ReviewOncogene2023
Translation of AI into oncology clinical practice.
Review in Oncogene, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.Briefings in functional genomics · 2025Pooled it
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- PROACTIVE-AI: An Intelligent Dashboard for Assessing and Predicting of FDA-approved AI Software Performance in Real-World Clinical Scenarios.Research square · 2026Article
- Review
- Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care.JMIR research protocols · 2026Article
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Innovative biomaterial solutions for enhancing the efficacy of tumor immunotherapy.Immunologic research · 2026Review
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Evaluation of an artificial intelligence model based on multiparametric transrectal ultrasound for localizing clinically significant prostate cancer by simulation of targeted biopsies.European radiology · 2026Article
- Integrating Blood-Based Immune-Inflammation Biomarkers into Artificial Intelligence-Driven Prognostic Models in Oncology.International journal of molecular sciences · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Overcoming domain-specific challenges for artificial intelligence in abdominal oncology toward clinical translation.Discover oncology · 2026Review
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- Determinants of acceptance and usage of generative AI among Chinese medical students: a UTAUT-based empirical investigation.Frontiers in psychology · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence.Nature reviews. Urology · 2026Review
- Exploring medical students' attitudes and perceptions toward artificial intelligence in medicine in Shandong Province, China.BMC medical education · 2025Article
- Transformer graph variational autoencoder for generative molecular design.Biophysical journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.
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.