Challenging Recent Developments in Dermatology: Considering Artificial Intelligence and Medical Ethics

Main Article Content

Kadircan H. Keskinbora, MD, PhD Eda Kumbasar, MD

Abstract

Dermatology has gradually changed over the past few decades with the advances of Artificial Intelligence (AI) in the field of medicine. However, the application of Artificial intelligence in clinical practice remains a challenge.


When using Artificial intelligence in the dermatology practice, it is extremely important to consider not only the diagnosis and treatment of patients but also medical ethics details. This article reveals that there may be ethical difficulties when using Artificial intelligence in the field of dermatology and addresses the challenges related to ethical problems that Artificial intelligence may have caused in dermatological practices in recent years.  Artificial intelligence in medicine and dermatology causes challenges related to ethics and transparency. There are ethical problems, risks, and potential harms associated with the unexamined use of Artificial intelligence and machine learning when applied to health information and services.


Studies have shown that Artificial intelligence can diagnose skin lesions using clinical and dermoscopic images with accuracy that is on par with or better than dermatologists. As a result, Artificial intelligence is becoming a more significant tool in dermatology. 


Advances in artificial technology improve diagnostic precision and enable early illness assessment.  The accuracy rate of artificial intelligence systems used in skin cancer diagnosis is almost the same as dermatologists.


However, there is still a dearth of clinical validation in the actual world. In this article, deep learning applications in dermatology are examined, which is the state-of-the-art, artificial intelligence technology for image analysis. We also assess the technology's present limitations, possible points of failure, difficulties with performance measurement, interpretability, and ethical issues. It is crucial to take into account medical ethics in addition to patient diagnosis and treatment when implementing Artificial intelligence in dermatology practices. This article discusses the ethical issues that Artificial intelligence may have raised in dermatological practices in the recent past and indicates that there might be moral dilemmas when applying Artificial Intelligence in this field. 

Keywords: Dermatology, artificial intelligence, medical ethics, intelligent diagnosis, clinical decision making

Article Details

How to Cite
KESKINBORA, Kadircan H.; KUMBASAR, Eda. Challenging Recent Developments in Dermatology: Considering Artificial Intelligence and Medical Ethics. Medical Research Archives, [S.l.], v. 12, n. 6, june 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5483>. Date accessed: 22 july 2024. doi: https://doi.org/10.18103/mra.v12i6.5483.
Section
Research Articles

References

1. Wang G, Meng X, Zhang F. Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis. Medicine (Baltimore). 2023;102(45):e35993. Doi:10.1097/MD.0000000000035993
2. Zhang J, Zhong F, He K, Ji M, Li S, Li C. Recent advancements and perspectives in the diagnosis of skin diseases using machine learning and deep learning: A Review. Diagnostics 2023;13(23):3506. https://doi.org/10.3390/diagnostics13233506
3. Rosado B, Menzies S, Harbauer A, Pehamberger H, Wolff K, Binder M, et al. Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. Arch Dermatol. 2003;139(3):361-366. Doi:10.1001/archderm.139.3.361
4. Zortea M, Schopf TR, Thon K, Geilhufe M, Hindberg K, Kirchesch H, et al. Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists. Artif Intell Med. 2014;60(1):13-26. Doi:10.1016/j.artmed.2013.11.006
5. 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):1529-1538. Doi:10.1016/j.jid.2018.01.028
6. 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):2277-2279. Doi:10.1016/j.jid.2018.04.040
7. Jeong HK, Park C, Henao R, Kheterpal M. Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations. JID Innovations 2023;3:100150. https://doi.org/10.1016/j.xjidi.2022.100150
8. 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 Healthc Inform. 2017;2017:83-90. Doi:10.1109/ICHI.2017.31
9. Bibi I, Schaffert D, Blauth M, et al. Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study. J Med Internet Res. 2023;25:e50886. Published Nov. 28, 2023. Doi:10.2196/50886
10. 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):518-524. Doi:10.1111/jdv.15965
11. Han SS, Park GH, Lim W, Kim MS, Na JI, Park I, et al. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PLoS One. 2018;13(1):e0191493. Published Jan. 19, 2018. Doi:10.1371/journal.pone.0191493
12. Noyan MA, Durdu M, Eskiocak AH. TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases. Sci Rep 2020;10(1), 18314. https://doi.org/10.1038/s41598-020-75546-z
13. Alsaade FW, Aldhyani THH, Al-Adhaileh MH. Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms. Comput Math Methods Med. 2021:9998379. Published May 15, 2021. Doi:10.1155/2021/9998379
14. Botsalı A, Yürekli A. Melanomda yapay zekâ uygulamaları [Artificial intelligence applications in melanoma]. Türsen Ü, ed. Dermatolojide Yapay Zekâ. 1. Ed. Ankara: Türkiye Klinikleri; 2022. p.34-38.
15. Yildiz O. Melanoma detection from dermoscopy images with deep learning methods: a comprehensive study. Journal of the Faculty of Engineering and Architecture of Gazi University, 2019;34(4):2241-2260. Doi:10.17341/gazimmfd.435217
16. Malvehy J, Hauschild A, Curiel-Lewandrowski C, et al. Clinical performance of the Nevisense system in cutaneous melanoma detection: an international, multicentre, prospective and blinded clinical trial on efficacy and safety. Br J Dermatol. 2014;171(5):1099-1107. Doi:10.1111/bjd.13121
17. Seité S, Khammari A, Benzaquen M, Moyal D, Dréno B. Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs. Exp Dermatol. 2019;28(11):1252-1257. Doi:10.1111/exd.14022
18. Wheeler DR. Art, Artificial Intelligence, and Aesthetics in Plastic Surgery. Plast Reconstr Surg. 2021;148(3):529e-530e. Doi:10.1097/PRS.0000000000008289
19. Rat C, Hild S, Rault Sérandour J, Gaultier A, Quereux G, Dreno B, et al. Use of Smartphones for Early Detection of Melanoma: Systematic Review. J Med Internet Res. 2018;20(4):e135. Published Apr 13, 2018. Doi:10.2196/jmir.9392
20. Türk Dermatoloji Derneği, Teletıp hakkında hukuksal değerlendirme [Turkish Dermatology Association, Legal evaluation on Telemedicine]. http://turkdermatoloji.org.tr/haber_arsivi/detay/524, https://turkdermatoloji.org.tr/media/files/file/TELE_TIP_GORUS_GENEL.pdf Accessed on March 22, 2024
21. Keskinbora KH, Kumbasar E. Ethical Concerns in Dermatology and Cosmetic Applications. Medical Research Archives 2023;11(2), published on Feb. 28, 2023. https://doi.org/10.18103/mra.v11i2.3667
22. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks [published correction appears in Nature. 2017 Jun 28; 546(7660):686]. Nature. 2017;542(7639):115-118. Doi:10.1038/nature21056
23. Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero JM, Alfageme F, Taberner R. Artificial Intelligence in Dermatology: A Threat or an Opportunity? Actas Dermosifiliogr 2022;113(1):30-46. Doi: 10.1016/j.ad.2021.07.003
24. Keskinbora KH. Medical ethics considerations on artificial intelligence. J Clin Neurosci. 2019;64:277-282. Doi:10.1016/j.jocn.2019.03.001
25. Jonsen A. Do no harm. In: Beauchamp T, Childress J, ed.s. Principles of biomedical ethics. Oxford: Oxford University Press; 1989
26. Grant-Kels JM. Ethical issues in dermatology: part I. Clin Dermatol. 2012;30(2):149-150. Doi:10.1016/j.clindermatol.2011.06.026
27. Gordon ER, Trager MH, Kontos D, Weng C, Geskin LJ, Dugdale LS, et al. Ethical considerations for artificial intelligence in dermatology: a scoping review. Br J Dermatol. 2024 Feb 8:ljae040. Doi: 10.1093/bjd/ljae040
28. Young AT, Xiong M, Pfau J, Keiser MJ, Wei ML. Artificial Intelligence in Dermatology: A Primer. J Invest Dermatol. 2020;140(8):1504-1512. Doi:10.1016/j.jid.2020.02.026
29. Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491-497. Doi:10.1093/jamia/ocz192
30. Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatol Ther (Heidelb). 2022;12(12):2637-2651. Doi:10.1007/s13555-022-00833-8
31. Jobson D, Mar V, Freckelton I. Legal and ethical considerations of artificial intelligence in skin cancer diagnosis. Australas J Dermatol. 2022;63(1):e1-e5. Doi:10.1111/ajd.13690
32. Birch J, Creel KA, Jha AK, Plutynski A. Clinical decisions using AI must consider patient values. Nat Med. 2022;28(2):229-232. Doi:10.1038/s41591-021-01624-y
33. Hamid S. The opportunities and risks of artificial intelligence in medicine and healthcare; 2016. Available: http://www.cuspe.org/wp-content/uploads/2016/09/Hamid_2016.pdf Accessed on May 29, 2023.
34. Nagler RH, Franklin Fowler E, Gollust SE. Women's Awareness of and Responses to Messages About Breast Cancer Overdiagnosis and Overtreatment: Results From a 2016 National Survey. Med Care. 2017;55(10):879-885. Doi:10.1097/MLR.0000000000000798
35. Bosworth T. As AI Makes Its Way into Clinical Dermatology, Consider Its Limitations and Problems. https://www.medscape.com/viewarticle/ai-makes-its-way-clinical-dermatology-consider-its-2024a10005aj accessed on April 15, 2024.
36. Madigan LM, Fox LP. Where are we now with inpatient consultative dermatology?: Assessing the value and evolution of this subspecialty over the past decade. J Am Acad Dermatol. 2019;80(6):1804-1808. Doi:10.1016/j.jaad.2019.01.031
37. Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med. 2023;165:107413. Doi:10.1016/j.compbiomed.2023.107413
38. Nogaroli R, Faleiros Júnior JL. Ethical Challenges of Artificial Intelligence in Medicine and the Triple Semantic Dimensions of Algorithmic Opacity with Its Repercussions to Patient Consent and Medical Liability. In: Sousa Antunes, H., Freitas, P.M., Oliveira, A.L., Martins Pereira, C., Vaz de Sequeira, E., Barreto Xavier, L. (eds) Multidisciplinary Perspectives on Artificial Intelligence and the Law. Law, Governance and Technology Series, vol 58. Springer, Cham., 2024, pp.229-248. https://doi.org/10.1007/978-3-031-41264-6_12
39. 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):2017-2020. Doi:10.1097/CM9.0000000000000372
40. Barata C, Rotemberg V, Codella NCF, Tschandl P, Rinner C, Akay BN, et al. A reinforcement learning model for AI-based decision support in skin cancer. Nat Med. 2023;29(8):1941-1946. Doi:10.1038/s41591-023-02475-5