Language model (LM) capabilities have rapidly advanced over the past decade, driving their adoption across fields, including biomedical research. While domain-specific pretrained language models (PLMs) have long supported biomedical NLP, recent focus has shifted to medical large language models (LLMs) and their language understanding capabilities. In this paper, we investigate this capability by probing the medical conceptual alignment of various LMs against clinical expertise for identifying hallmarks of immunotherapy in breast cancer abstracts. Our results show a clear trade-off: LLMs achieve stronger performance and higher consistency on high-level concepts, whereas PLMs are more reliable for fine-grained, low-level concept identification. We find that hallucinations are the primary factor limiting LLM performance in these settings; once mitigated, LLMs surpass PLMs across all tiers of biomedical concepts. Despite this, PLMs remain competitive, particularly in closely aligning with medical expert judgments on detailed conceptual distinctions.