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

Sameer Mushtaq, Marcello Trovati () and Nik Bessis
Additional contact information
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

Future Internet, 2025, vol. 17, issue 10, 1-25

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.

Keywords: artificial intelligence; machine learning; deep learning; digital health (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/10/471/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/10/471/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:10:p:471-:d:1772803

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().