ReviewESMO real world data and digital oncology2026
Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.
Review in ESMO real world data and digital oncology, 2026. 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.
- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug development in oncology is facing rising complexity, prolonged timelines, and increasing costs, with many trials failing to reach completion or secure regulatory approval for the investigated treatment. Against this backdrop, artificial intelligence (AI) and real-world data have emerged as promising tools to improve the efficiency and quality of clinical research. This narrative review explores how AI can support clinical trial design, conduct, and analysis. AI-driven methods can streamline information gathering, optimize eligibility criteria, and predict trial success, thereby reducing costly failures. Patient recruitment and retention, often the most challenging aspects of oncology trials, may benefit from AI-supported matching algorithms, digital health technologies, and personalized interactions. During trial conduct, natural language processing and sensor-based monitoring offer opportunities to reduce administrative burden and capture real-world, patient-centred outcomes. For trial analyses, AI enhances radiology, digital pathology, pharmacometrics, and multimodal modelling, enabling more accurate prognostic and predictive insights. In addition, AI-based simulations, such as digital twins and
Indexed as
Identifiers
What Socratic 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 Socratic graph.