JMIR Publications

ABSTRACT

Background:

World Health Organization reports that chronic non-communicable diseases account for 74% of global deaths. Despite rapid advances in digital health technology, Artificial Intelligence tools for self-management remain deficient in two crucial elements: emotional connection with patients and trustworthiness. Concern around these two topics is of increasing interest and importance. With regards to trustworthiness of AI and emotional intelligence of AI however, the studies for these two concepts were developed completely separately and in an isolated manner and this in itself, is a considerable design gap. Within self-management for chronic conditions, it becomes necessary to build an approach to design with the integration of these two concerns.

Objective:

This systematic review aims to examine the extent to which the concepts of trustworthiness, emotional intelligence, situational awareness and personalization are integrated within artificial intelligence systems designed to facilitate self-management of chronic illness, and what the impact of integration is.

Methods:

From the beginning of February 2026, a thorough search was undertaken on 6 databases (PubMed, Scopus, IEEE Xplore, PsycINFO, Web of Science, and ACM Digital Library) along with connected papers, using Boolean strings linking together AI, chronic disease self-management, trust and emotional intelligence (tailored to each database's individual vernacular). Initial screening of identified articles was completed in two phases using the PRISMA 2020 criteria of pre-determined inclusion and exclusion criteria before critical appraisal using the MMAT v2018 and CASP tools.

Results:

After a systematic selection process of 1,486 studies, 45 studies were finally selected based on inclusion criteria. Four major theme areas emerged from the papers including: Technology in chronic illness self-management, Trust in human-AI interaction, Empathy and emotional intelligence in AI and The ethics, equity and ethical application of AI. The quality appraisal showed that 91% (41/45) of the selected studies were rated as high quality, with an average appraisal score of 93%. In addition, 73% (33/45) of the selected papers were published during 2024 and 2025 thus highlighting a high quality and contemporary compilation of literature on the subject.

Conclusions:

Despite advances in AI for chronic disease management and in trust-empathy theory, these fields remain siloed. We identify 5 critical research gaps; the lack of a combined trust-empathy model, the under-specification of context awareness, the absence of equity consideration, the exclusion of overtrust consideration, and lack of long-term studies demonstrating effectiveness and safety of emotional AI systems. Clinical Trial: Not registered. The review protocol was developed before the search but was not prospectively registered in PROSPERO or an equivalent database. This limitation is discussed in Section 4.4.