Gray-zone decisions in subclinical hypothyroidism: a qualitative analysis of ChatGPT responses

The management of subclinical hypothyroidism can at times present a gray area for clinicians due to heterogeneous clinical features, variable TSH (Thyroid-Stimulating Hormone) thresholds, and differences in guideline interpretations. Although large language models (LLMs) have the potential to support clinicians in such uncertainty-laden clinical situations, the existing literature has focused almost exclusively on quantitative outcomes. However, how an LLM such as Chat Generative Pretrained Transformer (ChatGPT) structures a clinical decision, which thematic components it draws upon, and how it organizes its reasoning has not yet been qualitatively examined in the context of subclinical hypothyroidism scenarios. This study aims to fill this important gap in the literature by thematically analyzing how ChatGPT generates decision-making patterns in subclinical hypothyroidism cases.

Five standardized subclinical hypothyroidism scenarios, each detailing clinical features representative of the TSH 4-10 mIU/L range, were presented to ChatGPT. The responses were evaluated using a structured framework approach and reflexive thematic analysis. The resulting themes, their contents, and the corresponding clinical decision profiles were systematically compared.

Seven main themes emerged: guideline-based framework, individualized decision-making, trial treatment approach, risk stratification, avoidance of overtreatment, shared decision-making, and referral to endocrinology. These themes converged into three broader decision profiles: a guideline-based conservative profile, a risk-focused proactive profile, and a symptom-balanced shared-decision profile.

This study is among the first to qualitatively examine the decision-making processes of ChatGPT in gray-zone clinical conditions such as subclinical hypothyroidism. The decision patterns identified here provide a methodological foundation for future large-scale, comparative studies incorporating real patient data to evaluate LLM-based clinical decision support systems.

Not applicable.