The development and use of artificial intelligence (AI) in dermatology: a narrative review
Abstract
Artificial intelligence (AI) is defined as a computer science involving program development aiming to reproduce human cognition to analyze complex data. Artificial intelligence has rapidly developed in the medical field. In dermatology, its development is relatively new and is generally used in the diagnostic, especially for skin imaging analysis and classification, and also for risk assessment. The greatest advances have been primarily in the diagnosis of melanoma, followed by the assessment of psoriasis, ulcers, and various other skin diseases. The use of AI has shown good accuracy and is comparable to dermatologists in various studies, especially related to melanoma and skin tumors. However, several obstacles exist in the application of AI to daily clinical practice, including generalizability, image standardization, the need for large data quantities, and legal and privacy aspects. In current developments, AI should be aimed at helping enhance the decision-making of clinicians.
References
Rowe M. An introduction to machine learning for clinicians. Acad Med. 2019; 94(10):1433-6.
https://doi.org/10.1097/ACM.0000000000002792
De A, Sarda A, Gupta S, Das S. Use of artificial intelligence in dermatology. Indian J Dermatol 2020; 65(5):352-7.
https://doi.org/10.4103/ijd.IJD_418_20
Romero-Brufau S, Wyatt KD, Boyum P, Mickelson M, Moore M, Cognetta-Rieke C. What’s in a name? A comparison of attitudes towards artificial intelligence (AI) versus augmented human intelligence (AHI). BMC Med Inform Decis Mak 2020; 20(1):167.
https://doi.org/10.1186/s12911-020-01158-2
Abraham A, Sobhanakumari K, Mohan A. Artificial intelligence in dermatology. J Ski Sex Transm Dis 2021; 3:99-102.
https://doi.org/10.25259/JSSTD_49_2020
Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019; 28(2):73-81.
https://doi.org/10.1080/13645706.2019.1575882
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020; 92(4):807-12.
https://doi.org/10.1016/j.gie.2020.06.040
Hogarty DT, Su JC, Phan K, Attia M, Hossny M, Nahavandi S, et al. Artificial intelligence in dermatology—where we are and the way to the future: a review. Am J Clin Dermatol 2020; 21(1):41-7.
https://doi.org/10.1007/s40257-019-00462-6
Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: challenges and opportunities. J Pathol Inform 2018; 9:38.
https://doi.org/10.4103/jpi.jpi_53_18
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Peter Campbell J. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 2020; 9(2):14.
https://doi.org/10.1167/tvst.9.2.14
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69:S36-40.
https://doi.org/10.1016/j.metabol.2017.01.011
Fujita H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 2020; 13(1):6-19.
https://doi.org/0.1007/s12194-019-00552-4
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med 2019; 25(1):24-9.
https://doi.org/10.1038/s41591-018-0316-z
Yamashita R, Nishio M, Gian Do RK, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018; 9(4):611-29.
https://doi.org/ 10.1007/s13244-018-0639-9
Yu K, Syed MN, Bernardis E, Gelfand JM. Machine learning applications in the evaluation and management of psoriasis: a systematic review. J Psoriasis Psoriatic Arthritis 2020; 5(4):147-59.
https://doi.org/10.1177/2475530320950267
Kassem MA, Hosny KM, Damaševičius R, Eltoukhy MM. Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review. Diagnostics 2021; 11(8):1390.
https://doi.org/10.3390/diagnostics11081390
Glaister J. Automatic segmentation of skin lesions from dermatological photographs. 2013. p.4-54.
Oliveira RB, Filho ME, Ma Z, Papa JP, Pereira AS, Tavares JMRS. Computational methods for the image segmentation of pigmented skin lesions: a review. Comput Methods Programs Biomed 2016; 131:127-41.
https://doi.org/10.1016/j.cmpb.2016.03.032
Sumithra R, Suhil M, Guru DS. Segmentation and classification of skin lesions for disease diagnosis. Procedia Comput Sci 2015; 45:76-85.
https://doi.org/10.1016/j.procs.2015.03.090
Majumder S, Ullah MA. Feature extraction from dermoscopy images for melanoma diagnosis. SN Appl Sci 2019; 1:753.
Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 2019; 19(1):281.
https://doi.org/10.1186/s12911-019-1004-8
Zhu CY, Wang YK, Chen HP, Gao KL, Shu C, Wang JC, et al. A deep learning based framework for diagnosing multiple skin diseases in a clinical environment. Front Med 2021; 8:626369.
https://doi.org/10.3389/fmed.2021.626369
Goldenberg G. Optimal treatment of actinic keratosis. Clin Interv Aging 2014; 9:15-6.
https://doi.org/10.2147/CIA.S54426
Thomsen K, Iversen L, Titlestad TL, Winther O. Systematic review of machine learning for diagnosis and prognosis in dermatology. J Dermatolog Treat 2020; 31(5):496-510.
https://doi.org/10.1080/09546634.2019.1682500
Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SMR, Jafari MH, Ward K, et al. Melanoma detection by analysis of clinical images using convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2016:1373-6.
https://doi.org/10.1109/EMBC.2016.7590963
Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, et al. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol 2019; 180(2):373-81.
https://doi.org/10.1111/bjd.16924
Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Invest Dermatol 2018; 138(7):1529-38.
https://doi.org/10.1016/j.jid.2018.01.028
Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer 2019; 119:11-7.
https://doi.org/10.1016/j.ejca.2019.05.023
Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018; 29(8):1836-42.
https://doi.org/10.1093/annonc/mdy166
Gustafson E, Pacheco J, Wehbe F, Silverberg J, Thompson W. A machine learning algorithm for identifying atopic dermatitis in adults from electronic health records. IEEE Int Conf Heal Inform 2017; 2017:83-90.
https://doi.org/10.1109/ICHI.2017.31
Shrivastava VK, Londhe ND, Sonawane RS, Suri JS. A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification. Comput Methods Programs Biomed 2017; 150:9-22.
https://doi.org/10.1016/j.cmpb.2017.07.011
Zhao S, Xie B, Li Y, Zhao X, Kuang Y, Su J, et al. Smart identification of psoriasis by images using convolutional neural networks: a case study in China. J Eur Acad Dermatol Venereol 2020; 34(3):518-24.
https://doi.org/10.1111/jdv.15965
George Y, Aldeen M, Garnavi R. Psoriasis image representation using patch-based dictionary learning for erythema severity scoring. Comput Med Imaging Graph 2018; 66:44-55.
https://doi.org/10.1016/j.compmedimag.2018.02.004
George Y, Aldeen M, Garnavi R. Automatic scale severity assessment method in psoriasis skin images using local descriptors. IEEE J Biomed Heal Inform 2020; 24(2):577-85.
https://doi.org/10.1109/JBHI.2019.2910883
Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med 2020; 26(6):900-8.
https://doi.org/10.1038/s41591-020-0842-3
Wang L, Pedersen PC, Agu E, Strong DiM, Tulu B. Area determination of diabetic foot ulcer images using a cascaded two-stage SVM-based classification. IEEE Trans Biomed Eng 2017; 64(9):2098-109.
https://doi.org/10.1109/TBME.2016.2632522
Mukherjee R, Manohar DD, Das DK, Achar A, Mitra A, Chakraborty C. Automated tissue classification framework for reproducible chronic wound assessment. Biomed Res Int 2014; 2014:851582.
https://doi.org/10.1155/2014/851582
Dhane DM, Maiti M, Mungle T, Bar C, Achar A, Kolekar M, et al. Fuzzy spectral clustering for automated delineation of chronic wound region using digital images. Comput Biol Med 2017; 89:551-60.
https://doi.org/10.1016/j.compbiomed.2017.04.004
Alderden J, Pepper GA, Wilson A, Whitney JD, Richardson S, Butcher R, et al. Predicting pressure injury in critical care patients: a machine learning model. Am J Crit Care 2018; 27(6):461-8.
https://doi.org/10.4037/ajcc2018525
Hekler A, Utikal JS, Enk AH, Berking C, Klode J, Schadendorf D, et al. Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur J Cancer 2019; 115:79-83.
https://doi.org/10.1016/j.ejca.2019.04.021
Lodha S, Saggar S, Celebi JT, Silvers DN. Discordance in the histopathologic diagnosis of difficult melanocytic neoplasms in the clinical setting. J Cutan Pathol 2008; 35(4):349-52.
https://doi.org/10.1111/j.1600-0560.2007.00970.x
Lim SS, Ohn J, Mun JH. Diagnosis of onychomycosis: from conventional techniques and dermoscopy to artificial intelligence. Front Med 2021; 8:637216.
https://doi.org/10.3389/fmed.2021.637216
Veredas FJ, Luque-Baena RM, Martín-Santos FJ, Morilla-Herrera JC, Morente L. Wound image evaluation with machine learning. Neurocomputing 2015; 164:112-22.
https://doi.org/10.1016/j.neucom.2014.12.091
Zang Q, Paris M, Lehmann DM, Bell S, Kleinstreuer N, Allen D, et al. Prediction of skin sensitization potency using machine learning approaches. J Appl Toxicol 2017; 37(7):792-805.
https://doi.org/10.1002/jat.3424
Elder A, Ring C, Heitmiller K, Gabriel Z, Saedi N. The role of artificial intelligence in cosmetic dermatology-Current, upcoming, and future trends. J Cosmet Dermatol 2021; 20(1):48-52.
https://doi.org/10.1111/jocd.13797
Leem S, Kim SJ, Kim Y, Shin JG, Song HJ, Lee SG, et al. Comparative analysis of skin characteristics evaluation by a dermatologist and the Janus-III measurement system. Ski Res Technol 2021; 27(1):86-92.
https://doi.org/10.1111/srt.12915
Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J. How big data and artificial intelligence can help better manage the Covid-19 pandemic. Int J Environ Res Public Health 2020; 17(9):3175.
https://doi.org/10.3390/ijerph17093176
Bragazzi NL, Damiani G, Mariano M. From Rheumatology 1.0 to Rheumatology 4.0 and beyond: the contributions of big data to the field of rheumatology. Mediterr J Rheumatol 2019; 30(1):3-6.
https://doi.org/ 10.31138/mjr.30.1.3
Huang JA, Hartanti IR, Colin MN, Pitaloka DA. Telemedicine and artificial intelligence to support self-isolation of Covid-19 patients: Recent updates and challenges. Digit Health 2022; 8:20552076221100634.
https://doi.org/10.1177/20552076221100634
Majidian M, Tejani I, Jarmain T, Kellett L, Moy R. Artificial intelligence in the evaluation of telemedicine dermatology patients. J Drugs Dermatol 2022; 21(2):191-4.
https://doi.org/ 10.36849/jdd.6277
Du-Harpur X, Watt FM, Luscombe NM, Lynch MD. What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol 2020; 183(3):423-30.
https://doi.org/10.1111/bjd.18880
Li CX, Shen CB, Xue K, Shen X, Jing Y, Wang ZY, et al. Artificial intelligence in dermatology: past, present, and future. Chin Med J (Engl) 2019; 132(17):2017-20.
https://doi.org/10.1097/CM9.0000000000000372
Navarrete-Dechent C, Dusza SW, Liopyris K, Marghoob AA, Halpern AC, Marchetti MA. Automated dermatological diagnosis: hype or reality? J Invest Dermatol 2018; 138(10):2277-9.
https://doi.org/10.1016/j.jid.2018.04.040
De Guzman LC, Maglaque RPC, Torres VMB, Zapido SPA, Cordel MO. Design and evaluation of a multi-model, multi-level artificial neural network for eczema skin lesion detection. In: Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation. IEEE; 2015. p. 42-7.
https://doi.org/10.1109/AIMS.2015.17
Eapen BR. Artificial intelligence in dermatology: a practical introduction to a paradigm shift. Indian Dermatol Online J 2020; 11(6):881-9.
https://doi.org/10.4103/idoj.IDOJ_388_20
Pai VV, Pai RB. Artificial intelligence in dermatology and healthcare: an overview. Indian J Dermatol Venereol Leprol 2021; 87(4):457-67.