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Explainable AI-Based Semantic Retrieval from an Expert-Curated Oncology Knowledge Graph for Clinical Decision Support

Author

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  • Sameer Mushtaq

    (Department of Computer Science, Edge Hill University, Ormskirk L39 4QP, UK)

  • Marcello Trovati

    (Business School, University of Lancashire, Preston PR1 2HE, UK)

  • Nik Bessis

    (Department of Computer Science, Edge Hill University, Ormskirk L39 4QP, UK)

Abstract

The modern oncology landscape is characterised by a deluge of high-dimensional data from genomic sequencing, medical imaging, and electronic health records, negatively impacting the analytical capacity of clinicians and health practitioners. This field is not new and it has drawn significant attention from the research community. However, one of the main limiting issues is the data itself. Despite the vast amount of available data, most of it lacks scalability, quality, and semantic information. This work is motivated by the data platform provided by OncoProAI, an AI-driven clinical decision support platform designed to address this challenge by enabling highly personalised, precision cancer care. The platform is built on a comprehensive knowledge graph, formally modelled as a directed acyclic graph, which has been manually populated, assessed and maintained to provide a unique data ecosystem. This enables targeted and bespoke information extraction and assessment.

Suggested Citation

  • Sameer Mushtaq & Marcello Trovati & Nik Bessis, 2025. "Explainable AI-Based Semantic Retrieval from an Expert-Curated Oncology Knowledge Graph for Clinical Decision Support," Future Internet, MDPI, vol. 17(10), pages 1-25, October.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:10:p:471-:d:1772803
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