ReviewRecent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer2026
Image-Guided Radiooncology: The Potential of Artificial Intelligence in Clinical Application.
Review in Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Defining artificial intelligence (AI) remains a complex challenge, given its rapidly evolving nature. Nevertheless, one broadly accepted definition-endorsed by the European Union-describes AI as a suite of algorithms that learn from data to make predictions. Since the early 2010s, AI has emerged as a highly effective approach for analyzing, processing, and even generating medical image data.In this chapter, we provide an overview of key AI paradigms-radiomics, deep learning (DL), and foundation models-and examine their current and potential applications in medical image analysis within radiation oncology. We focus on topics, such as image classification, treatment planning, and response assessment, as well as novel strategies involving vision language models (VLMs). Through this exploration, we offer insights into how AI is transforming clinical workflows and shaping future directions in radiation oncology.
Indexed as
Identifiers
42165833What 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.