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Equality, diversity, and inclusion in artificial intelligence-driven healthcare chatbots: addressing challenges and shaping strategies

Year: 2025

Type: article

Abstract: 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, enhances fairness, and requires cross-disciplinary collaboration to ensure AI delivers equitable, inclusive healthcare for all.

Source: European Journal of Cardiovascular Nursing

DOI: 10.1093/eurjcn/zvaf104

Authors: Aycan Kucukkaya, Emine Aktas Bajalan, Philip Moons, Polat Goktas

Institutions: Istanbul University-Cerrahpaşa, Istanbul University, KU Leuven, University College Dublin

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $3,792