AI in Dermatology: Bridging the Gap Between Potential and Practice
DOI:
https://doi.org/10.12775/JEHS.2024.52.004Keywords
artificial intelligence, dermatology, melanoma, skin, cancerAbstract
Artificial intelligence has been rapidly penetrating every element of our lives for quite some time. However, its presence in health care has remained elusive. This is particularly apparent in the field of dermatology, where, given the characteristics of this discipline of medicine, it would seems that its presence should be abundant. Malignant skin lesions are still high in the statistics in terms of cancer mortality while being one of the easiest to treat when diagnosed early. There are many reasons why artificial intelligence is not used in daily practice as an aid for cancers detection. However the most important one is the ongoing insufficient quality of the algorithms, which, despite great results in laboratory settings, do not produce good enough outcomes in clinical settings. Other important reasons are that people still distrust and fear artificial intelligence and simply the legal lack of adaptation of countries to its lawful and safe use. Despite the work of scientists and legislators the road to seeing artificial intelligence as a helping tool for dermatologists on a daily basis is still very long and requires the attention of scientists and the whole medical community.
References
Ertel W. Introduction to Artificial Intelligence. Springer; 2018. Accessed January 2, 2024. https://books.google.pl/books?hl=en&lr=&id=geFHDwAAQBAJ&oi=fnd&pg=PR5&dq=Artificial+intelligence&ots=3Fby7bgD3t&sig=8Z3EHLveMPaGzqmpDVvd-TwYndw&redir_esc=y#v=onepage&q=Artificial%20intelligence&f=false
Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review. 2020;53. doi:https://doi.org/10.1007/s10462-020-09825-6
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69(69):S36-S40. doi:https://doi.org/10.1016/j.metabol.2017.01.011
Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402. doi:https://doi.org/10.1001/jama.2016.17216
Brandt MG, Moore CC. Nonmelanoma Skin Cancer. Facial Plastic Surgery Clinics of North America. 2019;27(1):1-13. doi:https://doi.org/10.1016/j.fsc.2018.08.001
Dzwierzynski WW. Melanoma Risk Factors and Prevention. Clinics in Plastic Surgery. 2021;48(4):543-550. doi:https://doi.org/10.1016/j.cps.2021.05.001
Poland S, in Kraków SO. Health and health care in 2021. Warszawa, Kraków 2022. https://stat.gov.pl/obszary-tematyczne/zdrowie/zdrowie/zdrowie-i-ochrona-zdrowia-w-2021-roku,1,12.html
Cantisani C, Ambrosio L, Cucchi C, et al. Melanoma Detection by Non-Specialists: An Untapped Potential for Triage? Diagnostics. 2022;12(11):2821.
doi:https://doi.org/10.3390/diagnostics12112821
Han SS, Kim YJ, Moon IJ, et al. Evaluation of Artificial Intelligence–Assisted Diagnosis of Skin Neoplasms: A Single-Center, Paralleled, Unmasked, Randomized Controlled Trial. Journal of Investigative Dermatology. 2022;142(9):2353-2362.e2. doi:https://doi.org/10.1016/j.jid.2022.02.003
Fujisawa Y, Otomo Y, Ogata 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. British Journal of Dermatology. 2018;180(2):373-381. doi:https://doi.org/10.1111/bjd.16924
Phillips M, Greenhalgh J, Marsden H, Palamaras I. Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy. Dermatology Practical & Conceptual. Published online December 31, 2019:e2020011. doi:https://doi.org/10.5826/dpc.1001a11
Menzies SW, Sinz C, Menzies M, et al. Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial. The Lancet Digital Health. 2023;5(10):e679-e691. doi:https://doi.org/10.1016/S2589-7500(23)00130-9
Phillips M, Marsden H, Jaffe W, et al. Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions. JAMA Network Open. 2019;2(10):e1913436. doi:https://doi.org/10.1001/jamanetworkopen.2019.13436
Muñoz‐López C, Ramírez‐Cornejo C, Marchetti MA, et al. Performance of a deep neural network in teledermatology: a single‐centre prospective diagnostic study. Journal of the European Academy of Dermatology and Venereology. 2020;35(2):546-553. doi:https://doi.org/10.1111/jdv.16979
Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma. JAMA Dermatology. Published online June 19, 2019. doi:https://doi.org/10.1001/jamadermatol.2019.1375
Rajpara SM, Botello AP, Townend J, Ormerod AD. Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma. British Journal of Dermatology. 2009;161(3):591-604. doi:https://doi.org/10.1111/j.1365-2133.2009.09093.x
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digital Medicine. 2020;3(1):1-8. doi:https://doi.org/10.1038/s41746-020-00324-0
Premarket Approval (PMA). www.accessdata.fda.gov. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=p090012
Premarket Approval (PMA). www.accessdata.fda.gov. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P150046
Rudin C, Radin J. Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition. Harvard Data Science Review. 2019;1(2). doi:https://doi.org/10.1162/99608f92.5a8a3a3d
Rodrigues R. Legal and Human Rights Issues of AI: Gaps, Challenges and Vulnerabilities. Journal of Responsible Technology. 2020;4(100005):100005. doi:https://doi.org/10.1016/j.jrt.2020.100005
Liu Y, Primiero CA, Kulkarni V, Soyer HP, Betz-Stablein B. Artificial intelligence for the classification of pigmented skin lesions in populations with skin of colour: A systematic review. Dermatology. Published online March 21, 2023. doi:https://doi.org/10.1159/000530225
Jan-Oliver Kutza, Hannemann N, Hübner U, Babitsch B. The Representation of Trust in Artificial Intelligence Healthcare Research. Studies in health technology and informatics. Published online June 29, 2023. doi:https://doi.org/10.3233/shti230409
Gillespie N, Lockey S, Curtis C. Trust in artificial Intelligence: a five country study. Published online March 25, 2021. doi:https://doi.org/10.14264/e34bfa3
EU AI Act: first regulation on artificial intelligence | News | European Parliament. www.europarl.europa.eu. Published August 6, 2023. Accessed January 2, 2024. https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence/
Tai MCT. The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal. 2020;32(4):339-343. doi:https://doi.org/10.4103/tcmj.tcmj_71_20
Navarrete-Dechent C, Dusza SW, Liopyris K, Marghoob AA, Halpern AC, Marchetti MA. Automated Dermatological Diagnosis: Hype or Reality? Journal of Investigative Dermatology. 2018;138(10):2277-2279. doi:https://doi.org/10.1016/j.jid.2018.04.040
Automated Classification of Skin Lesions: From Pixels to Practice. Journal of Investigative Dermatology. 2018;138(10):2108-2110. doi:https://doi.org/10.1016/j.jid.2018.06.175
Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science. 2019;363(6433):1287-1289. doi:https://doi.org/10.1126/science.aaw4399
Navarrete-Dechent C, Liopyris K, Marchetti MA. Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement. Journal of Investigative Dermatology. Published online October 2020. doi:https://doi.org/10.1016/j.jid.2020.06.040
Combalia M, Codella N, Rotemberg V, et al. Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge. The Lancet Digital Health. 2022;4(5):e330-e339. doi:https://doi.org/10.1016/s2589-7500(22)00021-8
Gaudy-Marqueste C, Wazaefi Y, Bruneu Y, et al. Ugly Duckling Sign as a Major Factor of Efficiency in Melanoma Detection. JAMA Dermatology. 2017;153(4):279. doi:https://doi.org/10.1001/jamadermatol.2016.5500
Haggenmüller S, Maron RC, Hekler A, et al. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. European Journal of Cancer (Oxford, England: 1990). 2021;156:202-216. doi:https://doi.org/10.1016/j.ejca.2021.06.049
Podolec K, Brzewski P, Pirowska M, Wojas-Pelc A. Predictive value of global dermoscopic pattern in patients diagnosed with cutaneous melanoma. Advances in Dermatology and Allergology. 2021;38(4):572-577. doi:https://doi.org/10.5114/ada.2020.94593
Elder DE, Eguchi M, Barnhill RL, et al. Diagnostic error, uncertainty, and overdiagnosis in melanoma. Pathology. 2023;55(2):206-213. doi:https://doi.org/10.1016/j.pathol.2022.12.345
Kutzner H, Jutzi TB, Krahl D, et al. Overdiagnosis of melanoma – causes, consequences and solutions. JDDG: Journal der Deutschen Dermatologischen Gesellschaft. 2020;18(11):1236-1243. doi:https://doi.org/10.1111/ddg.14233
Znaor A. Melanoma burden, healthcare utilization and the potential for overdiagnosis in the elderly U.S. population. British Journal of Dermatology. 2017;177(3):625-625. doi:https://doi.org/10.1111/bjd.15759
Gordon C, Phillips M, Beresin EV. 3 - The Doctor–Patient Relationship. In: Stern TA, Fricchione GL, Cassem NH, Jellinek MS, Rosenbaum JF, eds. Massachusetts General Hospital Handbook of General Hospital Psychiatry (Sixth Edition). Sixth Edition. W.B. Saunders; 2010:15-23. doi:10.1016/B978-1-4377-1927-7.00003-0
Nasr-Esfahani E, Samavi S, Karimi N, et al. Melanoma detection by analysis of clinical images using convolutional neural network. IEEE Xplore. doi:https://doi.org/10.1109/EMBC.2016.7590963
Yang S, Shu C, Hu H, Ma G, Yang M. Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features. Computational and Mathematical Methods in Medicine. 2022;2022:9726181. doi:https://doi.org/10.1155/2022/9726181
Hekler A, Utikal JS, Enk AH, et al. Superior skin cancer classification by the combination of human and artificial intelligence. European Journal of Cancer. 2019;120:114-121. doi:https://doi.org/10.1016/j.ejca.2019.07.019
Tschandl P, Rinner C, Apalla Z, et al. Human–computer collaboration for skin cancer recognition. Nature Medicine. 2020;26(8):1229-1234. doi:https://doi.org/10.1038/s41591-020-0942-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Jakub Klarycki, Karolina Tomczyk, Dominika Podgórska, Miłosz Sanecki, Karolina Jurasz, Natalia Chojnacka, Ewa Rzeska, Radosław Cymer
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The periodical offers access to content in the Open Access system under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
Stats
Number of views and downloads: 330
Number of citations: 0