ReviewESMO real world data and digital oncology2025
Real-world radiology data for artificial intelligence-driven cancer support systems and biomarker development.
Review in ESMO real world data and digital oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Real-World Imaging Data: Opportunities and Challenges.JMIR medical informatics · 2026Article
- Cross-dataset adaptation of voxel-level deep radiomics for predicting survival in inoperable locally advanced NSCLC treated with immunotherapy.Frontiers in immunology · 2026Article
- Transforming clinical reasoning-the role of AI in supporting human cognitive limitations.Frontiers in digital health · 2025Review
- An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) and real-world data (RWD) opens up a new paradigm for exploiting radiology data to develop advanced diagnostic and therapeutic support systems. This review explores the advantages and challenges of utilizing vast digital image datasets from routine clinical practice and computational AI capabilities to enhance cancer patient care. Particularly, the application of AI to radiology data has shown promise in developing tools that automate clinical processes, such as tumor detection, while also identifying novel biomarkers in cancer for potential treatment support. Deep learning models, crucial for this transformation, require substantial data, making RWD a valuable resource for accelerating assay development. RWD offer diverse, extensive data reflecting real-world clinical practices, complementing clinical trial data and providing a broader understanding of patient populations and treatment responses. However, challenges such as data access, variability in quality, and processing complexities must be addressed. Standardizing data processing protocols and feature extraction methods is essential to ensure reproducibility and clinical applicability. Moreover, building trust among clinicians, patients, and regulatory bodies is crucial for successful implementation. This review highlights the potential of AI to analyze RWD imaging data and radiology reports, extracting relevant information and enhancing biomarker discovery. To facilitate practical use, we offer tools to address the main challenges associated with utilizing real-world imaging data, such as key aspects of image access and data processing.
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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.