ReviewNPJ precision oncology2026
The role of AI in oncology: present applications and future horizons.
Review in NPJ precision 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.
- The Rise of Generalist Foundation Models and Quantum Computing in Oncology.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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) offers a powerful means to accelerate precision oncology by individualizing care in an era of rapidly evolving treatment paradigms. However, there is limited regulatory oversights for safe clinical implementation of AI, and concerns surrounding data bias, ownership, and privacy have hindered broad integration into healthcare practice. In this perspective, we offer a forward-looking roadmap for the dissemination of AI in oncology. We discuss the role for AI in guiding biomarker-driven patient selection for clinical trials and in facilitating both personalized drug selection and de novo drug design by the potential to predict therapeutic response. We highlight the capabilities, and current limitations, of AI to inform realistic pathways for practical implementation.
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.