Page 70 - AIH-1-2
P. 70
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

