THE ANALYSIS STUDY OF ARTIFICIAL INTELIGENCE FOR SKIN CANCER: A COMPREHENSIVE SYSTEMATIC REVIEW

Authors

  • Tia Alviani Juwita Faculty of Medicine, HKBP Nommensen University, Medan, Indonesia Author
  • Indah Sari Siregar Faculty of Medicine, North Sumatera University, Medan, Indonesia Author

DOI:

https://doi.org/10.61841/nrx3cg60

Keywords:

Artificial intelligence, diagnostic, skin cancer.

Abstract

Background: Skin cancer diagnosis relies heavily on the interpretation of visual patterns, making it a complex task that requires extensive training in dermatology and dermatoscopy. However, AI algorithms have been shown to accurately diagnose skin cancers, even outperforming experienced dermatologists in image classification tasks in constrained settings.

The aim: The aim of this study to show about artificial intelligence for skin cancer.

Methods: By the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. This search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed, SagePub, and Science Direct were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done.

Result: Eight publications were found to be directly related to our ongoing systematic examination after a rigorous three-level screening approach. Subsequently, a comprehensive analysis of the complete text was conducted, and additional scrutiny was given to these articles.

Conclusion: The use of AI has high potential to facilitate the way skin cancer is diagnosed. Two main branches of AI are used to detect and classify skin cancer, namely shallow and deep techniques.

References

Wei ML, Tada M, So A, Torres R. Artificial intelligence and skin cancer. Front Med. 2024;11(March):10–4.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

Sanchez K, Kamal K, Manjaly P, Ly S, Mostaghimi A. Clinical Application of Artificial Intelligence for Nonmelanoma Skin Cancer. Curr Treat Options Oncol [Internet]. 2023;24(4):373–9. Available from: https://doi.org/10.1007/s11864-023-01065-4

Melarkode N, Srinivasan K, Qaisar SM, Plawiak P. AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions. Cancers (Basel). 2023;15(4).

Krakowski I, Kim J, Cai ZR, Daneshjou R, Lapins J, Eriksson H, et al. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. npj Digit Med. 2024;7(1):1–10.

Takiddin A, Schneider J, Yang Y, Abd-Alrazaq A, Househ M. Artificial intelligence for skin cancer detection: Scoping review. J Med Internet Res. 2021;23(11):1–25.

Das K, Cockerell CJ, Patil A, Pietkiewicz P, Giulini M, Grabbe S, et al. Machine learning and its application in skin cancer. Int J Environ Res Public Health. 2021;18(24).

Phillips M, Greenhalgh J, Marsden H, Palamaras I. Detection of malignant melanoma using artificial intelligence: An observational study of diagnostic accuracy. Dermatology Pract Concept. 2020;10(1):1–13.

Patel RH, Foltz EA, Witkowski A, Ludzik J. Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review. Cancers (Basel). 2023;15(19).

Liu Q, Zhang J, Bai Y. Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis. Front Oncol. 2023;13(October):1–14.

Jagemann I, Wensing O, Stegemann M, Hirschfeld G. Acceptance of Medical Artificial Intelligence in Skin Cancer Screening: Choice-Based Conjoint Survey. JMIR Form Res. 2024;8(1):1–10.

Marsden H, Morgan C, Austin S, DeGiovanni C, Venzi M, Kemos P, et al. Effectiveness of an image analyzing AI-based Digital Health Technology to identify Non-Melanoma Skin Cancer and other skin lesions: results of the DERM-003 study. Front Med. 2023;10(October):1–11.

Jutzi TB, Krieghoff-Henning EI, Holland-Letz T, Utikal JS, Hauschild A, Schadendorf D, et al. Artificial Intelligence in Skin Cancer Diagnostics: The Patients’ Perspective. Front Med. 2020;7(June).

Marchetti MA, Cowen EA, Kurtansky NR, Weber J, Dauscher M, DeFazio J, et al. Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study). npj Digit Med. 2023;6(1):1–11.

Willingham ML, Spencer SYPK, Lum CA, Navarro Sanchez JM, Burnett T, Shepherd J, et al. The potential of using artificial intelligence to improve skin cancer diagnoses in Hawai‘i’s multiethnic population. Melanoma

Res [Internet]. 2021 Dec 15;31(6):504–14. Available from: https://journals.lww.com/10.1097/CMR.0000000000000779

Sangers TE, Wakkee M, Moolenburgh FJ, Nijsten T, Lugtenberg M. Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners. Arch Dermatol Res [Internet]. 2023;315(5):1187–95. Available from: https://doi.org/10.1007/s00403-022-02492-3

Joly-Chevrier M, Nguyen AXL, Liang L, Lesko-Krleza M, Lefrançois P. The State of Artificial Intelligence in Skin Cancer Publications. J Cutan Med Surg. 2024;28(2):146–52.

Young AT, Xiong M, Pfau J, Keiser MJ, Wei ML. Artificial Intelligence in Dermatology: A Primer. J Invest Dermatol [Internet]. 2020;140(8):1504–12. Available from: https://doi.org/10.1016/j.jid.2020.02.026

Foltz EA, Witkowski A, Becker AL, Latour E, Lim JY, Hamilton A, et al. Artificial Intelligence Applied to NonInvasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review. Cancers (Basel). 2024;16(3).

Brancaccio G, Balato A, Malvehy J, Puig S, Argenziano G, Kittler H. Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check. J Invest Dermatol [Internet]. 2024;144(3):492–9. Available from: https://doi.org/10.1016/j.jid.2023.10.004

Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatol Ther (Heidelb) [Internet]. 2022;12(12):2637–51. Available from: https://doi.org/10.1007/s13555-022-00833-8

Furriel BCRS, Oliveira BD, Prôa R, Paiva JQ, Loureiro RM, Calixto WP, et al. Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review. Front Med. 2023;10.

Kuo KM, Talley PC, Chang CS. The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis. BMC Med Inform Decis Mak [Internet]. 2023;23(1):1–16. Available from: https://doi.org/10.1186/s12911-023-02229-w

Schreidah CM, Gordon ER, Adeuyan O, Chen C, Lapolla BA, Kent JA, et al. Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review. Front Med. 2024;11(April).

Venkatesh KP, Raza M, Kvedar J. AI-based skin cancer detection: the balance between access and overutilization. npj Digit Med. 2023;6(1).

Downloads

Published

2024-06-18

How to Cite

Juwita, T. A., & Siregar, I. S. (2024). THE ANALYSIS STUDY OF ARTIFICIAL INTELIGENCE FOR SKIN CANCER: A COMPREHENSIVE SYSTEMATIC REVIEW. Journal of Advanced Research in Medical and Health Science (ISSN 2208-2425), 10(6), 104-112. https://doi.org/10.61841/nrx3cg60