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

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.

Authors

Institutions

Publication Details

Journal
European Journal of Cardiovascular Nursing
Published
2025-05-31
DOI
https://doi.org/10.1093/eurjcn/zvaf104
Citations
5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
3.27
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Philip Moons, Polat Göktaş, Emine Aktaş, Aycan Küçükkaya
5 citations
European Journal of Cardiovascular Nursing
Artificial Intelligence in Healthcare and Education
3.27
article

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

Philip Moons, Polat Göktaş, Emine Aktaş, Aycan Küçükkaya
article en
5 citations

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.

European Journal of Cardiovascular NursingVol. 24(7)
University College Dublin (IE), Istanbul University-Cerrahpaşa (TR), KU Leuven (BE)
Reduced inequalities
Openalex Percentile: Top 7%
Artificial Intelligence in Healthcare and Education
3.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.