Scale AI releases CliniCARE-Bench: clinical agents often answer correctly for the wrong reasons

Scale AI released CliniCARE-Bench, evaluating 16 agent systems on 25 clinical scenarios and 750 real cases from MIMIC-IV, with four-choice accuracy ranging from 76.1% at best to 65.3% at worst. The benchmark requires agents to investigate cases in a governed, fully logged tool environment and provide one of four conclusions: Yes, No, Indeterminate: Lack of Data, or Indeterminate: Medically Ambiguous. When only conclusions that are both correct and do not use shortcuts prohibited by the expert clinical committee are accepted, scores drop by 4.8 to 14.8 percentage points, and rankings change accordingly, with Gemini-3.1-Pro falling from 72.7% to 57.9%. All systems over-conclude, with rates between 21.3% and 55.3%, while over-abstention is only 7.3% to 16.5%. Code is open-sourced on GitHub, and the paper is on arXiv.