Kucukkaya Aycan, Aktas Bajalan Emine, Moons Philip, Goktas Polat
Istanbul University-Cerrahpasa, Institute of Graduate Studies, Istanbul, Turkey.
KU Leuven, Department of Environment and Health, Leuven, Belgium.
Eur J Cardiovasc Nurs. 2025 Jun 2. doi: 10.1093/eurjcn/zvaf104.
Artificial intelligence (AI)-driven chatbots hold promise for improving patient care and healthcare efficiency, but integrating Equality, Diversity, and Inclusion (EDI) remains challenging. This discussion paper explores the potential for EDI-focused chatbots, emphasizing the need for ongoing assessment, diverse datasets, and collaboration among healthcare providers, technologists, and policymakers. While acknowledging current limitations such as algorithmic bias, the paper also emphasizes the potential of AI to support and extend human decision-making, particularly through real-time analytics and scalable patient support. Embedding EDI principles helps reduce bias, enhance fairness, and requires cross-disciplinary collaboration to ensure AI delivers equitable, inclusive healthcare for all.
人工智能驱动的聊天机器人有望改善患者护理和医疗保健效率，但将平等、多样性和包容性（EDI）融入其中仍然具有挑战性。本讨论文件探讨了以EDI为重点的聊天机器人的潜力，强调了持续评估、多样化数据集以及医疗保健提供者、技术专家和政策制定者之间合作的必要性。在承认算法偏见等当前局限性的同时，该文件还强调了人工智能支持和扩展人类决策的潜力，特别是通过实时分析和可扩展的患者支持。融入EDI原则有助于减少偏见、增强公平性，并且需要跨学科合作以确保人工智能为所有人提供公平、包容的医疗保健。
