Uma Revisão Sistemática das Técnicas de Justiça Algorítmica para Diagnóstico Radiológico: Avanços, Desafios e Perspectivas Futuras
Resumo
A justiça algorítmica tem ganhado recente destaque na área de diagnóstico de radiografias, onde algoritmos de inteligência artificial (IA) são aplicados para auxiliar médicos na interpretação e diagnóstico de imagens médicas. Esta revisão sistemática da literatura aborda o estado atual da pesquisa em justiça algorítmica nesse contexto, investigando quais as técnicas em ascensão associadas ao uso de algoritmos de IA para diagnóstico radiológico.Referências
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
Publicado
25/06/2024
Como Citar
LIMA, Lucas Freire de; LIMA, Luiz Fernando F. P. de; RIQUELME, Maristela de Freitas; RICARTE, Danielle Rousy Dias.
Uma Revisão Sistemática das Técnicas de Justiça Algorítmica para Diagnóstico Radiológico: Avanços, Desafios e Perspectivas Futuras. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (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.
