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The integration of deep learning into clinical decision infrastructure represents a pivotal advancement in healthcare systems and analytics, transforming disparate data streams into actionable intelligence that supports real-time, evidence-based decision-making. This narrative review synthesizes peer-reviewed literature to examine the systems-oriented implications of deep learning deployment within healthcare ecosystems. We focus on the architectural interplay among data ingestion, model inference, and decision-support loops, emphasizing how these elements enable closed-loop systems that adapt to evolving clinical contexts.Deep learning’s capacity to process multimodal data—encompassing electronic health records (EHRs), medical imaging, and real-time monitoring—has enabled sophisticated analytics frameworks that enhance diagnostic accuracy, prognostic modeling, and therapeutic optimization. For instance, fusion techniques combining imaging with structured EHR data have demonstrated potential for precision health applications, enabling nuanced patient stratification and personalized interventions. In mental health, deep learning models applied to outcome research have revealed patterns in longitudinal data, informing system-wide analytics that bridge predictive modeling with clinical workflows.From a systems perspective, the review highlights the evolution of clinical decision support systems (CDSS) augmented by deep learning, which incorporate feedback mechanisms to refine model performance and mitigate risks such as bias amplification. Ethical considerations, including algorithmic fairness and transparency, are integral to sustainable integration, as underscored by guidelines for early-stage evaluation and reporting standards. We explore architectures that facilitate human-AI collaboration, where deep learning serves as an augmentative tool rather than a replacement, ensuring alignment with clinical governance.Challenges in scalability, such as interoperability across healthcare infrastructures and the need for reproducible machine learning pipelines, are critically analyzed through a lens of systems resilience. The synthesis reveals opportunities for closed-loop systems that iteratively learn from interventions, promoting adaptive healthcare delivery. Ultimately, this review posits that deep learning’s role in clinical decision infrastructure hinges on holistic systems design that balances technological innovation with clinical utility and equity. By providing an original interpretive framework, we delineate pathways for integrating deep learning into healthcare analytics and advocate for governance models that prioritize patient-centered outcomes.

Immunotherapy with immune checkpoint inhibitors is a standard treatment for advanced non-small cell lung cancer (NSCLC), with durable responses in selected patients. Whole-slide histopathology images provide morphological and immune microenvironment information, while genomic expression data capture pathway activity and resistance mechanisms. Single-modality approaches based on either histopathology or genomics fail to capture complementary tumor information, limiting accurate stratification of responders and non-responders and leading to suboptimal treatment selection. We propose a multimodal fusion network that integrates whole-slide histopathology images and genomic expression data to predict immunotherapy response in NSCLC. Separate encoders process each modality, followed by cross-attention for joint representation learning in an end-to-end framework. The system includes a multiple instance learning-based WSI module, a gene expression encoder with attention over gene sets, and a cross-attention fusion module. The model outputs a binary or probabilistic prediction of treatment response using paired slide and genomic data. The model captures complementary morphological and molecular signals, linking immune infiltration patterns with transcriptomic activity. Attention mechanisms enhance interpretability by highlighting key tissue regions and gene pathways, while also improving robustness to partial modality missingness. This multimodal framework improves NSCLC immunotherapy response prediction by integrating histopathology and genomic data, offering a step toward more precise patient stratification in precision oncology.