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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Collective human intelligence outperforms artificial intelligence in a skin lesion classification task
Julia K. Winkler, Katharina Sies, Christine Fink, et al.
JDDG Journal der Deutschen Dermatologischen Gesellschaft (2021) Vol. 19, Iss. 8, pp. 1178-1184
Open Access | Times Cited: 20

Showing 20 citing articles:

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis
María Paz Salinas, Javiera Sepúlveda, Leonel Hidalgo, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 22
Artificial intelligence and skin cancer
Maria L. Wei, Mikio Tada, Alexandra So, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 18
Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?
Sara E. Cerminara, Phil F. Cheng, Lisa Kostner, et al.
European Journal of Cancer (2023) Vol. 190, pp. 112954-112954
Open Access | Times Cited: 22
Basic principles of artificial intelligence in dermatology explained using melanoma
Tim Hartmann, Johannes Passauer, Julien Hartmann, et al.
JDDG Journal der Deutschen Dermatologischen Gesellschaft (2024) Vol. 22, Iss. 3, pp. 339-347
Open Access | Times Cited: 6
Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review
Ian Miller, Nedeljka Rosić, Michael Stapelberg, et al.
Cancers (2024) Vol. 16, Iss. 7, pp. 1443-1443
Open Access | Times Cited: 6
Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review
Emilie A. Foltz, Alexander Witkowski, Alyssa L. Becker, et al.
Cancers (2024) Vol. 16, Iss. 3, pp. 629-629
Open Access | Times Cited: 5
In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment
Kevin W. Bishop, Kristen C. Maitland, Milind Rajadhyaksha, et al.
Journal of Biomedical Optics (2022) Vol. 27, Iss. 04
Open Access | Times Cited: 21
Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present
Linda Tognetti, Chiara Miracapillo, L. Simone, et al.
Bioengineering (2024) Vol. 11, Iss. 8, pp. 758-758
Open Access | Times Cited: 3
The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective
Joshua Yee, Cliff Rosendahl, Lauren G. Aoude
Melanoma Research (2023)
Open Access | Times Cited: 9
Collective human intelligence vs. artificial intelligence: a comparative analysis for melanoma diagnosis in darker skin tones
N. Litaiem, K. Sboui, Jinen Daghrir, et al.
International Journal of Dermatology (2024) Vol. 63, Iss. 10
Closed Access | Times Cited: 1
Versorgernetzwerke für Menschen mit Seltenen Erkrankungen: Daten und Expertise bündeln
Holm Graeßner, Holger Storf, Franz Schaefer
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz (2022) Vol. 65, Iss. 11, pp. 1164-1169
Open Access | Times Cited: 3
Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms
Tim Hartmann, Johannes Passauer, Julien Hartmann, et al.
JDDG Journal der Deutschen Dermatologischen Gesellschaft (2024) Vol. 22, Iss. 3, pp. 339-349
Open Access
Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions
Julia K. Winkler, Katharina S. Kommoss, Anastasia S. Vollmer, et al.
European Journal of Cancer (2024) Vol. 210, pp. 114297-114297
Closed Access
Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses
Jokūbas Liutkus, Artūras Kriukas, Dominyka Stragytė, et al.
Diagnostics (2023) Vol. 13, Iss. 13, pp. 2139-2139
Open Access | Times Cited: 1
Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning
Hequn Huang, Changqing Wang, Geng Gao, et al.
Dermatology and Therapy (2024) Vol. 14, Iss. 12, pp. 3307-3320
Open Access
Agreement on classification of clinical photographs of pigmentary lesions: Exercise after a training course with young dermatologists
Simone Cazzaniga, Lucia De Ponti, Giorgio Baratelli, et al.
Dermatology Reports (2022)
Open Access | Times Cited: 1
A digitális technológia kínálta új lehetőségek a melanoma malignum szűrésében.
Szabolcs Farkas-Ráduly, Marcell Csanádi, Tamás Ágh, et al.
Orvosi Hetilap (2022) Vol. 163, Iss. 49, pp. 1943-1951
Open Access | Times Cited: 1
Implementation of a dermatoscopy curriculum during residency at Augsburg University Hospital in Germany
Sandra Schuh, Stefan Schiele, Janis Thamm, et al.
JDDG Journal der Deutschen Dermatologischen Gesellschaft (2023) Vol. 21, Iss. 8, pp. 872-879
Open Access
Implementierung eines Dermatoskopie‐Curriculums in der Facharztausbildung am Universitätsklinikum Augsburg
Sandra Schuh, Stefan Schiele, Janis Thamm, et al.
JDDG Journal der Deutschen Dermatologischen Gesellschaft (2023) Vol. 21, Iss. 8, pp. 872-881
Open Access
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