Paper Number
ECIS2026-2070
Paper Type
SP
Abstract
Artificial Intelligence (AI) is reshaping clinical and self–management procedures in healthcare, raising the question of how digital technologies can support or undermine compassionate care. While the focus of prior research on digital empathy and affective computing is on detecting or simulating empathic cues, it often neglects the broader socio–technical mechanisms through which compassion occurs. Addressing this gap, this study develops a conceptual framework of compassion in AI–mediated healthcare based on an interpretive synthesis of 43 studies. Five dimensions of compassion are identified and linked to specific AI mechanisms. The findings reveal that while AI strongly supports perceptual and relational aspects of compassion, it weakly supports ethical deliberation and context–sensitive action. This places compassion as an emergent outcome of socio–technical configurations rather than a feature of AI systems. The framework provides a structured basis for analyzing and designing compassionate human–AI interactions in healthcare.
Recommended Citation
Bagheri, Samaneh, "Compassionate AI In Healthcare: A Socio-Technical Perspective" (2026). ECIS 2026 Proceedings. 13.
https://aisel.aisnet.org/ecis2026/hit/hit/13
Compassionate AI In Healthcare: A Socio-Technical Perspective
Artificial Intelligence (AI) is reshaping clinical and self–management procedures in healthcare, raising the question of how digital technologies can support or undermine compassionate care. While the focus of prior research on digital empathy and affective computing is on detecting or simulating empathic cues, it often neglects the broader socio–technical mechanisms through which compassion occurs. Addressing this gap, this study develops a conceptual framework of compassion in AI–mediated healthcare based on an interpretive synthesis of 43 studies. Five dimensions of compassion are identified and linked to specific AI mechanisms. The findings reveal that while AI strongly supports perceptual and relational aspects of compassion, it weakly supports ethical deliberation and context–sensitive action. This places compassion as an emergent outcome of socio–technical configurations rather than a feature of AI systems. The framework provides a structured basis for analyzing and designing compassionate human–AI interactions in healthcare.
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