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The integration of multimodal data sources in oncology, particularly imaging and electronic health records (EHRs), offers significant opportunities to advance precision medicine through sophisticated analytics architectures. This conceptual manuscript proposes a novel multimodal oncology integration framework (MOIF) to orchestrate seamless data fusion, analytical processing, and decision support within integrated imaging-EHR ecosystems. Drawing on theoretical foundations from clinical AI system architectures and healthcare analytics infrastructures, the framework emphasizes interoperability, governance, and monitoring to address challenges in data heterogeneity, privacy, and clinical workflow integration. By synthesizing recent literature on EHR intelligence ecosystems and decision support pipelines, we outline the architectural layers, including data ingestion, fusion, analytics, and feedback mechanisms, to enable real-time insights for oncology care. Conceptual formulas are introduced to model risk propagation, decision confidence, and governance load, providing interpretive tools for system dynamics. The architecture aims to enhance clinical decision-making by facilitating multi-modal data exchange and AI-driven analytics without empirical evaluations. Potential impacts include improved interoperability in oncology settings, reduced decision latency, and robust governance for deployment. This work contributes to the discourse on AI infrastructures in healthcare, offering a blueprint for future conceptual developments in integrated oncology ecosystems.

The rapid evolution of artificial intelligence (AI) in healthcare has paved the way for sophisticated systems aimed at enhancing early cancer detection across distributed clinical environments. This conceptual manuscript introduces the multi-center early detection orchestration network (MEDON), a novel intelligence architecture designed to integrate AI-driven analytics within multi-center screening ecosystems. MEDON conceptualizes a layered framework that facilitates seamless data interoperability, real-time decision support, and governance mechanisms to mitigate risks in federated healthcare settings. Drawing from theoretical foundations in clinical AI architectures and healthcare informatics, the architecture emphasizes modular components for intelligence orchestration, including adaptive monitoring pipelines and federated learning constructs without empirical validation. Key elements include interoperability frameworks for electronic health records (EHRs) and imaging data exchange, alongside governance models to ensure ethical deployment. The manuscript explores theoretical implications for workflow integration in screening programs, highlighting potential enhancements in detection sensitivity through conceptual risk propagation models and decision confidence formulas. By synthesizing recent literature on AI system architectures in oncology, this work proposes a blueprint for scalable, resilient intelligence ecosystems that could transform multi-center cancer screening paradigms. Ultimately, MEDON offers a theoretical pathway toward more equitable and efficient early detection strategies, addressing challenges in data silos and regulatory compliance across diverse clinical sites.

Oncology drug development is an expensive and high-failure process, with costs exceeding two billion dollars per approved drug and success rates below 10%. Deep learning has recently been explored as a strategy to improve efficiency across the drug discovery pipeline. This systematic review evaluates its application in target identification, compound screening and de novo drug design, and clinical trial optimization. Following PRISMA 2020 guidelines, multiple databases were searched and studies were screened using predefined inclusion criteria, with risk of bias assessed via established tools. The literature shows that graph neural networks and transformer-based models are the most widely used architectures, particularly in early-stage discovery tasks. Although many studies report strong in silico performance, often with AUC values above 0.80, only a small proportion demonstrate experimental or clinical validation. Overall, deep learning significantly advances computational drug discovery in oncology, but translation into clinically validated therapies remains limited, especially in trial optimization, highlighting the need for stronger prospective and experimental validation frameworks.