This retrospective feasibility study aimed to train and test an AI-based algorithm for automated detection of focal bone marrow lesions (FL) on MRI. 444 patients with monoclonal plasma cell disorders were included, focusing on FLs in the left pelvis. Using the nnDetection framework, the algorithm was trained on 334 patients with 494 FLs from center 1 and evaluated on an internal test set (36 patients, 89 FLs) and a multicentric external test set (74 patients, 262 FLs, centers 2–11). On the internal/external test sets, the algorithm achieved a mAP of 0.44/0.34, F1-score 0.54/0.44, sensitivity 0.49/0.34, and a PPV of 0.61/0.61. In two high-quality external subsets, performance approached that of the internal test set (mAP 0.45/0.41, F1- score 0.50/0.53, sensitivity 0.44/0.43, PPV 0.60/0.71). Automated and reference FL counts correlated significantly (internal r = 0.51, p = 0.001; external r = 0.59, p < 0.001). These results demonstrate the feasibility and multicentric robustness of automated FL detection and quantification from MRI. [1]