Clinician-LLM Error Overlap Undermines Second-Check Assumption

Health systems are integrating large language models into clinical workflows as a safety net, on the logic that a second automated reader can only catch mistakes. A new arXiv paper, Complementary Vulnerabilities, challenges that assumption: a second reasoner reduces error only insofar as it fails independently of the first. If clinician and model errors overlap, the extra check adds little protection.

The paper frames this as an error-overlap problem and argues for explicit auditing of where clinicians and LLMs make the same mistakes. It calls for governance that treats complementary failure modes as a precondition for safe deployment, rather than assuming any LLM second opinion is inherently beneficial.

Only this single source was provided, so there is no independent comparison to report. The available abstract lays out the core premise but is truncated, so the full methods and empirical findings are not covered here.