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Enhancing Outcome Measurement in Oncology Clinical Trials Through Artificial Intelligence: A Scoping Review
Rachel Woodford1,2, Sally Lord1, Frank Lin3
1National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia.
JCO Clinical Cancer Informatics
|July 10, 2026
Summary
Artificial intelligence (AI) can improve oncology trials by optimizing patient selection and identifying surrogate endpoints. Further collaboration is needed to ensure AI
Area of Science:
- Oncology
- Clinical Trials
- Artificial Intelligence
Background:
- Oncology trial success rates remain low, with failures often occurring in late-phase studies due to methodological issues.
- Increasing tumor molecular subclassification challenges traditional drug development frameworks.
- Artificial intelligence (AI) presents opportunities to enhance oncology trial efficiency and precision.
Purpose of the Study:
- To provide a structured overview of AI applications in oncology trials, focusing on outcome selection and surrogate endpoint evaluation.
- To highlight emerging AI applications for accelerating drug development and improving clinical care.
Main Methods:
- Scoping review of AI applications in oncology clinical trials.
- Emphasis on outcome selection, surrogate endpoint evaluation, patient selection, biomarker identification, and synthetic control arms.
Main Results:
- AI, particularly deep learning, can analyze large datasets to uncover complex patterns for trial optimization.
- Potential AI applications include patient-trial matching, eligibility criteria refinement, survival outcome modeling, and novel surrogate endpoint identification.
- Emerging areas like patient selection, biomarker identification, and synthetic control arms show promise for immediate implementation.
Conclusions:
- AI offers significant potential to improve oncology trial design, evaluation, and drug delivery.
- Broader harmonization is required for AI implementation, ensuring reproducibility, transparency, and regulatory confidence.
- Sustained collaboration between trialists, AI developers, and regulators is essential for AI to deliver meaningful advances.
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