ReviewBJC reports2025
Increasing use of artificial intelligence in genomic medicine for cancer care- the promise and potential pitfalls.
Review in BJC reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Towards convergence of AI and blockchain for personalized medicine in pharmacogenomics.Scientific reports · 2026Article
- Osteoarthritis and dementia: contrasting disorders driven by mutual pathways of autophagy, mTOR, GLP-1, AMPK, Wnt, and WISP1.Expert review of clinical pharmacology · 2026Review
- Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.BJC reports · 2026Review
- Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations.Diagnostics (Basel, Switzerland) · 2026Review
- From toxicogenomics to predictive toxicology and exposomics: defining the next decade of gene-environment research.Frontiers in genetics · 2026Article
- AI-Guided Discovery of Oncogenic Signaling Crosstalk in Tumor Progression and Drug Resistance.Oncology research · 2026Review
- Lost in .*VCF Translation. From Data Fragmentation to Precision Genomics: Technical, Ethical, and Interpretive Challenges in the Post-Sequencing Era.Journal of personalized medicine · 2025Review
- Review
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Therapeutic Vaccination in Lung Cancer: Past Attempts, Current Approaches and Future Promises.Journal of respiratory biology and translational medicine · 2025Article
- AI, agentic models and lab automation for scientific discovery - the beginning of scAInce.Frontiers in artificial intelligence · 2025Review
- Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of genomic medicine produces large datasets, which need to be rapidly analysed to produce clinically actionable insights in cancer care. Artificial intelligence thrives on data, processing and learning from datasets with a degree of accuracy and efficiency that traditional computing algorithms can not achieve. Based on a patient's genome sequence, AI could allow earlier detection of cancer, inform personalised treatment plans and provide insights into prognostication. However, this valuable tool is met with skepticism, with stakeholders concerned over data security, liability for AI's mistakes due to hallucination and the threat to clinical jobs. This review highlights both the benefits and potential problems of using AI in genomic medicine for cancer care, with the aim to lessen the knowledge gap between clinicians and data scientists and facilitate the future deployment of AI in cancer care.
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.