Aug 2026· Nature Reviews Clinical Oncology· 0 citations· 133 references
Medicine
TL;DR
It is argued that achieving the potential of AI in oncology clinical trials will require rigorous prospective validation, harmonized regulatory standards, and coordination among clinicians, trialists, regulators, industry and patients.
TrialGPT 2.0 is presented, an AI-assisted clinical trial recommendation system designed for real-world deployment that improves clinician efficiency and identifying frequently overlooked trial opportunities, ultimately helping to expand and accelerate accrual to cancer trials.
Yin Fang, Qiao Jin, Shubo Tian et al.· 0 citations
The barriers to clinical trial participation in pancreatic cancer across the patient pathway, from referral to enrolment are reviewed and coordinated improvements in referral, molecular testing, eligibility criteria, trial design, and delivery are identified as key strategies to improve equitable access to clinical tri...
Sue-Sien Thang, Daniel Croagh, Sumitra S. Ananda et al.· Communications Medicine· 0 citations
This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Kanishk Yadav, Taneesha Gupta· Journal of the Egyptian Nati...· 0 citations
A framework of emerging trial models that better align clinical testing with the realities of CNS tumors are examined, including decentralized trial models and artificial intelligence, which offer additional tools to broaden access, improve enrollment, and reduce operational burden.
A. J. Wisdom, R. Rahman· Neurotherapeutics· 0 citations
Challenges such as data quality, algorithmic bias, lack of transparency, privacy, cybersecurity, regulatory uncertainty and the need for human oversight remain important.
Rajashree Somnath Chorgade, Srushti Dipak Nevase, Shubham Hanumant Dhavale et al.· International Journal of Inn...· 0 citations
This narrative review outlines the limitations of traditional evaluation frameworks and proposes a paradigm shift toward adaptive, iterative, and context-specific assessment methodologies that can progress from a promising technology to reliable clinical tools that improve patient outcomes, support clinical decision-ma...
M. Fosset, Joris Pensier, Boris Jung et al.· PLOS Digital Health· 0 citations
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