ArticleNEJM AI2025
The use of artificial intelligence for cancer therapeutic decision-making.
Article in NEJM AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Enhancing the quality and trustworthiness of large language model-generated summaries of clinical oncology literature.JAMIA open · 2026Article
- Review
- A context-augmented large language model for accurate precision oncology medicine recommendations.Cancer cell · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- AI Literacy Among Chinese Medical Students: Cross-Sectional Examination of Individual and Environmental Factors.JMIR medical education · 2026Article
- Prospective multi-centre evaluation of guideline-based artificial intelligence to streamline multidisciplinary tumour board.Frontiers in oncology · 2026Article
- Artificial intelligence-based miRNA analysis for precision oncology: diagnostic and prognostic insights.Frontiers in molecular biosciences · 2026Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Clinical Applications of Artificial Intelligence in Uveal Melanoma.Anticancer research · 2025Review
- REASONING BEYOND ACCURACY: EXPERT EVALUATION OF LARGE LANGUAGE MODELS IN DIAGNOSTIC PATHOLOGY.medRxiv : the preprint server for health sciences · 2025Article
- Artificial intelligence in oncology: Current status and possibilities (Review).Medicine internationalReview
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
Abstract
Artificial intelligence (AI) has the potential to transform cancer therapeutic decision-making by improving diagnostics and personalizing treatments. This review explores the current and future impact of AI in oncology, focusing on its applications in radiology, pathology, and the potential of Large Language Models (LLMs) in treatment selection. Despite significant advancements, AI integration into clinical workflows is limited due to challenges like data quality, model accuracy, and lack of validation through clinical trials. We propose key strategies to address these challenges, including developing robust multi-center datasets, promoting practical AI model development, researching workflow integration and human-AI collaboration, leveraging lessons from AI in medical imaging, establishing evaluation guidelines, and incentivizing prospective clinical trials. By implementing these strategies, AI can significantly enhance cancer care and patient outcomes, paving the way for its effective integration into oncology practice.
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.