasvi, T., Yang, J., Chandran, V., Das, S., Callis-Duffin, K. P., Ellinghaus, E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06672-6 · Cited by 136 · see citing works

Four-Class Classification of Skin Lesions With Task Decomposition Strategy

Shimizu, K., Iyatomi, H., Celebi, M. E., Norton, K., & Tanaka, M. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2348323 · Cited by 130 · see citing works

Acral melanoma detection using a convolutional neural network for dermoscopy images

Yu, C., Yang, S., Kim, W. O., Jung, J. W., Chung, K. Y., Lee, S. W., & Oh, B. H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193321 · Cited by 130 · see citing works

Automated Dermatological Diagnosis: Hype or Reality?

Navarrete-Dechent, C., Dusza, S. W., Liopyris, K., Marghoob, A. A., Halpern, A. C., & Marchetti, M. A. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.04.040 · Cited by 129 · see citing works

Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis

Dick, V., Sinz, C., Mittlböck, M., Kittler, H., & Tschandl, P. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.1375 · Cited by 125 · see citing works

Automated Classification of Skin Lesions: From Pixels to Practice

Narla, A., Kuprel, B., Sarin, K. Y., Novoa, R. A., & Ko, J. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.06.175 · Cited by 123 · see citing works

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

Mukherjee, R., Dhane, D. M., Das, D. K., Achar, A., Mitra, A., & Chakraborty, C. (2014) · BioMed Research International · doi:10.1155/2014/851582 · Cited by 119 · see citing works

Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology

Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_31_18 · Cited by 119 · see citing works

A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification

Shrivastava, V. K., Londhe, N. D., Sonawane, R. S., & Suri, J. S. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.07.011 · Cited by 118 · 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

Systematic review of machine learning for diagnosis and prognosis in dermatology

Thomsen, K. G., Iversen, L., Titlestad, T. L., & Winther, O. (2019) · Journal of Dermatological Treatment · doi:10.1080/09546634.2019.1682500 · Cited by 101 · see citing works

Gene expression analysis in Cutaneous T-Cell Lymphomas (CTCL) highlights disease heterogeneity and potential diagnostic and prognostic indicators

Litvinov, I. V., Tetzlaff, M. T., Thibault, P., Gangar, P., Moreau, L., Watters, A. K., Netchiporouk, E., Pehr, K., Prieto, V. G., Rahme, E., et al. (2017) · OncoImmunology · doi:10.1080/2162402x.2017.1306618 · Cited by 100 · see citing works

An unsupervised feature learning framework for basal cell carcinoma image analysis

Arévalo, J., Cruz-Roa, Á., Arias, V., Romero, E., & Gonzalez, F. A. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.04.004 · Cited by 99 · see citing works

Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma

Jafari, M. H., Nasr-Esfahani, E., Karimi, N., Soroushmehr, S. M. R., Samavi, S., & Najarian, K. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1567-8 · Cited by 98 · see citing works

Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study

Chang, W.-Y., Huang, A., Yang, C.-Y., Lee, C., Chen, Y.-C., Wu, T.-Y., & Chen, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076212 · Cited by 94 · see citing works

Multiple Transcriptome Data Analysis Reveals Biologically Relevant Atopic Dermatitis Signature Genes and Pathways

Ghosh, D., Ding, L., Sivaprasad, U., Geh, E., Biagini Myers, J., Bernstein, J. A., Khurana Hershey, G. K., & Mersha, T. B. (2015) · PLOS ONE · doi:10.1371/journal.pone.0144316 · Cited by 94 · see citing works

Attitudes towards artificial intelligence within dermatology: an international online survey

Polesie, S., Gillstedt, M., Kittler, H., Lallas, A., Tschandl, P., Zalaudek, I., & Paoli, J. (2020) · British Journal of Dermatology · doi:10.1111/bjd.18875 · Cited by 93 · see citing works

Automatic Segmentation of Scaling in 2-D Psoriasis Skin Images

Lu, J., Kazmierczak, E., Manton, J. H., & Sinclair, R. D. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2236349 · Cited by 88 · see citing works

Multivariate models for prediction of human skin sensitization hazard

Strickland, J. A., Zang, Q., Paris, M., Lehmann, D. M., Allen, D., Choksi, N. Y., Matheson, J. M., Jacobs, A. C., Casey, W., & Kleinstreuer, N. (2016) · Journal of Applied Toxicology · doi:10.1002/jat.3366 · Cited by 85 · see citing works

The Use of Transcriptional Profiling to Improve Personalized Diagnosis and Management of Cutaneous T-cell Lymphoma (CTCL)

Litvinov, I. V., Netchiporouk, E., Cordeiro, B., Doré, M.-A., Moreau, L., Pehr, K., Gilbert, M. J., Zhou, Y., Sasseville, D., & Kupper, T. S. (2015) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-3322 · Cited by 83 · see citing works

Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs

Seité, S., Khammari, A., Benzaquen, M., Moyal, D. D., & Dréno, B. (2019) · Experimental Dermatology · doi:10.1111/exd.14022 · Cited by 82 · see citing works

Computer versus human diagnosis of melanoma: evaluation of the feasibility of an automated diagnostic system in a prospective clinical trial

Dreiseitl, S., Binder, M., Hable, K., & Kittler, H. (2009) · Melanoma Research · doi:10.1097/cmr.0b013e32832a1e41 · Cited by 72 · see citing works

Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis

Arroyo, J. L. G., & García-Zapirain, B. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.11.002 · Cited by 71 · see citing works

Prediction of skin sensitization potency using machine learning approaches

Zang, Q., Paris, M., Lehmann, D. M., Bell, S. M., Kleinstreuer, N., Allen, D., Matheson, J. M., Jacobs, A. C., Casey, W., & Strickland, J. A. (2017) · Journal of Applied Toxicology · doi:10.1002/jat.3424 · Cited by 68 · see citing works

Computational approaches for skin sensitization prediction

Wilm, A., Kühnl, J., & Kirchmair, J. (2018) · Critical Reviews in Toxicology · doi:10.1080/10408444.2018.1528207 · Cited by 64 · see citing works

Fuzzy spectral clustering for automated delineation of chronic wound region using digital images

Dhane, D. M., Maity, M., Mungle, T., Bar, C., Achar, A., Kolekar, M. H., & Chakraborty, C. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.04.004 · Cited by 62 · see citing works

Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer

Zakhem, G. A., Fakhoury, J. W., Motosko, C. C., & Ho, R. S. (2020) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.01.028 · Cited by 60 · see citing works

Classification of pressure ulcer tissues with 3D convolutional neural network

García-Zapirain, B., Elmogy, M. M., El-Baz, A. S., & Elmaghraby, A. S. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1835-y · Cited by 59 · see citing works

Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assays

Tsujita‐Inoue, K., Hirota, M., Ashikaga, T., Atobe, T., Kouzuki, H., & Aiba, S. (2014) · Toxicology in Vitro · doi:10.1016/j.tiv.2014.01.003 · Cited by 54 · see citing works

A six-long non-coding RNA signature predicts prognosis in melanoma patients

Yang, S., Xu, J., & Zeng, X. (2018) · International Journal of Oncology · doi:10.3892/ijo.2018.4268 · Cited by 54 · see citing works

Predicting the long‐term outcomes of biologics in patients with psoriasis using machine learning

Emam, S., Du, A., Surmanowicz, P., Thomsen, S. F., Greiner, R., & Gniadecki, R. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18741 · Cited by 51 · see citing works

Ectopic Expression of Cancer–Testis Antigens in Cutaneous T-cell Lymphoma Patients

Litvinov, I. V., Cordeiro, B., Huang, Y., Zargham, H., Pehr, K., Doré, M.-A., Gilbert, M. J., Zhou, Y., Kupper, T. S., & Sasseville, D. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-0307 · Cited by 46 · see citing works

Automatic detection of melanoma using broad extraction of features from digital images

Jafari, M. H., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Ward, K., & Najarian, K. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590959 · Cited by 42 · see citing works

Development and evaluation of an automatic acne lesion detection program using digital image processing

Min, S., Kong, H., Yoon, C., Kim, H. C., & Suh, D. H. (2012) · Skin Research and Technology · doi:10.1111/j.1600-0846.2012.00660.x · Cited by 41 · see citing works

Genomic Allergen Rapid Detection In-House Validation—A Proof of Concept

Johansson, H., Rydnert, F., Kühnl, J., Schepky, A. G., Borrebaeck, C. A. K., & Lindstedt, M. (2014) · Toxicological Sciences · doi:10.1093/toxsci/kfu046 · Cited by 39 · see citing works

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

Daliri, M. R. (2012) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2012-0009 · Cited by 37 · see citing works

Detecting melanoma in dermoscopy images using scale adaptive local binary patterns

Riaz, F., Hassan, A., Javed, M. Y., & Coimbra, M. T. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945179 · Cited by 36 · see citing works

Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas

Liu, Z., Sun, J., Smith, L. N., Smith, M. L., & Warr, R. (2012) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-012-0895-7 · Cited by 34 · see citing works

A practical decision‐tree model to predict complexity of reconstructive surgery after periocular basal cell carcinoma excision

Tan, E., Lin, F. P. Y., Sheck, L. H. N., SALMON, P. J., & Ng, S. G. J. (2016) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.14012 · Cited by 34 · see citing works

Fuzzy logic color detection: Blue areas in melanoma dermoscopy images

Lingala, M., Stanley, R. J., Rader, R. K., Hagerty, J. R., Rabinovitz, H. S., Oliviero, M., Choudhry, I., & Stoecker, W. V. (2014) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2014.03.007 · Cited by 33 · see citing works

SVM classifier on chip for melanoma detection

Afifi, S. M., GholamHosseini, H., & Sinha, R. (2017) · doi:10.1109/embc.2017.8036814 · Cited by 33 · see citing works

Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification

Beibei Cheng, A., Stanley, R. J., Stoecker, W. V., Stricklin, S. M., Hinton, K. A., Nguyen, T. K., Rader, R. K., Rabinovitz, H. S., Oliviero, M., & Moss, R. H. (2012) · Skin Research and Technology · doi:10.1111/j.1600-0846.2012.00630.x · Cited by 31 · see citing works

A cascade classifier for diagnosis of melanoma in clinical images

Sabouri, P., GholamHosseini, H., Larsson, T. B., & Collins, J. J. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945177 · Cited by 31 · see citing works

A deep bag-of-features model for the classification of melanomas in dermoscopy images

Sabbaghi, S., Aldeen, M., & Garnavi, R. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590962 · Cited by 31 · see citing works

A multigene support vector machine predictor for metastasis of cutaneous melanoma

Dong, W. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.8219 · Cited by 31 · see citing works

Gene expression profiling and immune cell-type deconvolution highlight robust disease progression and survival markers in multiple cohorts of CTCL patients

Lefrançois, P., Xie, P., Wang, L., Tetzlaff, M. T., Moreau, L., Watters, A. K., Netchiporouk, E., Provost, N., Gilbert, M. J., Ni, X., et al. (2018) · OncoImmunology · doi:10.1080/2162402x.2018.1467856 · Cited by 31 · see citing works

Machine learning and melanoma: The future of screening

Safran, T., Viezel-Mathieu, A., Corban, J. P. A. H., Kanevsky, A., Thibaudeau, S., & Kanevsky, J. (2017) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2017.09.055 · Cited by 30 · see citing works

Artificial intelligence and melanoma diagnosis: ignoring human nature may lead to false predictions

Lallas, A., & Argenziano, G. (2018) · Dermatology Practical & Conceptual · doi:10.5826/dpc.0804a01 · Cited by 30 · see citing works

Automatic discrimination of actinic keratoses from clinical photographs

Σπυρίδωνος, Π. Π., Gaitanis, G., Likas, A. C., & Bassukas, I. D. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.07.001 · Cited by 26 · see citing works

Real‐time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location

Kaur, R. R., Albano, P. P., Cole, J. G., Hagerty, J. R., LeAnder, R., Moss, R. H., & Stoecker, W. V. (2015) · Skin Research and Technology · doi:10.1111/srt.12216 · Cited by 25 · see citing works

A pretrained neural network shows similar diagnostic accuracy to medical students in categorizing dermatoscopic images after comparable training conditions

Tschandl, P., Kittler, H., & Argenziano, G. (2017) · British Journal of Dermatology · doi:10.1111/bjd.15695 · Cited by 25 · see citing works

Automated skin biopsy histopathological image annotation using multi-instance representation and learning

Zhang, G., Yin, J. P., Li, Z., Su, X., Li, G., & Zhang, H. (2013) · BMC Medical Genomics · doi:10.1186/1755-8794-6-s3-s10 · Cited by 24 · see citing works

Segmentation of acne lesion using fuzzy C-means technique with intelligent selection of the desired cluster

Khan, J. A., Malik, A. S., Kamel, N., Dass, S. C., & Affandi, A. M. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319042 · Cited by 24 · see citing works

A comparison of classification methods as diagnostic system: A case study on skin lesions

Odeh, S. M., & Baareh, A. K. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.09.012 · Cited by 24 · see citing works

Artificial intelligence in dermatology: are we there yet?

Lim, B. C. W., & Flaherty, G. T. (2019) · British Journal of Dermatology · doi:10.1111/bjd.17899 · Cited by 23 · see citing works

Crowdsourcing dermatology: DataDerm, big data analytics, and machine learning technology

Park, A. J., Ko, J., & Swerlick, R. A. (2017) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2017.08.053 · Cited by 22 · see citing works

Differential evolution based advised SVM for histopathalogical image analysis for skin cancer detection

Masood, A., & Al-Jumaily, A. A. (2015) · doi:10.1109/embc.2015.7318478 · Cited by 21 · see citing works

SVM classifier on chip for melanoma detection

(16722666), S. A., (13727044), H. G., & Sinha, R. (2024) · Figshare · Cited by 21 · see citing works

TruSeq-Based Gene Expression Analysis of Formalin-Fixed Paraffin-Embedded (FFPE) Cutaneous T-Cell Lymphoma Samples: Subgroup Analysis Results and Elucidation of Biases from FFPE Sample Processing on the TruSeq Platform

Lefrançois, P., Tetzlaff, M. T., Moreau, L., Watters, A. K., Netchiporouk, E., Provost, N., Gilbert, M. J., Ni, X., Sasseville, D., Duvic, M. A., et al. (2017) · Frontiers in Medicine · doi:10.3389/fmed.2017.00153 · Cited by 20 · see citing works

Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris

Reimann, E., Lättekivi, F., Keermann, M., Abram, K., Kõks, S., Kingo, K., & Fazeli, A. (2019) · Acta Dermato Venereologica · doi:10.2340/00015555-3337 · Cited by 18 · see citing works

Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis

Zhang, G., Yin, J. P., Su, X., Huang, Y., Lao, Y., Liang, Z., Ou, S., & Zhang, H. (2014) · BioMed Research International · doi:10.1155/2014/305629 · Cited by 17 · see citing works

Automated lesion segmentation and dermoscopic feature segmentation for skin cancer analysis

Pour, M. P., Şeker, H., & Shao, L. (2017) · doi:10.1109/embc.2017.8036906 · Cited by 17 · see citing works

How I learned to stop worrying and love machine learning

Mattessich, S., Tassavor, M., Swetter, S. M., & Grant-Kels, J. M. (2018) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2018.06.003 · Cited by 16 · see citing works

Adaptable texture‐based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma

Kefel, S., Kefel, S., LeAnder, R., Kaur, R. R., Kasmi, R., Mishra, N. K., Rader, R. K., Cole, J. G., Woolsey, Z. T., & Stoecker, W. V. (2016) · Skin Research and Technology · doi:10.1111/srt.12281 · Cited by 13 · see citing works

PredyCLU: a prediction system for chronic leg ulcers based on fuzzy logic; part I – exploring the venous side

de Franciscis, S., Fregola, S., Gallo, A., Argirò, G., Barbetta, A., Buffone, G., Calio’, F. G., Caridi, G. D., Amato, B., & Serra, R. (2015) · International Wound Journal · doi:10.1111/iwj.12529 · Cited by 13 · see citing works

GA-ANFIS Expert System Prototype for Prediction of Dermatological Diseases

Fazlic, L. B., Avdagic, K., & Omanović, S. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-512-8-622 · Cited by 12 · see citing works

Automatic dirt trail analysis in dermoscopy images

Beibei Cheng, A., Stanley, R. J., Stoecker, W. V., Osterwise, C., Stricklin, S. M., Hinton, K. A., Moss, R. H., Oliviero, M., & Rabinovitz, H. S. (2012) · Skin Research and Technology · doi:10.1111/j.1600-0846.2011.00602.x · Cited by 11 · see citing works

PredyCLU : A prediction system for chronic leg ulcers based on fuzzy logic; part II —Exploring the arterial side

Serra, R., Bracale, U. M., Barbetta, A., Ielapi, N., Licastro, N., Gallo, A., Fregola, S., Turchino, D., Gasbarro, V., Mastroroberto, P., et al. (2020) · International Wound Journal · doi:10.1111/iwj.13360 · Cited by 11 · see citing works

Sector expansion and elliptical modeling of blue‐gray ovoids for basal cell carcinoma discrimination in dermoscopy images

Guvenc, P., LeAnder, R., Kefel, S., Stoecker, W. V., Rader, R. K., Hinton, K. A., Stricklin, S. M., Rabinovitz, H. S., Oliviero, M., & Moss, R. H. (2012) · Skin Research and Technology · doi:10.1111/srt.12006 · Cited by 10 · see citing works

Paraconsistent Artificial Neural Networks and Aspects of Pattern Recognition

Souza, S., & Abe, J. M. (2015) · Intelligent systems reference library · doi:10.1007/978-3-319-19722-7_9 · Cited by 10 · see citing works

The inevitability of change

el‐Azhary, R. A. (2018) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2018.09.003 · Cited by 10 · see citing works

Gene Variability between Perineural-Positive and Perineural-Negative Squamous Cell Skin Cancers

Mays, A. C., & Chou, J.-H. (2015) · International Journal of Genomic Medicine · doi:10.4172/2332-0672.1000133 · Cited by 7 · see citing works

Gene Variability between Perineural-Positive and Perineural-Negative Squamous Cell Skin Cancers

Mays, A. C., & Chou, J.-H. (2015) · Journal of Clinical & Medical Genomics · doi:10.4172/2472-128x.1000133 · Cited by 3 · see citing works

Nevus and Melanoma Paraconsistent Classification

Souza, S., & Minoro, A. J. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-474-9-244 · Cited by 3 · see citing works

A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition

Abedini, M., Stefan, V. C., Chakravorty, R., Davis, M. T., & Garnavi, R. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-691 · Cited by 3 · see citing works

A novel SVM-ID3 Hybrid Feature Selection Method to Build a Disease Model for Melanoma using Integrated Genotyping and Phenotype Data from dbGaP

Aydın, S. Y., & Yücebaş, S. C. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-432-9-501 · Cited by 1 · see citing works

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond

Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works

A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare

Ahmed, M. I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., & Dennison, A. (2023) · Cureus · doi:10.7759/cureus.46454 · Cited by 364 · see citing works

Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends

Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226826 · Cited by 279 · see citing works

A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Bharati, S., Mondal, M. R. H., & Podder, P. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3266418 · Cited by 235 · see citing works

AI-driven healthcare: A review on ensuring fairness and mitigating bias

Chinta, S. V., Wang, Z., Palikhe, A., Zhang, X., Kashif, A., Smith, M. A., Liu, J., & Zhang, W. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000864 · Cited by 241 · see citing works

Knowledge and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students

Mehta, N., Harish, V., Bilimoria, K., Morgado, F., Ginsburg, S., Law, M., & Das, S. (2021) · MedEdPublish · doi:10.15694/mep.2021.000075.1 · Cited by 145 · see citing works

Deep learning in dermatology: a systematic review of current approaches, outcomes, and limitations

Jeong, H. K., Park, C., Henao, R., & Kheterpal, M. (2023) · JID Innovations · doi:10.1016/j.xjidi.2022.100150 · Cited by 134 · see citing works

Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review

Knop, M., Weber, S., Mueller, M., & Niehaves, B. (2022) · JMIR Human Factors · doi:10.2196/28639 · Cited by 103 · see citing works

Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians

Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T., Nguyen, S. H., Tran, B. X., Latkin, C. A., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.755644 · Cited by 87 · see citing works

Artificial intelligence in the detection of skin cancer

Beltrami, E. J., Brown, A. C., SALMON, P. J., Leffell, D. J., Ko, J., & Grant-Kels, J. M. (2022) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2022.08.028 · Cited by 77 · see citing works

Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care

Escalé-Besa, A., Yélamos, O., Vidal‐Alaball, J., Fuster‐Casanovas, A., Catalina, Q. M., Börve, A., Aguilar, R. A.-E., Fustà‐Novell, X., Cubiró, X., Rafat, M. E., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-31340-1 · Cited by 70 · see citing works

Principles, applications, and future of artificial intelligence in dermatology

Omiye, J. A., Gui, H., Daneshjou, R., Cai, Z. R., & Muralidharan, V. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1278232 · Cited by 78 · 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

Acne in the first three decades of life: An update of a disorder with profound implications for all decades of life

Greydanus, D. E., Azmeh, R., Cabral, M. D., Dickson, C. A., & Patel, D. R. (2020) · Disease-a-Month · doi:10.1016/j.disamonth.2020.101103 · Cited by 72 · see citing works

Diagnosis of Onychomycosis: From Conventional Techniques and Dermoscopy to Artificial Intelligence

Lim, S. S., Ohn, J., & Mun, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.637216 · Cited by 59 · see citing works

Artificial intelligence in dermatology and healthcare: An overview

Pai, V. V., & Pai, R. B. (2021) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_518_19 · Cited by 53 · see citing works

Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey

Polesie, S., McKee, P. H., Gardner, J. M., Gillstedt, M., Siarov, J., Neittaanmäki, N., & Paoli, J. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.591952 · Cited by 53 · see citing works

Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review

Göktaş, P., & Grzybowski, A. E. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13195909 · Cited by 50 · see citing works

Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis

Liu, X., Duan, C., Kim, M.-K., Zhang, L., Jee, E., Maharjan, B., Huang, Y., Du, D., & Jiang, X. L. (2024) · JMIR Medical Informatics · doi:10.2196/59273 · Cited by 46 · see citing works

Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs

Bonfanti‐Gris, M., García-Cañas, Á., Alonso-Calvo, R., Salido, M. P., & Ramiro, G. P. (2022) · Journal of Dentistry · doi:10.1016/j.jdent.2022.104301 · Cited by 44 · see citing works

Trends in Graphene-Based E-Skin and Artificial Intelligence for Biomedical Applications—A Review

Mudhulu, S., Manjunatha, C., Balaji, S., Khosla, A., & Sekhar, P. K. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3294297 · Cited by 40 · see citing works

In silico Prediction of Skin Sensitization: Quo vadis?

Ta, G. H., Weng, C., & Leong, M. K. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.655771 · Cited by 40 · see citing works

Artificial Intelligence in Dermatopathology: New Insights and Perspectives

Cazzato, G., Colagrande, A., Cimmino, A., Arezzo, F., Loizzi, V., Caporusso, C., Marangio, M., Foti, C., Romita, P., Lospalluti, L., et al. (2021) · Dermatopathology · doi:10.3390/dermatopathology8030044 · Cited by 37 · see citing works

Artificial Intelligence for Dermatopathology: Current Trends and the Road Ahead

Chen, S. B., & Novoa, R. A. (2022) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2022.01.003 · Cited by 37 · see citing works

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis

Ye, Z., Zhang, D., Zhao, Y., Chen, M., Wang, H., Seery, S., Qu, Y., Xue, P., & Jiang, Y. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102934 · Cited by 38 · see citing works

Insights into the Pathogenesis of HS and Therapeutical Approaches

Rosi, E., Fastame, M. T., Scandagli, I., Cesare, A. D., Ricceri, F., Pimpinelli, N., & Prignano, F. (2021) · Biomedicines · doi:10.3390/biomedicines9091168 · Cited by 35 · see citing works

Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives

Zbrzezny, A. M., & Krzywicki, T. (2025) · Applied Sciences · doi:10.3390/app15147856 · Cited by 35 · see citing works

Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes – A Narrative Review

Havelin, A., & Hampton, P. J. (2022) · Psoriasis Targets and Therapy · doi:10.2147/ptt.s323471 · Cited by 24 · see citing works

Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Alabdaljabar, M. S., Hasan, B. S., Noseworthy, P. A., Maalouf, J. F., Ammash, N. M., & Hashmi, S. K. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s383810 · Cited by 24 · see citing works

Cutaneous Oncology: Strategies for Melanoma Prevention, Diagnosis, and Therapy

Saeed, W., Shahbaz, E., Maqsood, Q., Ali, S. W., & Mahnoor, M. (2024) · Cancer Control · doi:10.1177/10732748241274978 · Cited by 27 · see citing works

GeXSe (Generative Explanatory Sensor System): A Deep Generative Method for Human Activity Recognition of Smart Spaces IOT

Sun, Y., Pargoo, N. S., & Ortiz, J. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3541158 · Cited by 34 · see citing works

A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub‐epidermal moisture measurements

Lustig, M., Schwartz, D., Bryant, R. A., & Gefen, A. (2022) · International Wound Journal · doi:10.1111/iwj.13728 · Cited by 20 · see citing works

Artificial intelligence in dermatopathology: A systematic review

Lalmalani, R. M., Lim, C. X. Y., & Oh, C. C. (2025) · Clinical and Experimental Dermatology · doi:10.1093/ced/llae361 · Cited by 19 · see citing works

The Future of Precision Prevention for Advanced Melanoma

Lee, K., Betz‐Stablein, B., Stark, M. S., Janda, M., McInerney-Leo, A. M., Caffery, L. J., Gillespie, N. A., Yanes, T., & Soyer, H. P. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.818096 · 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

Artificial intelligence: its role in dermatopathology

Jartarkar, S. R. (2022) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_725_2021 · Cited by 18 · see citing works

Editorial: The Emerging Role of Artificial Intelligence in Dermatology

Mahmood, F., Bendayan, S., Ghazawi, F. M., & Litvinov, I. V. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.751649 · Cited by 19 · see citing works

A Review: Causes, Consequences, and Management Strategies of Facial Overfilling

Peng, C.-X., Xv, W., & Ao, Y.-J. (2025) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s539888 · 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

Artificial intelligence in Dermatopathology

Jartarkar, S. R., Cockerell, C. J., Patil, A., Kassir, M., Babaei, M., Weidenthaler‐Barth, B., Grabbe, S., & Goldust, M. (2023) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.15565 · Cited by 17 · see citing works

Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study

Marri, S. S., Inamadar, A. C., Janagond, A. B., & Albadri, W. (2023) · JMIR Dermatology · doi:10.2196/45529 · Cited by 15 · see citing works

Introducing Prescriptive and Predictive Analytics to MBA Students with Microsoft Excel

Diamant, A. (2023) · INFORMS Transactions on Education · doi:10.1287/ited.2023.0286 · Cited by 14 · see citing works

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings

Nguyen, K. A., Wilson, D. L., Diiulio, J., Hall, B., Militello, L. G., Gellad, W. F., Harle, C. A., Lewis, M. O., Schmidt, S. O., Rosenberg, E. I., et al. (2024) · Bioelectronic Medicine · doi:10.1186/s42234-024-00156-3 · Cited by 13 · see citing works

Erythemato-Squamous Diseases Prediction and Interpretation Using Explainable AI

Rathore, A. S., Arjaria, S. K., Gupta, M., Chaubey, G., Mishra, A. K., & Rajpoot, V. (2022) · IETE Journal of Research · doi:10.1080/03772063.2022.2114953 · Cited by 13 · see citing works

Indecision on the use of artificial intelligence in healthcare—A qualitative study of patient perspectives on trust, responsibility and self-determination using AI-CDSS

Schneider, D., Liedtke, W., Klausen, A. D., Lipprandt, M., Funer, F., Bratan, T., Heyen, N. B., Aichinger, H., & Langanke, M. (2025) · Digital Health · doi:10.1177/20552076251339522 · Cited by 15 · see citing works

Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond

Yang, G., Ye, Q., & Xia, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01998 · Cited by 13 · see citing works

Using Artificial Intelligence as a Diagnostic Decision Support Tool in Skin Disease: Protocol for an Observational Prospective Cohort Study

Escalé-Besa, A., Fuster‐Casanovas, A., Börve, A., Yélamos, O., Fustà‐Novell, X., Rafat, M. E., Marin-Gomez, F. X., & Vidal‐Alaball, J. (2022) · JMIR Research Protocols · doi:10.2196/37531 · Cited by 13 · see citing works

Application of artificial intelligence in the analysis of the facial skin health condition

Alagic, A., Alihodžić, S., Alispahić, N., Bečić, E., Smajović, A., Bečić, F., Bećirović, L. S., Pokvić, L. G., & Badnjević, A. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.06.005 · Cited by 15 · see citing works

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Grignaffini, F., Troiano, M., Barbuto, F., Simeoni, P., Mangini, F., D’Andrea, G., Piazzo, L., Cantisani, C., Musolff, N., Ricciuti, C., et al. (2023) · Algorithms · doi:10.3390/a16100466 · Cited by 12 · see citing works

Künstliche Intelligenz zur Unterstützung der Telemedizin am Beispiel Afrikas

Greis, C., Maul, L. V., Hsu, C., Djamei, V., Schmid‐Grendelmeier, P., & Navarini, A. A. (2020) · Der Hautarzt · doi:10.1007/s00105-020-04664-6 · Cited by 12 · see citing works

Out-of-Distribution Detection in Dermatology Using Input Perturbation and Subset Scanning

Kim, H., Tadesse, G. A., Cintas, C., Speakman, S., & Varshney, K. R. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761412 · Cited by 12 · see citing works

Artificial intelligence for nonmelanoma skin cancer

Trager, M. H., Gordon, E. R., Breneman, A., Weng, C., & Samie, F. H. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.016 · Cited by 11 · see citing works

Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China

Yu, J., Cheong, I. H., Kozlakidis, Z., & Wang, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1544520 · Cited by 12 · see citing works

Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network

Polesie, S., Gillstedt, M., Ahlgren, G., Ceder, H., Gyllencreutz, J. D., Fougelberg, J., Backman, E. J., Pakka, J., Zaar, O., & Paoli, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.723914 · Cited by 10 · see citing works

The Digital Way to Intercept Psoriatic Arthritis

Giovannini, I., Bösch, P., Dejaco, C., Marco, G. D., McGonagle, D., Quartuccio, L., Vita, S. D., Errichetti, E., & Zabotti, A. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.792972 · Cited by 10 · see citing works

Novel Discoveries and Clinical Advancements for Treating Onychomycosis: A Mechanistic Insight

Behera, J. K., Kumar, S., Sharma, R. K., Jain, A., Garg, N. K., Khopade, A. J., Sawant, K. K., Singh, R., & Nirbhavane, P. (2023) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2023.115174 · Cited by 9 · see citing works

Artificial intelligence in autoimmune bullous dermatoses

Manuelyan, K., Dragolov, M., Drenovska, K., Shahid, M., & Vassileva, S. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.008 · Cited by 9 · see citing works

The State of Artificial Intelligence in Skin Cancer Publications

Joly-Chevrier, M., Nguyen, A. X.-L., Liang, L., Lesko-Krleza, M., & Lefrançois, P. (2024) · Journal of Cutaneous Medicine and Surgery · doi:10.1177/12034754241229361 · Cited by 9 · see citing works

Automated Image Quality and Protocol Adherence Assessment of Examinations in Teledermatology: First Results

Ribeiro, R. P. S., & von Wangenheim, A. (2023) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2023.0155 · Cited by 8 · see citing works

AI in Psoriatic Disease: Scoping Review

Barlow, R., Bewley, A., & Gkini, M. A. (2024) · JMIR Dermatology · doi:10.2196/50451 · Cited by 9 · see citing works

Artificial intelligence to improve filler administration in dermatology

Landau, M., & Goldust, M. (2024) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.16472 · Cited by 8 · see citing works

Superficial Fungal Infections and Artificial Intelligence: A Review on Current Advances and Opportunities: REVISION

Pour, B. H. (2025) · Mycoses · doi:10.1111/myc.70007 · Cited by 8 · see citing works

The ethics of photo licensing

Tracy, L. E., Conover, S., & Waldman, R. A. (2021) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2021.01.013 · Cited by 8 · see citing works

[Translated article] Artificial intelligence in dermatology: A threat or an opportunity?

Martorell, A., Martín-Gorgojo, A., Ríos‐Viñuela, E., Rueda-Carnero, J. M., Alfageme, F., & Taberner, R. (2022) · Actas Dermo-Sifiliográficas · doi:10.1016/j.ad.2021.07.014 · Cited by 9 · see citing works

Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review

Cocuz, I. G., Niculescu, R., Popelea, M. C., Cocuz, M. E., Sabău, A.-H., Tinca, A. C., Cozac-Szőke, A. R., Chiorean, D. M., Budin, C. E., & Cotoi, O. S. (2025) · Diagnostics · doi:10.3390/diagnostics15172196 · Cited by 7 · see citing works

The ethics of using biased artificial intelligence programs in the clinic

Sood, A., Sangari, A., Chen, J. Y., & Stoff, B. K. (2021) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2021.11.031 · Cited by 7 · see citing works

Artificial Intelligence in Translational Medicine

Brogi, S., & Calderone, V. (2021) · International Journal of Translational Medicine · doi:10.3390/ijtm1030016 · Cited by 8 · see citing works

Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives

Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works

The Challenge Arising from New Knowledge about Immune and Inflammatory Skin Diseases: Where We Are Today and Where We Are Going

Campanati, A., Martina, E., & Offidani, A. (2022) · Biomedicines · doi:10.3390/biomedicines10050950 · Cited by 6 · see citing works

Explainable Artificial Intelligence (XAI) in Healthcare

Manek, A. S., Vashist, S., Tripathi, G., & Sindhu, S. (2025) · doi:10.1002/9781394249312.ch2 · Cited by 6 · see citing works

Artificial Intelligence-Aided Recognition of Pathological Characteristics and Subtype Classification of Superficial Perivascular Dermatitis

Bao, Y., Zhang, J., Zhang, Q., Chang, J., Lu, D., & Fu, Y. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.696305 · Cited by 5 · see citing works

L'office du juge : un éclairage via la modélisation mathématique

Véhel, J. L. (2020) · Les Cahiers de la Justice · doi:10.3917/cdlj.2004.0739 · Cited by 5 · see citing works

Contemporary Advances in Computer-Assisted Bone Histomorphometry and Identification of Bone Cells in Culture

Brent, M. B., & Emmanuel, T. (2022) · Calcified Tissue International · doi:10.1007/s00223-022-01035-2 · Cited by 5 · see citing works

Automated Machine Learning (AutoML) for the Diagnosis of Melanoma Skin Lesions From Consumer-Grade Camera Photos

Potluru, A., Arora, A., Arora, A., & Joiya, S. A. (2024) · Cureus · doi:10.7759/cureus.67559 · Cited by 5 · see citing works

The Impact of Artificial Intelligence on Diagnostic Medicine

Wheatley, M. (2024) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100007 · Cited by 4 · see citing works

Advances in genetic–immunological targeted therapies for psoriasis

Qi, Z., Wang, Y., Li, Z., & Sun, L. (2025) · Current Opinion in Immunology · doi:10.1016/j.coi.2025.102559 · Cited by 4 · see citing works

Evaluating the Impact of Skin Tone Representation on Out-of-Distribution Detection Performance in Dermatology

Benmalek, A., Cintas, C., Tadesse, G. A., Daneshjou, R., Varshney, K. R., & Cherifi, D. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635847 · Cited by 4 · see citing works

Advantages and Disadvantages of Using AI in Dermatology

Gholizadeh, N., Rokni, G. R., & Babaei, M. (2024) · Dermatological Reviews · doi:10.1002/der2.248 · Cited by 4 · see citing works

New challenges and opportunities to explainable artificial intelligence (XAI) in smart healthcare

Shoughi, A., Dowlatshahi, M. B., & Amiri, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23979-3.00020-8 · Cited by 4 · see citing works

Explainable artificial intelligence (XAI) for healthcare decision-making

Zeng, Z. (2022) · doi:10.32657/10356/155849 · Cited by 4 · see citing works

Machine learning methods for determining skin age: A systematic review

McMullen, E. P., Aflaki, R., Khatri, P. J., Metko, D., Storm, K., Butt, A. B., Maazi, M., Gupta, R., Grewal, R. S., & Champagne, T. (2025) · Journal of Tissue Viability · doi:10.1016/j.jtv.2025.100887 · Cited by 4 · see citing works

Optimizing Skin Quality via AI-Enhanced Physical Activity

Tertipi, Ν., Sfyri, Ε., Grech, V.-S., Kefala, V., & Rallis, E. A. (2025) · Cosmetics · doi:10.3390/cosmetics12030104 · Cited by 3 · see citing works

Physicians’ perceptions and attitudes on artificial intelligence in hospital management: a qualitative study

Zhou, X., Jomhari, N., Farid, N. D. N., & Xing, Y. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13815-1 · Cited by 4 · see citing works

A Comprehensive Diagnostic Tool for Skin Cancer Using a Multifaceted Computer Vision Approach

Srinivasan, P. H., & Srinivasan, V. (2020) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi51676.2020.9311557 · Cited by 3 · see citing works

Prediction and classification of pressure injuries by deep learning

Yılmaz, A., Kızıl, H., Kaya, U., Çakır, R., & Demiral, M. (2021) · Health Problems of Civilization · doi:10.5114/hpc.2021.110457 · Cited by 3 · see citing works

Phototherapy in the artificial intelligence era

Mancha, D., & Filipe, P. (2023) · Photodermatology Photoimmunology & Photomedicine · doi:10.1111/phpp.12890 · Cited by 2 · see citing works

Preprocessed Vision Transformers and Classical Classifiers in Diagnosing Skin Diseases

Şenol, B., & Demiroğlu, U. (2025) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.1577835 · Cited by 2 · see citing works

Theoretical appraisal of explanatory paradigms for artificial intelligence usage by medical doctors

Velasco, L. C., & Wang, W. (2025) · Digital Health · doi:10.1177/20552076251384221 · Cited by 2 · see citing works

Regenerative Strategies for Androgenetic Alopecia: Evidence, Mechanisms, and Translational Pathways

Britva, R. L., & Gilhar, A. (2026) · Cosmetics · doi:10.3390/cosmetics13010019 · Cited by 2 · see citing works

Cutting-edge AI technologies in skin cancer applications

Diao, Y., Chen, X., Huang, Z., Tan, Q., Yang, M., Yu, H., Xu, Y., & Hu, X. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218256 · Cited by 2 · see citing works

Emerging Applications of Artificial Intelligence in Dermatopathology

Smith, M. P., & Schulman, J. M. (2024) · Current Dermatology Reports · doi:10.1007/s13671-024-00431-1 · Cited by 2 · see citing works

Revolutionizing Microbial Infection Diagnosis: The Role of Artificial Intelligence

Hossainpour, H., & Mahmoudi, H. (2024) · Iranian Journal of Medical Microbiology · doi:10.30699/ijmm.18.2.66 · Cited by 2 · see citing works

Utility of artificial intelligence in dermatology: Challenges and perspectives

Sachdev, S. S., Tafti, F., Thorat, R., Kshirsagar, M. M., Pinge, S., & Sawant, S. S. (2025) · IP Indian Journal of Clinical and Experimental Dermatology · doi:10.18231/j.ijced.2025.001 · Cited by 2 · see citing works

Explainable image recognition models for aiding radiologists in clinical decision making

Michael, A., Vasanth, A., Sagayaradjy, F. A., Feroz, M. N., & Gnanapragasam, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24028-7.00014-3 · Cited by 1 · see citing works

Using ChatGPT 4.0 for diagnosis in Dermatology: performance analysis in clinical cases from Anais Brasileiros de Dermatologia

Pacheco, M. A., & Martini, A. P. S. (2025) · Anais Brasileiros de Dermatologia · doi:10.1016/j.abd.2025.501143 · Cited by 1 · see citing works

A 35-Year Longitudinal Analysis of Dermatology Patient Behavior Across Economic and Cultural Manifestations in Tunisia, and the Impact of Digital Tools

Akrout, M., Amdouni, H., Feriani, A., Kourda, M., & Abid, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23223-7_4 · Cited by 1 · see citing works

Artificial intelligence in dermatology – Where do we stand?

Mohta, A., & Mohta, A. (2023) · Indian Journal of Skin Allergy · doi:10.25259/ijsa_13_2022 · Cited by 1 · see citing works

An Interpretable Prediction Model for Stroke Based on XGBoost and SHAP

Fang, T., & Deng, J. (2023) · Journal of Clinical and Nursing Research · doi:10.26689/jcnr.v7i3.4957 · Cited by 1 · see citing works

Artificial intelligence in dermatology

Abraham, A., Sobhanakumari, K., & Mohan, A. (2020) · Journal of Skin and Sexually Transmitted Diseases · doi:10.25259/jsstd_49_2020 · Cited by 9 · 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

Artificial intelligence in dermatology: a clinician’s guide

Geetha, K. (2026) · Journal of the Egyptian Womenʼs Dermatologic Society · doi:10.4103/jewd.jewd_106_25

The development and use of artificial intelligence (AI) in dermatology: a narrative review

Darmawan, I., Yusharyahya, S. N., Sampurna, A. T., & Saputro, A. H. (2024) · Indonesian Journal of Biomedicine and Clinical Sciences · doi:10.22146/inajbcs.v56i3.15970

P003 Artificial intelligence in dermatology: current clinical applications, limitations and implementation challenges

Shelton, E. M., Patel, Moon, J., & Tolkachjov, S. N. (2026) · British Journal of Dermatology · doi:10.1093/bjd/ljag086.030