Front Matter

Abstract

Conversational agents (CAs), such as medical interview assistants, are increasingly used in healthcare settings due to their potential for intuitive user interaction.Ensuring the inclusivity of these systems is critical to provide equitable and effective digital health support.However, the underlying technology, models and data can foster inequalities and exclude certain individuals.This paper explores key principles of inclusivity in patient-oriented language processing (POLP) for healthcare CAs to improve accessibility, cultural sensitivity, and fairness in patient interactions.We will outline, how considering the six facets of inclusive Artificial Intelligence (AI) will shape POLP within healthcare CA.Key considerations include leveraging diverse datasets, incorporating gender-neutral and inclusive language, supporting varying levels of health literacy, and ensuring culturally relevant communication.To address these issues, future research in POLP should focus on optimizing conversation structure, enhancing the adaptability of CAs' language and content, integrating cultural awareness, improving explainability, managing cognitive load, and addressing bias and fairness concerns.

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