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Artificial Intelligence in Health                                       AI in AD – Diagnosis and monitoring



               of active scratching. PLoS One. 2013;8(12):e82389.     doi: 10.3389/fmed.2023.1278232
               doi: 10.1371/journal.pone.0082389               87.  Shorten C, Khoshgoftaar TM.  A  survey on image data
                                                                  augmentation for deep learning. J Big Data. 2019;6(1):60.
            77.  Mochizuki H, Papoiu AD, Nattkemper LA, et al. Scratching
               induces overactivity in motor-related regions and      doi: 10.1186/s40537-019-0197-0
               reward system in chronic itch patients. J Invest Dermatol.   88.  Yosinski J, Clune J, Bengio Y, Lipson H. How transferable
               2015;135(11):2814-2823.                            are features in deep neural networks? Adv Neural Inf Process
               doi: 10.1038/jid.2015.255                          Syst, 2014;27:3320-3328.
            78.  Ching  T,  Himmelstein  DS,  Beaulieu-Jones  BK,  et  al.   89.  Settles B.  Active Learning Literature Survey. Computer
               Opportunities and obstacles for deep learning in biology   Sciences Technical Report 1648, University of Wisconsin-
               and medicine. J R Soc Interface. 2018;15(141):20170387.  Madison; 2009. Available from: http://digital.library.wic.
                                                                  edu/1793/60660 [Last accessed on 2024 Jan 09].
               doi: 10.1098/rsif.2017.0387
                                                               90.  Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans
            79.  Winkler JK, Fink C, Toberer F, et al. Association between   Knowl Data Eng. 2010;22(10):1345-1359.
               surgical skin markings in dermoscopic images and
               diagnostic performance of a deep learning convolutional      doi: 10.1109/tkde.2009.191
               neural network for melanoma recognition. JAMA Dermatol.   91.  Fawcett T. An introduction to ROC analysis. Pattern
               2019;155(10):1135-1141.                            Recognit Lett. 2006;27(8):861-874.
               doi: 10.1001/jamadermatol.2019.1735                doi: 10.1016/j.patrec.2005.10.010
            80.  Haw WY, Al-Janabi A, Arents BW, et al. Global guidelines   92.  Srivastava N, Hinton G, Krizhevsky A, Sutskever  I,
               in dermatology mapping project (GUIDEMAP): A scoping   Salakhutdinov R. Dropout:  A  simple way to prevent
               review of dermatology clinical practice guidelines.  Br J   neural  networks  from overfitting.  J  Mach Learn Res.
               Dermatol. 2021;185(4):736-744.                     2014;15(1):1929-1958.
               doi: 10.1111/bjd.20428                          93.  Polesie S, Gillstedt M, Kittler H,  et al. Attitudes towards
            81.  Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi  A.   artificial intelligence within dermatology: An international
               Artificial intelligence in digital pathology-new tools for   online survey. Br J Dermatol. 2020;183(1):159-161.
               diagnosis and precision oncology. Nat Rev Clin Oncol.      doi: 10.1111/bjd.18875
               2019;16(11):703-715.
                                                               94.  Polesie  S, McKee PH, Gardner JM,  et al. Attitudes
               doi: 10.1038/s41571-019-0252-y                     toward artificial intelligence within dermatopathology:
            82.  Niazi MK, Parwani AV, Gurcan MN. Digital pathology and   An  international  online survey.  Front Med  (Lausanne).
               artificial intelligence. Lancet Oncol. 2019;20(5):e253-e261.  2020;7:591952.
               doi: 10.1016/s1470-2045(19)30154-8                 doi: 10.3389/fmed.2020.591952
            83.  Steele L, Velazquez‐Pimentel D, Thomas B. Do AI models   95.  Nelson CA, Pérez-Chada LM, Creadore A,  et al. Patient
               recognise rare, aggressive skin cancers? An assessment of a   perspectives on the use of artificial intelligence for skin
               direct-to-consumer application in the diagnosis of Merkel   cancer screening:  A  qualitative study.  JAMA Dermatol.
               cell carcinoma and amelanotic melanoma.  J  Eur Acad   2020;156(5):501-512.
               Dermatol Venereol. 2021;35(12):e877-e879.          doi: 10.1001/jamadermatol.2019.5014
               doi: 10.1111/jdv.17517                          96.  Koguchi-Yoshioka H, Watanabe R, Fujisawa Y,  et al. Skin

            84.  Li Z, Koban KC, Schenck TL, Giunta RE, Li Q, Sun Y.   resident memory T-cell population is not constructed effectively
               Artificial intelligence in dermatology image analysis: Current   in systemic sclerosis. Br J Dermatol. 2019;180(1):219-220.
               developments and future trends. J Clin Med. 2022;11(22):6826.     doi: 10.1111/bjd.17100
               doi: 10.3390/jcm11226826                        97.  Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset,
            85.  Kleinberg G, Diaz MJ, Batchu S, Lucke-Wold B. Racial   a large collection of multi-source dermatoscopic images of
               underrepresentation  in  dermatological  datasets  leads  to   common pigmented skin lesions. Sci Data. 2018;5:180161.
               biased machine learning models and inequitable healthcare.      doi: 10.1038/sdata.2018.161
               J Biomed Res. 2022;3(1):42-47.
                                                               98.  ISIC-International Skin Imaging Collaboration; 2018.
               doi: 10.46439/biomedres.3.025                      Available  from:    https://www.isic-archive.com/#!/
                                                                  topwithheader/widecontenttop/main  [Last  accessed  on
            86.  Omiye JA, Gui H, Daneshjou R, Cai ZR, Muralidharan, V.
               Principles, applications, and future of artificial intelligence   2024 Jan 09].
               in dermatology. Front Med (Lausanne). 2023;10:1278232.  99.  Mendonça T, Ferreira PM, Marques JS, Marcal AR, Rozeira J.


            Volume 1 Issue 2 (2024)                         64                               doi: 10.36922/aih.2775
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