A Systematic Review of Algorithmic Justice Techniques for Radiological Diagnosis: Advances, Challenges, and Future Perspectives
Abstract
Algorithmic fairness has gained prominence in the area of radiographic diagnostics, where artificial intelligence (AI) algorithms are applied to assist doctors in interpreting and diagnosing medical images. This systematic literature review addresses the current state of algorithmic fairness research in this context, investigating which techniques are on the rise associated with the use of AI algorithms for radiological diagnosis.References
Chen, R.J. et al. (2023). Algorithmic fairness in artificial intelligence for medicine and healthcare. In Nature Biomedical Engineering, 7(6), 719–742. DOI: 10.1038/s41551-023-01056-8
Correa, R., Shaan, M., Trivedi, H. (2022) A systematic review of ‘fair’ AI model development for image classification and prediction. In J. Med. Biol. Eng. 42, 816–827. DOI: 10.1007/s40846-022-00754-z
Dolata, M., Feuerriegel, S., e Schwabe, G. (2022). A sociotechnical view of algorithmic fairness. In Information Systems Journal, 32(4), 754–818. DOI: 10.1111/isj.12370
Kitchenham, B. (2004). Procedures for performing systematic reviews. In, Keele Univ.. 33.
Lin, M. et al. (2023). Improving model fairness in image-based computer-aided diagnosis. In Nat Commun 14, 6261. DOI: 10.1038/s41467-023-41974-4
Macht, B. (2022). Considering the potential impact of data bias on AI/ML and the Medical Device Ecosystem. In Biomedical Instrumentation & Technology, p. 127-129.
Makkar, A., Santosh, K. (2023). SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays. In Int. J. Mach. Learn. & Cyber. 14, 2659–2670. DOI: 10.1007/s13042-023-01789-7
Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. In European radiology experimental, 2(1), 35. DOI: 10.1186/s41747-018-0061-6
Ricci Lara, M.A., Echeveste, R. e Ferrante, E. (2022). Addressing fairness in artificial intelligence for medical imaging. In Nat Commun 13, 4581. DOI: 10.1038/s41467-022-32186-3
Busby, L. P., Courtier, J. L., e Glastonbury, C. M. (2018). Bias in Radiology: The How and Why of Misses and Misinterpretations. Radiographics : a review publication of the Radiological Society of North America, Inc, 38(1), 236–247. DOI: 10.1148/rg.2018170107
Correa, R., Shaan, M., Trivedi, H. (2022) A systematic review of ‘fair’ AI model development for image classification and prediction. In J. Med. Biol. Eng. 42, 816–827. DOI: 10.1007/s40846-022-00754-z
Dolata, M., Feuerriegel, S., e Schwabe, G. (2022). A sociotechnical view of algorithmic fairness. In Information Systems Journal, 32(4), 754–818. DOI: 10.1111/isj.12370
Kitchenham, B. (2004). Procedures for performing systematic reviews. In, Keele Univ.. 33.
Lin, M. et al. (2023). Improving model fairness in image-based computer-aided diagnosis. In Nat Commun 14, 6261. DOI: 10.1038/s41467-023-41974-4
Macht, B. (2022). Considering the potential impact of data bias on AI/ML and the Medical Device Ecosystem. In Biomedical Instrumentation & Technology, p. 127-129.
Makkar, A., Santosh, K. (2023). SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays. In Int. J. Mach. Learn. & Cyber. 14, 2659–2670. DOI: 10.1007/s13042-023-01789-7
Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. In European radiology experimental, 2(1), 35. DOI: 10.1186/s41747-018-0061-6
Ricci Lara, M.A., Echeveste, R. e Ferrante, E. (2022). Addressing fairness in artificial intelligence for medical imaging. In Nat Commun 13, 4581. DOI: 10.1038/s41467-022-32186-3
Busby, L. P., Courtier, J. L., e Glastonbury, C. M. (2018). Bias in Radiology: The How and Why of Misses and Misinterpretations. Radiographics : a review publication of the Radiological Society of North America, Inc, 38(1), 236–247. DOI: 10.1148/rg.2018170107
Published
2024-06-25
How to Cite
LIMA, Lucas Freire de; LIMA, Luiz Fernando F. P. de; RIQUELME, Maristela de Freitas; RICARTE, Danielle Rousy Dias.
A Systematic Review of Algorithmic Justice Techniques for Radiological Diagnosis: Advances, Challenges, and Future Perspectives. In: UNDERGRADUATE RESEARCH WORKS CONTEST - BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTHCARE (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 37-42.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2024.2771.
