The emergence of foundation models has marked a transformative shift in AI, enabling robust generalization across diverse downstream tasks through putative zero-shot learning. Large Language Models and Vision-Language Models have demonstrated strong capabilities in tasks such as image interpretation, report generation, and question answering by effectively learning from multimodal data - images paired with associated text - often with minimal supervision. In the healthcare domain, this ability to align visual and textual information reduces the reliance on extensive manual annotations, as models can leverage existing clinical reports and imaging data to learn meaningful representations. This integration holds promise for improving diagnostic support, treatment planning, and overall patient care, even in data-constrained settings. In this review, we provide a definitive taxonomy of the medical VLM landscape, tracing the evolution from early Contrastive Alignment and Generative MLLMs to the cutting-edge frontiers of Dense Pixel-Grounding, Sparse Mixture-of-Experts (MoE), and Reasoning-Incentivized (RL) architectures. We critically examine the "medical bottleneck"-identifying the persistent challenges of data scarcity, the "hallucination" risks in generative diagnostics, the computational strain of 3D volumetric processing, and the lack of standardized, clinically-grounded evaluation metrics.

Paired biomedical assays increasingly measure different molecular or clinical views from the same sample. The statistical problem is simple to state but hard to solve: the views often have different dimensions, noise models, and dynamic ranges, yet downstream analysis requires a common representation. Single-cell CITE-seq is a useful example because transcript counts and surface-protein abundances are observed in the same cell. Existing paired-omics methods, including probabilistic models, matrix-factorization approaches, and neural fusion models, have addressed this setting with different assumptions. Fewer studies, however, have asked whether a symmetric contrastive objective can align the two views while retaining modality-specific signal through reconstruction. We present scCLIP, a contrastive masked-reconstruction framework for paired single-cell multi-omics integration. scCLIP trains RNA and ADT branches jointly with a bidirectional cross-modal contrastive loss and masked reconstruction losses. The branches use the same architectural template but retain separate input/output adapters, encoder-decoder parameters, and projection heads; the projected embeddings are compared in an ℓ2-normalized space with a learnable logit scale. We evaluate scCLIP on five paired RNA-protein datasets, where it is compared against TotalVI, BREMSC, jointDIMMSC, scMM, and SCOIT and achieves the highest ARI and FMI on every dataset (with TotalVI second-best overall and marginally higher NMI on three of the five datasets), and on the larger NeurIPS 2021 BMMC CITE-seq benchmark (90,261 cells, 134 proteins, 45 cell types) where scCLIP scales without architectural change and produces strong batch mixing on a 12-batch dataset. We additionally provide direct evidence of RNA-ADT alignment through retrieval and distance-based alignment metrics, and report standard batch-mixing scores on the NeurIPS embedding. The results support scCLIP as a reusable paired-view representation-learning template, with RNA-protein integration serving as the primary empirical testbed. The source code and an end-to-end tutorial for applying scCLIP to new CITE-seq data are publicly available at https://github.com/xubohao39-cyber/scCLIP.

Accurate prediction of drug-target interactions (DTI) is essential for drug discovery. Despite the success of pre-trained language models (PLMs) in learning robust molecular and protein representations, a fundamental challenge remains in characterizing the fine-grained, localized biochemical interactions between drug substructures and protein binding sites. Such critical interaction patterns are often underrepresented in conventional global embedding approaches, thereby limiting both predictive accuracy and biological interpretability. To address this challenge, we propose CoAff-DTI, an end-to-end deep learning framework designed to enhance multi-scale interaction modeling for DTI prediction. The model introduces three key components. First, a token-level decomposition strategy is employed to transform global embeddings into pharmacophore- and residue-level representations, facilitating the capture of localized features. Second, an Affinity-Guided Cross-Attention (AGCA) module is designed to explicitly model fine-grained interactions between ligand substructures and protein residues. Third, an Affinity-Gating Fusion (AGF) module is proposed to enhance cross-modal feature integration by dynamically modeling element-wise interactions. Extensive experiments on multiple benchmark datasets demonstrate that CoAff-DTI consistently outperforms state-of-the-art methods. In addition, attention-based visualization results suggest improved interpretability, as the model's learned attention patterns align effectively with experimentally verified binding regions.