Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Expert Consensus Best Practices for the Safe, Ethical, and Effective Design and Implementation of Artificially Intelligent Conversational Agent (i.e., Chatbot/Virtual Human) Systems in Health Care Applications
1
Zitationen
5
Autoren
2025
Jahr
Abstract
The integration of artificially intelligent conversational agents (AICAs), variously referred to as chatbots and virtual humans (VHs), is transforming health care delivery and education. This article explores our perspective on best practices for the evolution, potential, and ethical considerations of AICAs in clinical and educational contexts. Early applications of simulation technology in health care focused on productivity improvements, teletherapy, and virtual reality therapy applications. Recent technological advancements have enabled the development of high-fidelity extended reality systems and AICAs capable of engaging users in credible interactions. These systems leverage natural language processing, machine learning, large language models, and advanced VH authoring software to create interactive, personalized, and engaging experiences. Recent efforts in the creation of AICAs suggest significant potential benefits, including enhanced patient engagement, improved access to self-care resources, and low-stigma interaction environments. They have demonstrated promise in mental health support, providing a sense of safety and encouraging open disclosure. However, the rapid adoption of AICAs raises critical challenges, including safeguarding user privacy, ensuring system reliability, and addressing ethical concerns. Incidents of harm, such as inappropriate interactions and psychological distress, highlight the need for rigorous design and implementation best practices. This article outlines key principles for developing safe, effective, and equitable AICAs, emphasizing transparency in artificial intelligence (AI) identity, accountability, cultural sensitivity, and informed consent. Additionally, the authors advocate for robust privacy measures, adaptive learning capabilities, and evidence-based content validation to optimize user experience and maintain trust. To mitigate risks, a "human-in-the-loop" approach is recommended, ensuring health care professionals oversee AI-supported decisions. By adhering to these best practices, AICAs can enhance health care accessibility, support clinical training, and complement human professionals. This work aims to provide a foundation for the ethical and effective integration of AICAs, maximizing their potential while minimizing risks, ultimately advancing patient care and education in the digital age.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.100 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.466 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.429 Zit.