ReviewFrontiers in oncology2026
AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.
Review in Frontiers in oncology, 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer is one of the most common cancers in men, and its incidence has been increasing annually. Early screening, accurate diagnosis, and personalized treatment are crucial for improving patient prognosis. With technological advancements, artificial intelligence (AI) has been increasingly applied as a key tool in the diagnosis and treatment of prostate cancer. In the field of diagnosis, AI-driven multi-modal models that integrate imaging, pathological, and clinical data have enabled precise segmentation and grading of prostate cancer. This not only enhances diagnostic accuracy and efficiency but also reduces differences in judgments between medical professionals. In treatment, AI has been integrated into surgical procedures, radiation therapy, and targeted drug development to optimize treatment plans and explore relevant biological indicators and treatment targets. Although various AI models have demonstrated clinical value, most have not been put into practical clinical use, which highlights the significant limitations of current AI technology. This review focuses on the practical application outcomes and future development directions of AI in prostate cancer diagnosis and treatment. This study aims to explore the potential of AI in clinical practice, promote its deep integration with clinical work, and construct reliable and safe diagnostic and therapeutic models. These efforts are expected to alleviate the workload of medical professionals, improve diagnostic accuracy, facilitate personalized treatment planning, and ultimately enhance patient prognosis and quality of life. The trend of intelligent medicine should be embraced, and AI-assisted diagnostic and therapeutic technologies should be actively and rationally adopted to promote the advancement of medical care.
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What Socratic 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 Socratic graph.