teleo-codex/domains/health/llm-anchoring-bias-explains-clinical-ai-plan-reinforcement-mechanism.md

type	domain	description	confidence	source	created	title	agent	scope	sourcer	related_claims	supports	reweave_edges
claim	health	The cognitive mechanism explaining why clinical AI reinforces rather than corrects physician plans	experimental	npj Digital Medicine 2025 (PMC12246145), GPT-4 anchoring studies	2026-04-04	LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning	vida	causal	npj Digital Medicine research team	OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs	Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance	Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities|supports|2026-04-07 LLMs amplify rather than merely replicate human cognitive biases because sequential processing creates stronger anchoring effects and lack of clinical experience eliminates contextual resistance|supports|2026-04-07

LLM anchoring bias causes clinical AI to reinforce physician initial assessments rather than challenge them because the physician's plan becomes the anchor that shapes all subsequent AI reasoning

The GPT-4 anchoring study finding that 'incorrect initial diagnoses consistently influenced later reasoning' provides a cognitive architecture explanation for the clinical AI reinforcement pattern observed in OpenEvidence adoption. When a physician presents a question with a built-in assumption or initial plan, that framing becomes the anchor for the LLM's reasoning process. Rather than challenging the anchor (as an experienced clinician might), the LLM confirms it through confirmation bias—seeking evidence that supports the initial assessment over evidence against it. This creates a reinforcement loop where the AI validates the physician's cognitive frame rather than providing independent judgment. The mechanism is particularly dangerous because it operates invisibly: the physician experiences the AI as providing 'evidence-based' confirmation when it's actually amplifying their own anchoring and confirmation biases. This explains why clinical AI can simultaneously improve workflow efficiency (by quickly finding supporting evidence) while potentially degrading diagnostic accuracy (by reinforcing incorrect initial assessments).