Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management

Abstract

The rapid integration of AI-driven chatbots into oncology education represents both a transformative opportunity and a critical challenge. These systems, powered by advanced language models, can deliver personalized, real-time cancer information to patients, caregivers, and clinicians, bridging gaps in access and availability. However, their ability to convincingly mimic human-like conversation raises pressing concerns regarding misinformation, trust, and their overall effectiveness in digital health communication. This review examines the dual-edged role of AI chatbots, exploring their capacity to support patient education and alleviate clinical burdens, while highlighting the risks of lack of or inadequate algorithmic opacity (i.e., the inability to see the data and reasoning used to make a decision, which hinders appropriate future action), false information, and the ethical dilemmas posed by human-seeming AI entities. Strategies to mitigate these risks include robust oversight, transparent algorithmic development, and alignment with evidence-based oncology protocols. Ultimately, the responsible deployment of AI chatbots requires a commitment to safeguarding the core values of evidence-based practice, patient trust, and human-centered care.

Ähnliche Arbeiten