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Recent advances in AI foundation models have significant potential forlightening the clinical workload by mimicking the comprehensive andmulti-faceted approaches used by medical professionals. In the field ofradiation oncology, the integration of multiple modalities holds greatimportance, so the opportunity of foundational model is abundant. Inspired bythis, here we present RO-LMM, a multi-purpose, comprehensive large multimodalmodel (LMM) tailored for the field of radiation oncology. This modeleffectively manages a series of tasks within the clinical workflow, includingclinical context summarization, radiation treatment plan suggestion, andplan-guided target volume segmentation by leveraging the capabilities of LMM.In particular, to perform consecutive clinical tasks without erroraccumulation, we present a novel Consistency Embedding Fine-Tuning (CEFTune)technique, which boosts LMM's robustness to noisy inputs while preserving theconsistency of handling clean inputs. We further extend this concept toLMM-driven segmentation framework, leading to a novel Consistency EmbeddingSegmentation~(CESEG) techniques. Experimental results including multi-centrevalidation confirm that our RO-LMM with CEFTune and CESEG results in promisingperformance for multiple clinical tasks with generalization capabilities.

End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding