[en] The number of professional pathologists is not high in contrast with the total population, and, worldwide, the cases of cancer have a tendency to rise each year. In the face of this, the implementation of Artificial Intelligence (AI) and, more specifically, Explainable Artificial Intelligence (xAI) techniques could contribute to prevent a work overload on pathologists. Despite recent advances in this subject, AI/xAI systems are still not fully integrated in the histopathology workflow. This could be due to the fact that the implementation of AI/xAI models in histopathology is subject to technical, social and legal requirements, among others. It is necessary to determine these requirements in order to solve this issue. With the intention of providing a wider picture, this article will present a rapid literature review bringing together all current requirements and obstacles that the implementation of AI/xAI faces in histopathology.
Miguel, Juan Cristian; Universidad Tecnológica Nacional,Facultad Regional Buenos Aires,Buenos Aires,Argentina
GREVISSE, Christian ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) > Medical Education
Sardella, Antonia; University of Luxembourg,Faculty of Science, Technology and Medicine,Esch-sur-Alzette,Luxembourg
PolIo-Cattaneo, Maria F; Universidad Tecnológica Nacional,Facultad Regional Buenos Aires,Buenos Aires,Argentina
External co-authors :
yes
Language :
English
Title :
Requirements and Challenges to use Explainable Artificial Intelligence in Histopathology: A Rapid Review
Publication date :
22 August 2024
Event name :
2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)
Event place :
Orlando, United States - Florida
Event date :
03-06 June 2024
Audience :
International
Main work title :
2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)
Q. Ye, J. Xia, and G. Yang, "Explainable ai for covid-19 ct classifiers: An initial comparison study," in 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021, pp. 521-526. DOI: 10.1109/CBMS52027.2021.00103.
H. Verma, J. Mlynar, R. Schaer, et al., "Rethinking the role of ai with physicians in oncology: Revealing perspectives from clinical and research workflows," in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, ser. CHI '23, Hamburg, Germany: Association for Computing Machinery, 2023, ISBN: 9781450394215. DOI: 10.1145/3544548.3581506. [Online]. Available: https://doi.org/10.1145/3544548.3581506.
M. M. Abdelsamea, U. Zidan, Z. Senousy, M. M. Gaber, E. Rakha, and M. Ilyas, "A survey on artificial intelligence in histopathology image analysis," WIREs Data Mining and Knowledge Discovery, vol. 12, no. 6, e1474, 2022. DOI: https://doi.org/10.1002/widm.1474. eprint: https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1474. [Online]. Available: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1474.
F. Giuste, W. Shi, Y. Zhu, et al., "Explainable artificial intelligence methods in combating pandemics: A systematic review," IEEE Reviews in Biomedical Engineering, vol. 16, pp. 5-21, 2023. DOI: 10.1109/RBME.2022.3185953.
T. Zehra, A. Parwani, J. Abdul-Ghafar, and Z. Ahmad, "A suggested way forward for adoption of AI-Enabled digital pathology in low resource organizations in the developing world," en, Diagnostic Pathology, vol. 18, no. 1, p. 68, May 2023, ISSN: 1746-1596. DOI: 10.1186/s13000-023-01352-6. [Online]. Available: https://diagnosticpathology.biomedcentral.com/articles/10.1186/s13000-023-01352-6 (visited on 08/05/2023).
P.-r. Liu, L. Lu, J.-y. Zhang, T.-t. Huo, S.-x. Liu, and Z.-w. Ye, "Application of Artificial Intelligence in Medicine: An Overview," en, Current Medical Science, vol. 41, no. 6, pp. 1105-1115, Dec. 2021, ISSN: 2096-5230, 2523-899X. DOI: 10.1007/s11596-021-2474-3. [Online]. Available: https://link.springer.com/10.1007/s11596-021-2474-3 (visited on 08/05/2023).
M. A. Ahmad, A. Teredesai, and C. Eckert, "Interpretable machine learning in healthcare," in 2018 IEEE International Conference on Healthcare Informatics (ICHI), 2018, pp. 447-447. DOI: 10.1109/ICHI.2018.00095.
C. J. Cai, S. Winter, D. Steiner, L. Wilcox, and M. Terry, ""hello ai": Uncovering the onboarding needs of medical practitioners for human-ai collaborative decision-making," Proc. ACM Hum.-Comput. Interact., vol. 3, no. CSCW, Nov. 2019. DOI: 10.1145/3359206. [Online]. Available: https://doi.org/10.1145/3359206.
B. Cartaxo, G. Pinto, and S. Soares, "Rapid reviews in software engineering," in Contemporary Empirical Methods in Software Engineering, M. Felderer and G. H. Travassos, Eds. Cham: Springer International Publishing, 2020, pp. 357-384, ISBN: 978-3-030-32489-6. DOI: 10.1007/978-3-030-32489-613. [Online]. Available: https://doi.org/10.1007/978-3-030-32489-613.
A. Tricco, E. Lillie, W. Zarin, et al., "Prisma extension for scoping reviews (prisma-scr): Checklist and explanation," Annals of Internal Medicine, vol. 169, Sep. 2018. DOI: 10.7326/M18-0850.
J. O. O. Ayorinde, F. Citterio, M. Landrò, et al., "Artificial intelligence you can trust: What matters beyond performance when applying artificial intelligence to renal histopathology?" eng, Journal of the American Society of Nephrology: JASN, vol. 33, pp. 2133-2140, 12 Dec. 2022. DOI: 10.1681/ASN.2022010069.
K. A. Tran, O. Kondrashova, A. Bradley, E. D. Williams, J. V. Pearson, and N. Waddell, "Deep learning in cancer diagnosis, prognosis and treatment selection," en, Genome Medicine, vol. 13, no. 1, p. 152, Dec. 2021, ISSN: 1756-994X. DOI: 10.1186/s13073-021-00968-x. [Online]. Available: https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-021-00968-x (visited on 08/05/2023).
M. Mohammadi, J. Cooper, O. Arandelović, et al., "Weakly supervised learning and interpretability for endometrial whole slide image diagnosis.," eng, Experimental biology and medicine (Maywood, N.J.), vol. 247, pp. 2025-2037, 22 Nov. 2022. DOI: 10.1177/15353702221126560.
W. L. C. dos-Santos, L. A. R. De Freitas, A. A. Duarte, M. F. Angelo, and L. R. Oliveira, "Computational pathology, new horizons and challenges for anatomical pathology," en, Surgical and Experimental Pathology, vol. 5, no. 1, p. 10, Dec. 2022, ISSN: 2520-8454. DOI: 10.1186/s42047-022-00113-x. [Online]. Available: https://surgexppathol.biomedcentral.com/articles/10.1186/s42047-022-00113-x (visited on 08/05/2023).
W. Rösler, M. Altenbuchinger, B. Baeßler, et al., "An overview and a roadmap for artificial intelligence in hematology and oncology," en, Journal of Cancer Research and Clinical Oncology, vol. 149, no. 10, pp. 7997-8006, Aug. 2023, ISSN: 0171-5216, 1432-1335. DOI: 10.1007/s00432-023-04667-5. [Online]. Available: https://link.springer.com/10.1007/s00432-023-04667-5 (visited on 08/05/2023).
A. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Müller, "Causability and explainability of artificial intelligence in medicine.," eng, Wiley interdisciplinary reviews. Data mining and knowledge discovery, vol. 9, e1312, 4 Jul. 2019. DOI: 10.1002/widm.1312.
A. Shaban-Nejad, M. Michalowski, J. S. Brownstein, and D. L. Buckeridge, "Guest editorial explainable ai: Towards fairness, accountability, transparency and trust in healthcare," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 7, pp. 2374-2375, Jul. 2021, ISSN: 2168-2208. DOI: 10.1109/JBHI.2021.3088832.
A. B. Tosun, F. Pullara, M. J. Becich, D. L. Taylor, J. L. Fine, and S. C. Chennubhotla, "Explainable ai (xai) for anatomic pathology.," eng, Advances in anatomic pathology, vol. 27, pp. 241-250, 4 Jul. 2020. DOI: 10.1097/PAP.0000000000000264.
M. H. Jarrahi, V. Davoudi, and M. Haeri, "The key to an effective ai-powered digital pathology: Establishing a symbiotic workflow between pathologists and machine.," eng, Journal of pathology informatics, vol. 13, p. 100 156, 2022. DOI: 10.1016/j.jpi.2022.100156.
The Cancer Genome Atlas Program (TCGA). [Online]. Available: https://www.cancer.gov/ccg/research/genome-sequencing/tcga (visited on 02/28/2024).
Cancer Imaging Archive. [Online]. Available: https://www.cancerimagingarchive.net/browse-collections/ (visited on 02/28/2024).
Digital Pathology Association's Whole-Slide Imaging Repository. [Online]. Available: https://digitalpathologyassociation.org/digital-anatomic-pathology-academy (visited on 02/28/2024).
