Integration of Artificial Intelligence in Telemedicine: Development and Evaluation of a Chatbot Specialized in Viral Diseases
DOI:
https://doi.org/10.33936/isrtic.v8i2.6840Keywords:
Chatbot, Viral diseases, Telecare, Artificial Intelligence, Natural Language ProcessingAbstract
This study evaluates the implementation and effectiveness of an integrated chatbot within telemedicine, specifically designed for patients with viral diseases. The objective was to develop and validate a natural language processing (NLP)-based tool to enhance communication between patients and healthcare providers, offering rapid, accurate, and personalized responses. Utilizing a methodology that incorporates advanced NLP technologies, the chatbot was programmed to respond to queries related to symptoms, prevention, and management of viral diseases. The system's evaluation was conducted through the simulation of real-world scenarios, comparing the chatbot's responses with those of medical experts. The results indicate that the chatbot achieves a high similarity to expert responses, with an average cosine similarity of 0.913 and a mean Euclidean distance of 0.405, demonstrating the relevance and accuracy of the generated answers. Conclusively, the study shows that chatbots can play a vital role in telemedicine, facilitating quicker and more effective access to medical information and improving the management of patient care for viral diseases. This research highlights the importance of integrating NLP in digital health, underscoring the potential of chatbots to revolutionize healthcare by offering accessible and personalized solutions to current challenges in viral disease management within the biomedical field.
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Amjad, A., Kordel, P., & Fernandes, G. (2023). A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence. Sustainability, 15(8), 6655. https://doi.org/10.3390/su15086655
Angular. (2024). Angular - Official Documentation. Angular. https://angular.io/
Bharti, U., Bajaj, D., Batra, H., Lalit, S., Lalit, S., & Gangwani, A. (2020). Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 870–875. https://doi.org/10.1109/ICCES48766.2020.9137944
Branley-Bell, D., Brown, R., Coventry, L., & Sillence, E. (2023). Chatbots for embarrassing and stigmatizing conditions: could chatbots encourage users to seek medical advice? Frontiers in Communication, 8. https://doi.org/10.3389/fcomm.2023.1275127
Burrell, D. N. (2023). Dynamic Evaluation Approaches to Telehealth Technologies and Artificial Intelligence (AI) Telemedicine Applications in Healthcare and Biotechnology Organizations. Merits, 3(4), 700–721. https://doi.org/10.3390/merits3040042
Chen, E. T. (2020). Improving Patient Care With Telemedicine Technology (pp. 1–18). https://doi.org/10.4018/978-1-7998-0047-7.ch001
Chowdhury, M. N.-U.-R., Haque, A., & Soliman, H. (2023). Chatbots: A Game Changer in mHealth. 2023 Sixth International Symposium on Computer, Consumer and Control (IS3C), 362–366. https://doi.org/10.1109/IS3C57901.2023.00103
Easton, K., Potter, S., Bec, R., Bennion, M., Christensen, H., Grindell, C., Mirheidari, B., Weich, S., de Witte, L., Wolstenholme, D., & Hawley, M. S. (2019). A Virtual Agent to Support Individuals Living With Physical and Mental Comorbidities: Co-Design and Acceptability Testing. Journal of Medical Internet Research, 21(5), e12996. https://doi.org/10.2196/12996
Flask. (2024). Welcome to Flask — Flask Documentation (3.0.x). https://flask.palletsprojects.com/en/3.0.x/
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Guo, Q., Wang, M., & Wang, H. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. https://doi.org/10.48550/arXiv.2312.10997
Garcia Saiso, S., Marti, M. C., Malek Pascha, V., Pacheco, A., Luna, D., Plazzotta, F., Nelson, J., Tejerina, L., Bagolle, A., Savignano, M. C., Baum, A., Orefice, P. J., Haddad, A. E., Messina, L. A., Lopes, P., Rubió, F. S., Otzoy, D., Curioso, W. H., Luna, A., … D’Agostino, M. (2021). Barreras y facilitadores a la implementación de la telemedicina en las Américas. Revista Panamericana de Salud Pública, 45, 1. https://doi.org/10.26633/RPSP.2021.131
Google. (2022). Firebase. https://firebase.google.com/?hl=es-419
Insomnia. (2021). Insomnia Docs. https://docs.insomnia.rest/
Khairat, S., Chourasia, P., Muellers, K. A., Andreadis, K., Lin, J. J., & Ancker, J. S. (2023). Patient and Provider Recommendations for Improved Telemedicine User Experience in Primary Care: A Multi-Center Qualitative Study. Telemedicine Reports, 4(1), 21–29. https://doi.org/10.1089/tmr.2023.0002
Koulaouzidis, G., Charisopoulou, D., Wojakowski, W., Koulaouzidis, A., Marlicz, W., & Jadczyk, T. (2020). Telemedicine in cardiology in the time of coronavirus disease 2019: a friend that everybody needs. Polish Archives of Internal Medicine. https://doi.org/10.20452/pamw.15432
Levonian, Z., Li, C., Zhu, W., Gade, A., Henkel, O., Postle, M.-E., & Xing, W. (2023). Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference. https://doi.org/10.48550/arXiv.2310.03184
Mahdavi, A., Amanzadeh, M., Hamedan, M., & Naemi, R. (2023). Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review. Shiraz E-Medical Journal, 24(11). https://doi.org/10.5812/semj-139627
Mukherjee, S., & Sonal, R. (2023). A reconciliation between cosine similarity and Euclidean distance in individual decision-making problems. Indian Economic Review, 58(2), 427–431. https://doi.org/10.1007/s41775-023-00206-8
OpenAI. (2024a). OpenAI Embeddings. 2024. https://platform.openai.com/docs/guides/embeddings
OpenAI. (2024b). OpenAI Text Generation Models. 2024. https://platform.openai.com/docs/guides/text-generation
Ritchey, K. C., Foy, A., McArdel, E., & Gruenewald, D. A. (2020). Reinventing Palliative Care Delivery in the Era of COVID-19: How Telemedicine Can Support End of Life Care. American Journal of Hospice and Palliative Medicine®, 37(11), 992–997. https://doi.org/10.1177/1049909120948235
Saigí-Rubió, F. (2023). Promoting telemedicine in Latin America in light of COVID-19. Revista Panamericana de Salud Pública, 47, 1. https://doi.org/10.26633/RPSP.2023.17
Schachner, T., Keller, R., & v Wangenheim, F. (2020). Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review. Journal of Medical Internet Research, 22(9), e20701. https://doi.org/10.2196/20701
Singh, A., Joshi, S., & Domb, M. (2023). Embedded Conversational AI, Chatbots, and NLP to Improve Healthcare Administration and Practices. 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), 38–45. https://doi.org/10.1109/ICACRS58579.2023.10404985
Song, Y., Bernard, L., Jorgensen, C., Dusfour, G., & Pers, Y.-M. (2021). The Challenges of Telemedicine in Rheumatology. Frontiers in Medicine, 8. https://doi.org/10.3389/fmed.2021.746219
Vasileiou, M. V., & Maglogiannis, I. G. (2022). The Health ChatBots in Telemedicine: Intelligent Dialog System for Remote Support. Journal of Healthcare Engineering, 2022, 1–12. https://doi.org/10.1155/2022/4876512
Yamaguchi, D., Li, G.-D., & Nagai, M. (2007). A grey-based rough approximation model for interval data processing. Information Sciences, 177(21), 4727–4744. https://doi.org/10.1016/j.ins.2007.05.022
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Copyright (c) 2024 Alejandro Josafat Rodriguez Vargas, Sandra Verónica Falconí Peláez , Bertha Eugenia Mazón Olivo, Eduardo Alejandro Tusa Jumbo
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