Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rajesh, M.; Prema, S.; Siva Balan, R.V.; Nilabar Nisha, U.; Chinnu, Silambarasu; Moorthy, Usha Title A name given to the resource Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Intelligence-Based Medicine;Volume;14;Issue;;Article No.;100395; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ibmed.2026.100395" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ibmed.2026.100395</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039937361?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039937361?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajesh M., Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation (DU), Paiyanur, Tamilnadu, Chennai, India; Prema S., Department of Computer Science, R.M.D. Engineering College, Kavaraipettai, Tamil Nadu, India; Siva Balan R.V., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Nilabar Nisha U., Department of Computer Science &amp; Engineering, Mahendra Institute of Technology, Namakkal, India; Chinnu S., Department of child health Nursing, Government College of Nursing Gorakhpur, BRD Medical College Campus, India; Moorthy U., School of Computer Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Bengaluru, Karnataka, Manipal, India Description An account of the resource Tumours are diverse and multiscale, making it difficult for modern medicine to diagnose early cancer. Using structured clinical data, radiologic imaging features, and liquid samples, this research presents a multimodal AI framework for the early and reliable detection of cancer. The proposed approach surpasses single-modality approaches by integrating signals from various domains, including cancer genetic, anatomical, and physiological data. Using attention-based fusion, representation learning, and better preprocessing, we developed a prediction model that fine-tuned the weights of different modes. The results of the experiments demonstrated that it outperformed unimodal models on all datasets in terms of sensitivity, specificity, and generalisation. The framework has potential for screening purposes because of its ability to detect cancer at an early stage. Clinical confidence and interpretability were both boosted by the results of explainability tests, which revealed substantial feature contributions. The suggested multimodal framework outperformed unimodal baselines across all assessment cohorts with an AUC of 0.94, sensitivity of 0.91, and specificity of 0.88. Experimental results confirm multimodal fusion's clinically interpretable early cancer detection and precision oncology decision assistance. Copyright 2026. Published by Elsevier B.V. Subject The topic of the resource AI explainability; Clinical decision support; Early cancer detection; Liquid biopsy; Multi-omics integration; Multimodal artificial intelligence; Precision oncology; Radiomics Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 26665212; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online