{"ID":5937896,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T05:39:36.778348344Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03720","arxiv_id":"2607.03720","title":"Between Knowledge and Care: A Mixed-Methods Evaluation of Generative AI for T2DM Self-Management from Patient and Physician Perspectives","abstract":"Generative AI is increasingly used for everyday health guidance, yet its clinical appropriateness in chronic disease contexts remains poorly understood. This paper presents a two-part mixed-methods study on \\revise{Type 2 Diabetes Mellitus (T2DM)}, examining how patients and physicians assess AI-generated health information. \\revise{Study~1} analyzes 784 \\revise{participant reported} patient queries to characterize seven informational need categories and \\revise{develops a structured five dimensional physician rating rubric informed by patient query categories and clinician priorities} (\\textit{Accuracy, Safety, Clarity, Integrity, Action Orientation}). \\revise{Study~2} engages seven physicians scoring responses from four AI models and discussing evaluative reasoning through in-depth interviews. Models perform well on factual explanation and lifestyle guidance but consistently underperform on medication reasoning and emotional support. Two \\revise{analytic concepts} emerge \\revise{from the data}. The \\textit{pre-visit primer} \\revise{frames AI as preparation for clinical encounters rather than as a replacement for physicians}. The \\textit{fluency illusion} \\revise{describes how polished language may convey epistemic authority that the clinical content does not support}. Patients and physicians converged on three shared limitations (role boundaries, emotional inadequacy, personalization gaps) while diverging in evaluative emphasis, \\revise{which informed} four design directions, task-aware orchestration, risk-aware fallback, dynamic personalization, and emotionally attuned interaction.","short_abstract":"Generative AI is increasingly used for everyday health guidance, yet its clinical appropriateness in chronic disease contexts remains poorly understood. This paper presents a two-part mixed-methods study on \\revise{Type 2 Diabetes Mellitus (T2DM)}, examining how patients and physicians assess AI-generated health inform...","url_abs":"https://arxiv.org/abs/2607.03720","url_pdf":"https://arxiv.org/pdf/2607.03720v1","authors":"[\"Ruiqi Chen\",\"Yibo Meng\",\"Huidi Lu\",\"Xiaolan Ding\"]","published":"2026-07-04T05:53:57Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}