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Cited by in F6Publishing
For: Puri P, Comfere N, Drage LA, Shamim H, Bezalel SA, Pittelkow MR, Davis MDP, Wang M, Mangold AR, Tollefson MM, Lehman JS, Meves A, Yiannias JA, Otley CC, Carter RE, Sokumbi O, Hall MR, Bridges AG, Murphree DH. Deep learning for dermatologists: Part II. Current applications. J Am Acad Dermatol 2020:S0190-9622(20)30918-X. [PMID: 32428608 DOI: 10.1016/j.jaad.2020.05.053] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 Lee KJ, Janda M, Stark MS, Sturm RA, Soyer HP. On Naevi and Melanomas: Two Sides of the Same Coin? Front Med (Lausanne) 2021;8:635316. [PMID: 33681261 DOI: 10.3389/fmed.2021.635316] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
2 Martorell A, Martin-gorgojo A, Ríos-viñuela E, Rueda-carnero J, Alfageme F, Taberner R. Artificial intelligence in dermatology: A threat or an opportunity? Actas Dermo-Sifiliográficas (English Edition) 2021. [DOI: 10.1016/j.adengl.2021.11.007] [Reference Citation Analysis]
3 Traore A, Ata-ul-karim ST, Duan A, Soothar MK, Traore S, Zhao B. Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques. Remote Sensing 2021;13:4476. [DOI: 10.3390/rs13214476] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
4 Cheng CT, Wang Y, Chen HW, Hsiao PM, Yeh CN, Hsieh CH, Miao S, Xiao J, Liao CH, Lu L. A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nat Commun 2021;12:1066. [PMID: 33594071 DOI: 10.1038/s41467-021-21311-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
5 Quattrocchi E, Sominidi-Damodaran S, Murphree DH, Meves A. β3 integrin immunohistochemistry as a method to predict sentinel lymph node status in patients with primary cutaneous melanoma. Int J Dermatol 2020;59:1241-8. [PMID: 32772371 DOI: 10.1111/ijd.15125] [Reference Citation Analysis]
6 Puri P, Comfere N, Pittelkow MR, Bezalel SA, Murphree DH. COVID-19: An opportunity to build dermatology's digital future. Dermatol Ther 2020;33:e14149. [PMID: 32767453 DOI: 10.1111/dth.14149] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Puri P, Yiannias JA, Mangold AR, Swanson DL, Pittelkow MR. The policy dimensions, regulatory landscape, and market characteristics of teledermatology in the United States. JAAD Int 2020;1:202-7. [PMID: 34409341 DOI: 10.1016/j.jdin.2020.09.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
8 Malciu AM, Lupu M, Voiculescu VM. Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology. J Clin Med 2022;11:429. [PMID: 35054123 DOI: 10.3390/jcm11020429] [Reference Citation Analysis]
9 Chen SB, Novoa RA. Artificial intelligence for dermatopathology: Current trends and the road ahead. Seminars in Diagnostic Pathology 2022. [DOI: 10.1053/j.semdp.2022.01.003] [Reference Citation Analysis]