A conceptual explainable multi-modal breast cancer intelligence framework with feasibility analysis, computational characterisation, and clinical translation roadmap

Abstract

Breast cancer continues to be one of the highest killers among women in the world, therefore, there is a pressing need for intelligent clinical decision-support systems that are not only accurate, but also transparent, semantically interpretable and deployable in heterogeneous healthcare environments. Despite the recent progress with multimodal AI systems in enhancing diagnostic accuracy, current methods face limitations in the integration of heterogeneous data, their semantic reasoning, the lack of explainability, and collaborative learning limitations due to privacy concerns. To overcome these shortcomings, this research introduces the concept of OncoGraph-LLM, a conceptual multimodal explainable breast cancer intelligence framework that integrates deep imaging analysis, language intelligence, semantic knowledge representation, explainability by design and distributed privacy-preserving learning, in a single clinically-centric architecture. The framework leverages cooperative CNN–Vision Transformer feature extraction to utilize complementary local and global representations of images, the integration of contextual clinical information via ClinicalBERT and BioGPT, knowledge graph guided semantic inference to enhance reasoning, and an integrated explainability strategy that integrates feature attribution, semantic reasoning, uncertainty-aware interpretation, and clinician-oriented explanation generation to support transparent clinical decision-making. In addition, a federated learning architecture is implemented to support collaborative secure intelligence among distributed healthcare institutions without compromising patient privacy. To support the conceptual feasibility of the proposed framework, the design traceability, the computational complexity, deployment feasibility, technology readiness, comparative architectural analysis, and qualitative module contribution assessments are provided to illustrate the expected capabilities and the potential for translation of the integrated system. These analyses do not involve empirical performance testing, but offer a theoretical rationale for the proposed architecture, and highlight important implementation points. In conclusion, the proposed OncoGraph-LLM framework provides a holistic conceptual framework for reliable, interpretable, privacy-sensitive, and clinically actionable multimodal artificial intelligence for precision breast cancer diagnosis and decision-making.