ReviewFrontiers in medicine2025
Addressing the current challenges in the clinical application of AI-based Radiomics for cancer imaging.
Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Application and integration of deep learning in tumour radiomics: bibliometrics and visualisation analysis.Frontiers in artificial intelligence · 2026Pooled it
- Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants.Dentistry journal · 2026Review
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Review
- Review
- Radiomics in Medical Imaging: Methods, Applications, and Challenges.Journal of imaging · 2026Review
- Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions.Health science reports · 2026Review
- Review
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Editorial: Advancing cancer imaging technologies: bridging the gap from research to clinical practice.Frontiers in oncology · 2026Article
- Multi-parametric MRI habitat radiomics with interpretable machine learning for early prediction of axillary lymph node metastasis in triple-negative breast cancer.Frontiers in medicine · 2026Article
- From Semantic Modeling to Precision Radiotherapy: An AI Framework Linking Radiobiology, Oncology, and Public Health Integration.Biomedicines · 2025Review
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Artificial intelligence in oncology: Current status and possibilities (Review).Medicine internationalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into Radiomics has transformed cancer imaging by enabling advanced predictive modeling, improved diagnostic accuracy, and personalized treatment strategies. However, the clinical application of AI-based Radiomics faces significant challenges that hinder its widespread adoption. Intrinsic limitations, such as limited datasets, data heterogeneity, and the lack of interpretability in AI models, compromise reliability and generalizability. Practical challenges, including integration into rigid clinical workflows, infrastructural constraints, regulatory barriers, and clinician training gaps, further complicate implementation. Addressing these barriers requires coordinated efforts to establish standardized imaging protocols, foster multi-institutional collaborations, and develop centralized repositories of diverse datasets. In addition, challenges programs for healthcare professionals and regulatory reforms are essential to build trust and streamline adoption. Future research should prioritize enhancing AI interpretability, conducting longitudinal studies to assess clinical impact, and incorporating patient-centered approaches to align AI models with precision medicine objectives. By overcoming these challenges, AI-based Radiomics can advance cancer imaging, improve patient outcomes, and contribute to a new era in personalized cancer care.
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.