ArticleNPJ digital medicine2025
Continuous multimodal data supply chain and expandable clinical decision support for oncology.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- AI Agents for Multimodal Oncology Diagnosis: Toward Transparent and Traceable Clinical Decision Support.JMIR cancer · 2026Review
- Theranostic Smart Bionanosensors for Solid Tumors: Advances, Challenges, and Future Directions.Applied biochemistry and biotechnology · 2026Review
- Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.iScience · 2026Article
- Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition.NPJ digital medicine · 2026Article
- Application of the LightGBM algorithm in cross-border supply chain risk management: prediction and mitigation strategy development.Scientific reports · 2026Article
- Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance.Journal of medical systems · 2026Review
- Beyond prediction: AI as a mechanistic microscope and digital twin for colorectal cancer immunotherapy.Frontiers in immunology · 2026Review
- MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling.NPJ digital medicine · 2025Article
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- Using Artificial Intelligence to Develop Clinical Decision Support Systems-The Evolving Road of Personalized Oncologic Therapy.Diagnostics (Basel, Switzerland) · 2025Article
- Visual Perception and Pre-Attentive Attributes in Oncological Data Visualisation.Bioengineering (Basel, Switzerland) · 2025Review
- Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.Frontiers in medicine · 2025Review
- Evaluating the combined efficacy of Telisotuzumab Vedotin and artificial intelligence in the treatment of non-squamous non-small cell lung cancer: a narrative review focusing on pharmaceutical and technical insights.Frontiers in oncology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The study introduces a clinical decision support system (CDSS) developed at a single academic cancer center, integrating real-time clinical, genomic, and imaging data for over 170,000 patients across 11 cancer types. We have developed the Yonsei Cancer Data Library (YCDL) data integration framework to continuously collect and update multimodal datasets comprising over 800 features per case. Quality control measures, using 143 logical comparisons, addressed missing data and outliers, achieving median accuracies of 92.6% for surgical and 98.7% for molecular pathology. An Extract-Transform-Load (ETL) process with natural language processing transformed unstructured data, enabling survival analyses stratified by tumor stage, which revealed significant stage-dependent differences. The CDSS dashboard visualizes patient trajectories and key milestones. User feedback from oncology professionals showed strong acceptance, with satisfaction scores exceeding 4 out of 5. This framework demonstrates the potential of multimodal data integration to enhance clinical decision-making and patient outcomes, with future research needed to validate its generalizability and scalability.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.