ReviewDiscover oncology2026
Artificial intelligence and transforming cancer care.
Review in Discover 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.
- Therapeutic potential and mechanisms of rehabilitation interventions in preventing cancer metastasis.Frontiers in oncology · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized approaches from diagnosis to treatment. This review explores the transformative role of AI across the cancer care continuum, highlighting its contributions, challenges, and future directions. AI has significantly advanced cancer detection and diagnosis by improving the interpretation of medical imaging (CT, MRI, PET scans, digital pathology) and liquid biopsies, allowing for early and accurate identification of tumors and biomarkers. In genomics and molecular profiling, AI facilitates the analysis of large-scale sequencing data to uncover actionable mutations and support targeted therapy decisions. This review also examines AI-powered prognostic models that integrate clinical, genomic, and electronic health record data to predict outcomes such as survival rates and recurrence risks, allowing for more precise treatment planning. In the therapeutic landscape, AI aids in optimizing radiation dosing, guiding surgical interventions, and predicting individual responses to chemotherapy, immunotherapy, and targeted treatments, thereby reducing uncertainty and improving outcomes. Key limitations, such as data privacy concerns, algorithmic bias, model opacity, and integration hurdles are discussed, along with strategies to address them, including explainable AI, standardized validation, and clinician training. Looking ahead, innovations like federated learning, generative AI for drug discovery, and multimodal data integration are poised to enhance precision oncology further. By synthesizing current developments and emerging trends, this review underscores the potential of AI to drive equitable, efficient, and personalized cancer care on a global scale.
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.