Single-Pass Decision Scores Enable Rapid Classification with Adjustable Thresholds in Medical Multimodal Large Language Models

Multimodal large language models (MLLMs) answer binary clinical questions with a yes or no token, leaving the decision threshold implicit and fixed. We derive a continuous score from a single forward pass by renormalizing the probabilities of the yes and no tokens, allowing the threshold to be adapted to clinical needs without retraining. We evaluated this approach with MedGemma and Gemma-3 (4B and 27B) on six medical imaging datasets. The default threshold was frequently misaligned with the task: for identifying normal chest radiographs, MedGemma-27B labeled every image as abnormal, whereas a validation-selected threshold yielded a sensitivity of 0.945 and a specificity of 0.696. Normalization achieved the highest AUROC on four of six datasets (CheXpert: 0.819 vs. 0.777 for raw and 0.743 for text-elicited probabilities), and a single decision required 53-79 ms on one GPU. This simple post-hoc method turns medical MLLMs into fast classifiers with adjustable operating points. Code: https://github.com/TruhnLab/llm-threshold-calibration