Right Answer, Wrong Reason: Accuracy, Consistency, and Consensus Are Misleading Indicators of LLM Faithfulness in Clinical Decision Support

arXiv:2609.32817 · cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Consistency — 19× in this paper · explained in 3 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Accuracy — 9× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Self-consistency — 6× in this paper · explained in 2 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Causal Faithfulness Score — 3× in this paper
• Chain-of-Thought Prompting — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Explanation Stability Index — 2× in this paper
• Hallucination — 2× in this paper · explained in 3 episodes
• Perturbation Stability Score — 2× in this paper
• Abstention — 1× in this paper · explained in 1 episode
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Redundancy — 1× in this paper · explained in 2 episodes

Sources

• Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
• Measuring Faithfulness in Chain-of-Thought Reasoning
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations
• Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis

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