Between Knowledge and Care: Evaluating Generative AI-Based IUI in Type 2 Diabetes Management Through Patient and Physician Perspectives
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911350798680064 |
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| author | Meng, Yibo Chen, Ruiqi Liu, Bingyi Guan, Yan Ding, Xiaolan |
| author_facet | Meng, Yibo Chen, Ruiqi Liu, Bingyi Guan, Yan Ding, Xiaolan |
| contents | Generative AI systems are increasingly used by patients seeking everyday health guidance, yet their appropriateness in chronic care contexts remains unclear. Focusing on Type 2 Diabetes Mellitus (T2DM), this paper presents a mixed-methods investigation into how AI-generated health information is interpreted by patients and evaluated by physicians in China. Drawing on formative patient grounding and a dimension-based physician evaluation, we examine AI responses along five quality dimensions: Accuracy, Safety, Clarity, Integrity, and Action Orientation. Our findings reveal that while current systems perform well in factual explanation and general lifestyle guidance, they frequently break down in safety signaling, contextual judgment, and responsibility boundaries, particularly when fluent responses invite overtrust. By treating quality dimensions as an interpretive lens rather than a fixed framework, this work highlights the need for intelligent user interfaces that actively mediate AI outputs in chronic disease management, supporting calibrated trust and responsible boundary-setting in long-term care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Between Knowledge and Care: Evaluating Generative AI-Based IUI in Type 2 Diabetes Management Through Patient and Physician Perspectives Meng, Yibo Chen, Ruiqi Liu, Bingyi Guan, Yan Ding, Xiaolan Human-Computer Interaction H.5.2; I.2.6; J.3 Generative AI systems are increasingly used by patients seeking everyday health guidance, yet their appropriateness in chronic care contexts remains unclear. Focusing on Type 2 Diabetes Mellitus (T2DM), this paper presents a mixed-methods investigation into how AI-generated health information is interpreted by patients and evaluated by physicians in China. Drawing on formative patient grounding and a dimension-based physician evaluation, we examine AI responses along five quality dimensions: Accuracy, Safety, Clarity, Integrity, and Action Orientation. Our findings reveal that while current systems perform well in factual explanation and general lifestyle guidance, they frequently break down in safety signaling, contextual judgment, and responsibility boundaries, particularly when fluent responses invite overtrust. By treating quality dimensions as an interpretive lens rather than a fixed framework, this work highlights the need for intelligent user interfaces that actively mediate AI outputs in chronic disease management, supporting calibrated trust and responsible boundary-setting in long-term care. |
| title | Between Knowledge and Care: Evaluating Generative AI-Based IUI in Type 2 Diabetes Management Through Patient and Physician Perspectives |
| topic | Human-Computer Interaction H.5.2; I.2.6; J.3 |
| url | https://arxiv.org/abs/2510.10048 |