Artificial intelligence chain-of-thought reasoning in nuanced medical scenarios: mitigation of cognitive biases through model intransigence
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View abstract on PubMed
Summary
This summary is machine-generated.Chain-of-thought reasoning large language models (LLMs) show persistent cognitive biases and produce inflexible recommendations in medical scenarios. Clinicians must remain vigilant when using AI for patient care.
Area Of Science
- Artificial Intelligence
- Medical Informatics
- Cognitive Science
Background
- Large language models (LLMs) are increasingly integrated into clinical decision-making.
- LLMs can exhibit human-like cognitive biases, impacting nuanced medical choices.
Purpose Of The Study
- To evaluate if chain-of-thought reasoning in LLMs can mitigate cognitive biases observed in physicians.
- To assess the extent of bias, intransigence, and deviance in LLM responses compared to human physicians.
Main Methods
- Ten medical scenarios were presented to LLMs (DeepSeek, OpenAI, Google) in biased versions.
- Bias was measured by response discrepancies; intransigence by Shannon entropy; deviance by comparison to 2507 physicians' responses.
Main Results
- One LLM mitigated 6 of 10 biases but produced intransigent responses.
- Specific biases (post hoc fallacy, decoy effects, Occam's razor, hindsight) persisted across models.
- LLM responses significantly deviated from practicing physicians' responses in all scenarios.
Conclusions
- Chain-of-thought reasoning LLMs still exhibit persistent cognitive biases and intransigent recommendations.
- Clinicians must critically evaluate AI outputs, considering potential biases in complex medical decisions.
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