Impactos da Inteligência Artificial na Tomada de Decisão Médica: Um Mapeamento Sistemático
Resumo
Com advento da Inteligência Artificial (IA) muitas soluções na área da Informática Médica estão sendo discutidas atualmente. Nesse sentido, o presente artigo apresenta um Mapeamento Sistemático da Literatura (MSL), com o intuito de identificar algumas das mudanças provocadas pela inserção da Inteligência Artificial no cotidiano de profissionais da saúde. Foi evidenciado como a IA auxilia o campo médico e aborda as principais ferramentas e técnicas de processamento de dados. Como resultados, foram analisados 14 publicações científicas presentes nas bases da Scopus, Web of Science, ACM Digital Library, Science Direct e IEEE. Algumas das ferramentas mais comuns adotadas no processamento de informações médicas são: processamento de texto e imagens, e principalmente a detecção de enfermidades.Referências
Aizawa, K., Aoyama, M., and Murakami, K. (2012). Language recognition power of watson-crick automata: Multiheads and sensing. In 2012 Third International Conference on Networking and Computing, pages 357–361.
Aljaaf, A. J., Al-Jumeily, D., Hussain, A. J., Lamb, D., Al-Jumaily, M., and Abdel-Aziz, K. (2014). A study of data classification and selection techniques for medical decision support systems. In International Conference on Intelligent Computing, pages 135–143. Springer.
Dascalu, C. G., Zegan, G., Cernei, E. R., and Mavru, R. B. (2015). Time series analysis in predicting the oro-maxillo-facial traumas. In 2015 E-Health and Bioengineering Conference (EHB), pages 1–4.
El-Sherbiny, B., Nabil, N., El-Naby, S. H., Emad, Y., Ayman, N., Mohiy, T., and AbdelRaouf, A. (2018). Blb (brain/lung cancer detection and segmentation and breast dense calculation). In 2018 First International Workshop on Deep and Representation Learning (IWDRL), pages 41–47. IEEE.
Escalante, H. J., Montes-y Gómez, M., González, J. A., Gómez-Gil, P., Altamirano, L., Reyes, C. A., Reta, C., and Rosales, A. (2012). Acute leukemia classification by ensemble particle swarm model selection. Artificial intelligence in medicine, 55(3):163–175.
Farooq, A., Anwar, S., Awais, M., and Alnowami, M. (2017). Artificial intelligence based smart diagnosis of alzheimer’s disease and mild cognitive impairment. In 2017 International Smart cities conference (ISC2), pages 1–4. IEEE.
Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Citeseer.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Longoni, C., Bonezzi, A., and Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4):629–650.
Madhu, D., Jain, C. N., Sebastain, E., Shaji, S., and Ajayakumar, A. (2017). A novel approach for medical assistance using trained chatbot. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pages 243–246.
McCarthy, J., Minsky, M. L., Rochester, N., and Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4):12–12.
Moreno, S., Bonfante, M., Zurek, E., and San Juan, H. (2019). Study of medical image processing techniques applied to lung cancer. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6. IEEE.
Ozkan, I. A., Koklu, M., and Sert, I. U. (2018). Diagnosis of urinary tract infection based on artificial intelligence methods. Computer methods and programs in biomedicine, 166:51–59.
Papageorgiou, E. I. (2011). A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing, 11(1):500–513.
Shen, Y., Colloc, J., Jacquet-Andrieu, A., and Lei, K. (2015). Emerging medical informatics with case-based reasoning for aiding clinical decision in multiagent system. Journal of biomedical informatics, 56:307–317.
Silva, A. and Bállico, R. D. V. Impactos da implementação da inteligência artificial na tomada de decisão médica.
Wilhelm, P., Reinhardt, J. M., and Van Daele, D. (2020). A deep learning approach to video fluoroscopic swallowing exam classification. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pages 1647–1650.
Yeasmin, S. (2019). Benefits of artificial intelligence in medicine. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pages 1–6. IEEE.
Yu, H. Q. (2019). Extracting reliable health condition and symptom information to support machine learning. In 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 1683–1687.
Aljaaf, A. J., Al-Jumeily, D., Hussain, A. J., Lamb, D., Al-Jumaily, M., and Abdel-Aziz, K. (2014). A study of data classification and selection techniques for medical decision support systems. In International Conference on Intelligent Computing, pages 135–143. Springer.
Dascalu, C. G., Zegan, G., Cernei, E. R., and Mavru, R. B. (2015). Time series analysis in predicting the oro-maxillo-facial traumas. In 2015 E-Health and Bioengineering Conference (EHB), pages 1–4.
El-Sherbiny, B., Nabil, N., El-Naby, S. H., Emad, Y., Ayman, N., Mohiy, T., and AbdelRaouf, A. (2018). Blb (brain/lung cancer detection and segmentation and breast dense calculation). In 2018 First International Workshop on Deep and Representation Learning (IWDRL), pages 41–47. IEEE.
Escalante, H. J., Montes-y Gómez, M., González, J. A., Gómez-Gil, P., Altamirano, L., Reyes, C. A., Reta, C., and Rosales, A. (2012). Acute leukemia classification by ensemble particle swarm model selection. Artificial intelligence in medicine, 55(3):163–175.
Farooq, A., Anwar, S., Awais, M., and Alnowami, M. (2017). Artificial intelligence based smart diagnosis of alzheimer’s disease and mild cognitive impairment. In 2017 International Smart cities conference (ISC2), pages 1–4. IEEE.
Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Citeseer.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Longoni, C., Bonezzi, A., and Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4):629–650.
Madhu, D., Jain, C. N., Sebastain, E., Shaji, S., and Ajayakumar, A. (2017). A novel approach for medical assistance using trained chatbot. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pages 243–246.
McCarthy, J., Minsky, M. L., Rochester, N., and Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4):12–12.
Moreno, S., Bonfante, M., Zurek, E., and San Juan, H. (2019). Study of medical image processing techniques applied to lung cancer. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6. IEEE.
Ozkan, I. A., Koklu, M., and Sert, I. U. (2018). Diagnosis of urinary tract infection based on artificial intelligence methods. Computer methods and programs in biomedicine, 166:51–59.
Papageorgiou, E. I. (2011). A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing, 11(1):500–513.
Shen, Y., Colloc, J., Jacquet-Andrieu, A., and Lei, K. (2015). Emerging medical informatics with case-based reasoning for aiding clinical decision in multiagent system. Journal of biomedical informatics, 56:307–317.
Silva, A. and Bállico, R. D. V. Impactos da implementação da inteligência artificial na tomada de decisão médica.
Wilhelm, P., Reinhardt, J. M., and Van Daele, D. (2020). A deep learning approach to video fluoroscopic swallowing exam classification. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pages 1647–1650.
Yeasmin, S. (2019). Benefits of artificial intelligence in medicine. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pages 1–6. IEEE.
Yu, H. Q. (2019). Extracting reliable health condition and symptom information to support machine learning. In 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 1683–1687.
Publicado
25/10/2021
Como Citar
RESENDE, Fabrícia Karollyne Santos; INVENCÃO, Maria Estella Santos da; SILVA, Gilton José Ferreira da.
Impactos da Inteligência Artificial na Tomada de Decisão Médica: Um Mapeamento Sistemático. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 21. , 2021, Maceió.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 41-50.
DOI: https://doi.org/10.5753/erbase.2021.20055.
