ReviewJournal of Zhejiang University. Science. B2025
Real-world data and evidence: pioneering frontiers in precision oncology.
Review in Journal of Zhejiang University. Science. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Large language models for breast cancer treatment planning: a blinded real-world evaluation of DeepSeek, ChatGPT, and oncologist recommendations.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Real-world studies (RWSs) have emerged as a transformative force in oncology research, complementing traditional randomized controlled trials (RCTs) by providing comprehensive insights into cancer care within routine clinical settings. This review examines the evolving landscape of RWSs in oncology, focusing on their implementation, methodological considerations, and impact on precision medicine. We systematically analyze how RWSs leverage diverse data sources, including electronic health records (EHRs), insurance claims, and patient registries, to generate evidence that bridges the gap between controlled clinical trials and real-world clinical practice. The review underscores the key contributions of RWSs, including capturing therapeutic outcomes in traditionally underrepresented populations, expanding drug indications, and evaluating long-term safety and effectiveness in routine clinical settings. While acknowledging significant challenges, including data quality variability and privacy concerns, we discuss how emerging technologies like artificial intelligence are helping to address these limitations. The integration of RWSs with traditional clinical research is revolutionizing the paradigm of precision oncology and enabling more personalized treatment approaches based on real-world evidence.
Indexed as
Identifiers
What OpenQuestion holds
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.