Auditing Multimodal LLM Raters: Central Tendency Bias in Clinical Ordinal Scoring
This work identifies and characterizes a critical bias in LLM-based clinical ordinal scoring, highlighting the need for calibration-aware evaluation before deployment in high-stakes screening workflows.
The paper benchmarks multimodal LLMs against supervised models for scoring Clock Drawing Test images, finding that while LLMs achieve competitive tolerance-based agreement (GPT-5 within-1 accuracy 92%), they exhibit a systematic central tendency bias that compresses predictions toward the middle of the scale, disproportionately affecting clinically critical extreme scores.
Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal clinical scales remains poorly understood. We benchmark three frontier LLM families against supervised deep learning models for scoring Clock Drawing Test (CDT) images on two public datasets using the Shulman rubric. While fully fine-tuned Vision Transformers achieve the best calibration (MAE 0.52, within-1 accuracy 91%), zero-shot LLMs remain competitive on tolerance-based agreement (GPT-5 MAE 0.67, within-1 accuracy 92%) despite higher absolute error. However, per-score analysis reveals that all three LLM families exhibit a pronounced central tendency effect (systematic endpoint compression): predictions are systematically compressed toward the middle of the scale, with over-prediction at the low end (score 0 to 1) and under-prediction at the high end (score 5 to 4). This effect disproportionately affects the clinically critical extremes where accurate scoring most impacts screening decisions for cognitive impairment. Targeted ablations show that neither few-shot exemplars spanning the full score range nor removing clinical terminology from the prompt eliminates the effect. Our findings extend the LLM-as-a-judge bias literature from NLP evaluation to clinical assessment, and highlight the need for calibration-aware evaluation and post-hoc calibration before deploying LLM-based raters in high-stakes screening workflows.