Agente de inteligência artificial para verificar a validade de sinais auto-coletados por pacientes de telemedicina
Resumo
Este artigo apresenta uma proposta para o desenvolvimento de uma solução que utilize inteligência artificial generativa para avaliar a coleta de dados fisiológicos realizados pelo próprio paciente, identificando sua validade e auxiliando o paciente a realizar aquisições com qualidade.Referências
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BERRYMAN, J. and ZIEGLER, A. (2025). Prompt engineering for LLMs: The art and science of building large language model-based applications. O’Really Media Inc., Sebastopol, CA, USA.
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DELGADO-CHAVES, F., JENNINGS, M. J., ATALAIA, A., WOLFF, J., HORVÁTH, R., MAMDOUH, Z. M., BAUMBACH, J., and BAUMBACH, L. (2025). Transforming literature screening: the emerging role of large language models in systematic reviews. Proceedings of the National Academy of Sciences of the United States of America, 122(2). DOI: 10.1073/pnas.2411962122.
HE, Y., TANG, B., and WANG, X. (2024). Generative models for automatic medical decision rule extraction from text. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), pages 7034–7048, Miami, Florida, USA. Association for Computational Linguistics. DOI: 10.18653/v1/2024.emnlp-main.399.
HUANG, K. (2025). Agentic AI: theories and practices. Progress in IS. Springer Nature Switzerland, Cham, Suiça. DOI: 10.1007/978-3-031-90026-6.
JURAFSKY, D. and MARTIN, J. H. (2025). Speech and language processing: an introduction to natural language processing, Computational Linguistics, and Speech Recognition with Language Models. 3 edition. Disponível em [link]. Acesso em 11 set. 2025.
Lucas, H. C., Upperman, J. S., and Robinson, J. R. (2024). A systematic review of large language models and their implications in medical education. Medical education, 58(11):1276–1285.
MAZUMDER, E. R. (2025). AI agent for all: a creative guide to understanding and building AI agents. via tolino media. Disponível em [link]. Acesso em 10 set. 2025.
PATTHI, T. (2025). Agentic AI:a practical guide to build agent-based AI systems that think and act: build intelligent agents for real-world AI automation, Machine Learning, and Autonomous Systems with Practical Code and Tools. Disponível em [link]. Acesso em 13 set. 2025.
SHAO, C. C., KATTA, M. H., SMITH, B. P., JONES, B. A., GLEASON, L. T., ABBAS, A., WADHWANI, N., WALLACE, E. L., MUGAVERO, M. J., and CHU, D. I. (2025). Reducing no-show visits and disparities in access: the impact of telemedicine. Journal of Telemedicine and Telecare, 31(7):1041–1049. DOI: 10.1177/1357633X241241357.
SHIN, D. A., KIM, J., CHOI, S.-W., and LEE, J. C. (2023). DNN based reliability evaluation for telemedicine data. Biomedical Engineering Letters, 13(1):11–19. DOI: 10.1007/s13534-022-00248-6.
SIAM, A. I., EL-AFFENDI, M. A., ELAZM, A. A., EL-BANBY, G. M., EL-BAHNASAWY, N. A., EL-SAMIE, F. E. A., and EL-LATIF, A. A. A. (2023). Portable and real-time iot-based healthcare monitoring system for daily medical applications. IEEE Transactions on Computational Social Systems, 10(4):1629–1641. DOI: 10.1109/TCSS.2022.3207562.
VEGESNA, A., TRAN, M., ANGELACCIO, M., and ARCONA, S. (2017). Remote patient monitoring via non-invasive digital technologies: a systematic review. Tele-medicine and e-Health, 23(1):3–17. DOI: 10.1089/tmj.2016.0051.
YILDIRIM, S. and CHENAGHLU, M. (2024). Mastering transformers: the journey from BERT to large language models and stable diffusion. Packt Publishing Ltd., Birmingham, Reino Unido.
BARBOSA, W., ZHOU, K., WADDELL, E., MYERS, T., and DORSEY, E. R. (2021). Improving access to care: telemedicine across medical domains. Annual Review of Public Health, 42(1):463–481. DOI: 10.1146/annurev-publhealth-090519-093711.
BERRYMAN, J. and ZIEGLER, A. (2025). Prompt engineering for LLMs: The art and science of building large language model-based applications. O’Really Media Inc., Sebastopol, CA, USA.
CHEN, Q., DU, J., HU, Y., KELOTH, V., PENG, X., RAJA, K., ZHANG, R., LU, Z., and XU, H. (2023). Benchmarking large language models for biomedical natural language processing applications and recommendations. Nature Communications, 16(1). DOI: 10.1038/s41467-025-56989-2.
DELGADO-CHAVES, F., JENNINGS, M. J., ATALAIA, A., WOLFF, J., HORVÁTH, R., MAMDOUH, Z. M., BAUMBACH, J., and BAUMBACH, L. (2025). Transforming literature screening: the emerging role of large language models in systematic reviews. Proceedings of the National Academy of Sciences of the United States of America, 122(2). DOI: 10.1073/pnas.2411962122.
HE, Y., TANG, B., and WANG, X. (2024). Generative models for automatic medical decision rule extraction from text. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), pages 7034–7048, Miami, Florida, USA. Association for Computational Linguistics. DOI: 10.18653/v1/2024.emnlp-main.399.
HUANG, K. (2025). Agentic AI: theories and practices. Progress in IS. Springer Nature Switzerland, Cham, Suiça. DOI: 10.1007/978-3-031-90026-6.
JURAFSKY, D. and MARTIN, J. H. (2025). Speech and language processing: an introduction to natural language processing, Computational Linguistics, and Speech Recognition with Language Models. 3 edition. Disponível em [link]. Acesso em 11 set. 2025.
Lucas, H. C., Upperman, J. S., and Robinson, J. R. (2024). A systematic review of large language models and their implications in medical education. Medical education, 58(11):1276–1285.
MAZUMDER, E. R. (2025). AI agent for all: a creative guide to understanding and building AI agents. via tolino media. Disponível em [link]. Acesso em 10 set. 2025.
PATTHI, T. (2025). Agentic AI:a practical guide to build agent-based AI systems that think and act: build intelligent agents for real-world AI automation, Machine Learning, and Autonomous Systems with Practical Code and Tools. Disponível em [link]. Acesso em 13 set. 2025.
SHAO, C. C., KATTA, M. H., SMITH, B. P., JONES, B. A., GLEASON, L. T., ABBAS, A., WADHWANI, N., WALLACE, E. L., MUGAVERO, M. J., and CHU, D. I. (2025). Reducing no-show visits and disparities in access: the impact of telemedicine. Journal of Telemedicine and Telecare, 31(7):1041–1049. DOI: 10.1177/1357633X241241357.
SHIN, D. A., KIM, J., CHOI, S.-W., and LEE, J. C. (2023). DNN based reliability evaluation for telemedicine data. Biomedical Engineering Letters, 13(1):11–19. DOI: 10.1007/s13534-022-00248-6.
SIAM, A. I., EL-AFFENDI, M. A., ELAZM, A. A., EL-BANBY, G. M., EL-BAHNASAWY, N. A., EL-SAMIE, F. E. A., and EL-LATIF, A. A. A. (2023). Portable and real-time iot-based healthcare monitoring system for daily medical applications. IEEE Transactions on Computational Social Systems, 10(4):1629–1641. DOI: 10.1109/TCSS.2022.3207562.
VEGESNA, A., TRAN, M., ANGELACCIO, M., and ARCONA, S. (2017). Remote patient monitoring via non-invasive digital technologies: a systematic review. Tele-medicine and e-Health, 23(1):3–17. DOI: 10.1089/tmj.2016.0051.
YILDIRIM, S. and CHENAGHLU, M. (2024). Mastering transformers: the journey from BERT to large language models and stable diffusion. Packt Publishing Ltd., Birmingham, Reino Unido.
Publicado
01/06/2026
Como Citar
MARASCHIN, Francisco Paulo; SPALDING, Luiz Eduardo S.; REBONATTO, Marcelo Trindade.
Agente de inteligência artificial para verificar a validade de sinais auto-coletados por pacientes de telemedicina. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1457-1462.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.21647.
