ReviewBJR artificial intelligence2025
General artificial intelligence for the diagnosis and treatment of cancer: the rise of foundation models.
Review in BJR artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- The Rise of Generalist Foundation Models and Quantum Computing in Oncology.Technology in cancer research & treatmentReview
- Assistive, not autonomous: Generative artificial intelligence in head and neck cancer care - A scoping review.Digital healthReview
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) and its computer algorithms, including those of machine learning and deep learning, have been transforming the oncology field. Generative AI (Gen AI) and general AI technologies, which can be adapted to various tasks based on diverse inputs, are being increasingly utilized in healthcare applications. Foundation models, driven by Gen AI and general AI technologies, have enthralled the medical world, representing the next generation of large-scale AI tools in medicine that are trained on massive amounts of data and will make a major impact due to their versatility, high performance, personalization enhancement, and assistance of healthcare workers. That said, it is undeniable that there are multiple obstacles that these algorithms must overcome to be successfully utilized in the field. In this review, we first present a brief background and a history of the recent rise of Gen AI and foundation models. Next, we explore their applications in the diagnosis and treatment of cancer, specifically in radiology, pathology, precision medicine, personalization of care, and surgical oncology. Then, we discuss some of the limitations that could hinder general and Gen AI's clinical translation.
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.