Article
Details
Citation
Menon TP, Mahajan A & Powell D (2026) Foundation model embeddings for multimodal oncology data integration. npj Digital Medicine, 9 (1), Art. No.: 131. https://doi.org/10.1038/s41746-025-02312-8
Abstract
Cancer care generates vast quantities of data including clinical records, pathology images, radiology scans, and molecular profiles, yet these modalities are rarely integrated in a systematic,
automated manner within routine clinical workflows, remaining largely siloed across separate departmental and technical systems. Foundation model-driven embeddings¡ªor numerical
representations (vectors) that summarize complex data such as text, images ,and molecular profiles¡ªoffer a framework to integrate these data streams into unified patient representations. Here we
examine the HONeYBEE platform¡¯s approach to multimodal integration in oncology, situate it within broader developments in representation learning, and clinical and technical challenges that may.shape its path to implementation
Journal
npj Digital Medicine: Volume 9, Issue 1
| Status | Published |
|---|---|
| Publication date | 31/01/2026 |
| Publication date online | 31/01/2026 |
| Date accepted by journal | 21/12/2025 |
| URL | |
| Publisher | Springer Science and Business Media LLC |
| ISSN | 2398-6352 |
| eISSN | 2398-6352 |
People (1)
Lecturer in Public Health & Innovation, Health Sciences ÍæÅ¼½ã½ã