Legal liability arising from livestream sales activities in the healthcare sector using virtual consultants
Published: July 20, 2026
Abstract
This study analyzes legal risks arising from livestream sales activities in the healthcare sector involving virtual consultants generated by artificial intelligence technologies. While livestream selling enhances customer engagement and operational efficiency, the use of AI-based virtual consultants introduces risks related to misinformation, lack of professional accountability, and potential harm to consumer health. The study proposes solutions to ensure transparency in AI use, establish governance mechanisms for AI systems, and protect consumer health and rights.
Keywords
AI livestreamartificial intelligence applicationsvirtual consultantsAI-based advisors
References
1.
Nguyễn Thị Hồng Nhung & Nguyễn Thị Thu Hương (2025). Bảo vệ người tiêu dùng trong hoạt động mua sắm giải trí trên các nền tảng thương mại điện tử tại Việt Nam. Tạp chí Khoa học Trường Đại học Mở Hà Nội, 552-552. https://doi.org/10.59266/houjs.2025.973
Phạm Thị Minh Thư (2023). Ảnh hưởng của nghiện internet đến hành vi mua hàng bốc đồng trực tuyến dưới tác động của trí tuệ cảm xúc. https://digital.lib.ueh.edu.vn/handle/UEH/70119
3.
Quốc hội (2005). Luật Thương mại số 36/2005/QH11 ngày 14/6/2005.
4.
Quốc hội (2023). Luật Bảo vệ quyền lợi người tiêu dùng số 19/2023/QH15 ngày 20/6/2023.
5.
Quốc hội (2025).Luật Trí tuệ nhân tạo số 134/2025/QH15 ngày 10/12/2025.
6.
Ben Saad, S., & Choura, F. (2022). Towards better interaction between salespeople and consumers: The role of virtual recommendation agent. European Journal of Marketing, 57(3), 858-903. https://doi.org/10.1108/EJM-11-2021-0892
Granat, M. M., Paź, A., & Mirowska-Guzel, D. (2025). Testing the knowledge of artificial intelligence chatbots in pharmacology: Examples of two groups of drugs. PeerJ Computer Science, 11, e2954. https://doi.org/10.7717/peerj-cs.2954
Han, B., Liu, D., & Qi, S. (2025). Beyond human streamers: How AI-avatar engagement strategies influence consumer acceptance. Asia Pacific Journal of Marketing and Logistics, 1-20. https://doi.org/10.1108/APJML-04-2025-0618
London, A. J. (2019). Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Center Report, 49(1), 15-21. https://doi.org/10.1002/hast.973
Mavlanova, T., Benbunan-Fich, R., & Koufaris, M. (2012). Signaling theory and information asymmetry in online commerce. Information & Management, 49(5), 240-247. https://doi.org/10.1016/j.im.2012.05.004
Pedreschi, D., Giannotti, F., Guidotti, R., Monreale, A., Ruggieri, S., & Turini, F. (2019). Meaningful Explanations of Black Box AI Decision Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9780-9784. https://doi.org/10.1609/aaai.v33i01.33019780
Shiferaw, M. W., Zheng, T., Winter, A., Mike, L. A., & Chan, L.-N. (2024). Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions. BMC Medical Informatics and Decision Making, 24(1), 404. https://doi.org/10.1186/s12911-024-02824-5
Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024). AI hallucination: Towards a comprehensive classification of distorted information in artificial intelligence-generated content. Humanities and Social Sciences Communications, 11(1), 1278. https://doi.org/10.1057/s41599-024-03811-x
Visser, B., van der Putten, P., & Zohrehvand, A. (2025). The Impact of AI Avatar Appearance and Disclosure on User Motivation. Data Science and Artificial Intelligence, 142–155. https://doi.org/10.1007/978-981-97-9793-6_10
