Impactos da Inteligência Artificial na Tomada de Decisão Médica: Um Mapeamento Sistemático
Abstract
With the advent of Artificial Intelligence (AI) many solutions in the field of Medical Informatics that optimize and improve clinical care to the population are currently being discussed. In this sense, this article proposes a Systematic Literature Mapping (MSL), in order to identify some of the changes that the Integration of Artificial Intelligence will cause in the daily lives of health professionals. It was shown how AI helps the medical field and addresses the main tools and techniques for data processing. As a result, 14 scientific publications were developed present in the bases of Scopus, Web of Science, ACM Digital Library and IEEE. Some of the most common tools used in the processing of medical information are: text and image processing and mainly, disease prediction.References
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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.
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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.
Published
2021-10-25
How to Cite
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: REGIONAL SCHOOL ON COMPUTING OF BAHIA, ALAGOAS, AND 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.
