ReviewFrontiers in oncology2026
Research progress of artificial intelligence in bone tumor imaging.
Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Comparative evaluation of explainable vision models for bone tumour detection on radiographs.Frontiers in digital health · 2026Article
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
This paper reviews the research progress of artificial intelligence (AI) in bone tumor imaging and explores its potential applications in improving diagnostic accuracy and clinical management. Bone tumors, including primary and metastatic tumors, often face the risk of misdiagnosis due to their rarity and diverse imaging characteristics, which significantly impacts patient prognosis. AI technologies, particularly deep learning (DL) algorithms, have been widely applied to the automatic recognition and segmentation of bone tumor regions in images, enhancing the efficiency and accuracy of radiological image analysis. Furthermore, AI plays a crucial role in the classification of bone tumors and the assessment of treatment efficacy, providing support for the development of individualized treatment plans. With the continuous advancement of AI technology, future research should focus on expanding its applications across different types of bone tumors and integrating multimodal imaging data to further strengthen clinical decision-making and patient management.
Indexed as
Identifiers
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.