Artificial Intelligence and Chatbots in Patient Management

Authors

  • Andrea Kelen University of Sopron
  • Beatrix Faragó University of Sopron

DOI:

https://doi.org/10.32976/stratfuz.2026.15

Keywords:

Artificial Intelligence, Chatbot, Digital Health, Patient Management, Regional Inequality

Abstract

Artificial intelligence (AI) and chatbots are expected to play an increasingly important role in patient management systems worldwide. These technologies have the potential to support preliminary triage, reduce waiting times, and alleviate the administrative burden placed on healthcare professionals. At the same time, their application raises substantial challenges related to data protection, ethical accountability, clinical responsibility, and professional practice. The purpose of this study is to present the opportunities and limitations of chatbot applications in healthcare and to illustrate their regional relevance in Hungary through the example of Northern Hungary. Based on a structured literature review and secondary data analysis, the findings indicate that chatbot integration may contribute to enhanced system efficiency and reduced inequalities in access to outpatient care, provided that implementation is accompanied by appropriate regulatory, ethical, and institutional safeguards.

Author Biographies

Andrea Kelen, University of Sopron

PhD Student, University of Sopron Alexandre Lamfalussy Doctoral School of Business and Organizational Science

Beatrix Faragó, University of Sopron

PhD, associate professor, University of Sopron Alexandre Lamfalussy Faculty of Economics Research Centre

References

Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006

AP News (2023). Health providers say AI chatbots could improve care, but research suggests some systems may perpetuate bias. Associated Press.

Bhatt, D., Ayyagari, S., & Mishra, A. (2024) A scalable approach to benchmarking the in-conversation differential diagnostic accuracy of a health AI (arXiv:2412.12538, v1). arXiv Preprint. https://doi.org/10.48550/arXiv.2412.12538

Döbrössy, B., Girasek, E., & Győrffy, Zs. (2024). The adaptation of digital health solutions during the COVID-19 pandemic in Hungary: A scoping review. International Journal of Health Policy and Management, 13, Article 7940. https://doi.org/10.34172/ijhpm.7940

EESZT. EESZT National eHealth infrastructure and the medium-long term developments of digital health. OECD Digital Platform. (OECD policy leírás), https://depp.oecd.org/policies/HUN1144?utm_source=chatgpt.com

Fernandes, B. Ó., Lucevic, A., Péntek, M., Kringos, D., Klazinga, N., Gulácsi, L., Zrubka, Z., & Baji, P. (2021). Self-Reported Waiting Times for Outpatient Health Care Services in Hungary: Results of a Cross-Sectional Survey on a National Representative Sample. International Journal of Environmental Research and Public Health, 18(5), 2213. https://doi.org/10.3390/ijerph18052213

Hindelang, M., Sitaru, S., & Zink, A. (2024). Transforming health care through chatbots for medical history-taking and future directions: A comprehensive systematic review. JMIR Medical Informatics, 12, e56628. https://doi.org/10.2196/56628

Horváthné Csolák, E. (2025). COVID hatása a kórházak gazdálkodására Magyarországon 2020-ban. (Balance sheets and results during COVID, Management of public hospitals in Hungary in 2020). Észak-magyarországi Stratégiai Füzetek, 22(1), 107-117. https://doi.org/10.32976/stratfuz.2025.8

Khare, A. Reddy Penubaka, K. K., Chithrakumar, T., Geetha, M., Kamalavalli, K., & Bhagirath Jadhav, A. (2025) - AI-driven patient flow management in hospitals: Reducing wait times and enhancing care. Journal of Neonatal Surgery, 14(105),696-708. https://doi.org/10.52783/jns.v14.2907

Laymouna, M., Ma, Y., Lessard, D., Schuster, T., Engler, K., & Lebouché, B. (2024). Roles, users, benefits, and limitations of chatbots in health care: A systematic review. Journal of Medical Internet Research, 26, e56930. https://doi.org/10.2196/56930

Li, X., Tian, D., Li, W., Dong, B., Wang, H., Yuan, J., Li, B., Shi, L., Lin, X., Zhao, L., & Liu, S. (2021). Artificial intelligence-assisted reduction in patients’ waiting time for outpatient process: A retrospective cohort study. BMC Health Services Research, 21, 237. https://doi.org/10.1186/s12913-021-06248-z

Pawellek, S., Ziegeldorf, A., & Wulff, H. (2022). Strategien und Effekte digitaler Interventionen bei der Übergewichts- und Adipositastherapie von Kindern und Jugendlichen – ein systematischer Review. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 65(5), 624–634. https://doi.org/10.1007/s00103-022-03512-3

Simon, B., Hartveg, Á., Dénes-Fazakas, L, Eigner, G., & Szilágyi, L. (2024). Advancing medical assistance: Developing an effective Hungarian-language medical chatbot with artificial intelligence. Information, 15(6), 297. https://doi.org/10.3390/info15060297

Rao, D. (2025). AI chatbots are leading some to psychosis. The Week. Retrieved 29 June 2025 from https://theweek.com/tech/ai-chatbots-psychosis-chatgpt-mental-health

Times of India (2025). AI chatbots like ChatGPT can be dangerous for doctors as well as patients, warns MIT research. The Times of India, 25 June 2025. https://timesofindia.indiatimes.com/technology/tech-news/ai-chatbots-like-chatgpt-can-be-dangerous-for-doctors-as-well-as-patients-as-warns-mit-research/articleshow/122076203.cms

Wah, J. (2025). Revolutionizing e-health: The role of AI-powered hybrid chatbots in healthcare solutions. Frontiers in Public Health, 13, 1530799. https://doi.org/10.3389/fpubh.2025.1530799

Downloads

Published

2026-07-20

How to Cite

Kelen, A., & Faragó, B. (2026). Artificial Intelligence and Chatbots in Patient Management. Strategic Issues of Northern Hungary, 23(02), 43–53. https://doi.org/10.32976/stratfuz.2026.15