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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.