While Large Language Models demonstrate expert proficiency on medical benchmarks, the clinical encounter requires more than factual retrieval. It demands a sophisticated rhetorical performance of care that balances authority with epistemic humility. This paper investigates the Clinical Fingerprint by comparing the structural and ethical integrity of advice generated by human physicians and various language models.Our findings reveal a fundamental divergence in how clinical information is prioritized and delivered. We show that whereas physicians utilize efficient, action-oriented structures to provide clear guidance, generic models often bury critical advice under layers of complex linguistic recursion. This creates a significant cognitive load for patients and risks a dangerous safety cliff where models adopt an unearned authoritative tone. Such models frequently mimic the confidence of a doctor while providing contradictory advice, particularly in complex cases involving multiple symptoms. By identifying these rhetorical gaps, our work emphasizes that domain-specific fine-tuning is an ethical necessity to ensure that AI assistants maintain the necessary humility and logical cohesion required for safe medical practice.

Large Language Models specialized for the medical domain achieve high performance on static benchmarks, but remain vulnerable to sycophantic confabulation, where the models generate medically spurious rationales to justify incorrect user hints. This robustness gap poses severe risks in clinical environments, as models may prioritize contextual faithfulness to a biased prompt over their internal parametric medical knowledge. This study introduces a mechanistic approach to identify and mitigate these failures in MedGemma-27B, isolating hint integration circuits using Sparse Autoencoders and geometric manifold analysis. Our findings reveal that sycophantic bias is a highly distributed and polymorphic concept, with biased reasoning routed through shifting dimensions across transformer layers. We identify the optimal layer for intervention and demonstrate that cluster-conditioned dynamic steering tailored to the geometric subspace of the prompt outperforms static global interventions, though it reveals a fundamental tension between bias resilience and the retention of internal parametric knowledge. This work proposes a principled framework toward clinical AI systems that are more robust and aligned with expert medical logic, demonstrating the potential of cluster-conditioned geometric interventions while characterizing the inherent trade-offs in clinical knowledge retention.