ReviewFrontiers in cell and developmental biology2026
Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.
Review in Frontiers in cell and developmental biology, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer continues to be a leading cause of mortality worldwide, presenting substantial challenges to public health systems. The traditional approaches to cancer diagnosis and prognosis prediction exhibit certain limitations with respect to accuracy, comprehensiveness, dynamic monitoring, and personalization. With the advancement of artificial intelligence (AI) technologies, novel diagnostic and predictive methods are increasingly addressing these shortcomings. This review provides a comprehensive overview of the primary AI algorithms applied in oncology, including machine learning, deep learning, and large language models. It further examines the distinctive characteristics and appropriate use cases of AI algorithms, highlighting their specific roles in cancer screening, diagnostic accuracy, and outcome forecasting. Additionally, the review discusses emerging trends and persistent challenges, aiming to provide actionable insights that support clinical decision-making and advance scientific innovation in this rapidly evolving field. In conclusion, this review systematically outlines recent advances in AI applications for cancer diagnosis and prognostic prediction, with the objective of facilitating a transformative shift in oncology from experience-based practices toward data-driven precision medicine.
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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.