ReviewLife (Basel, Switzerland)2024
The Integration of Radiomics and Artificial Intelligence in Modern Medicine.
Review in Life (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
What it found
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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
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Application of immunoPET imaging to enhance head and neck squamous cell carcinoma clinical management.Frontiers in oncology · 2025Pooled it
- Neural Mechanisms and Brain Alterations in Orofacial Pain: A Narrative Review of Human Neuroimaging.Diagnostics (Basel, Switzerland) · 2026Review
- Apparent diffusion coefficient-based single-sequence radiomics integrated with clinical variables and Prostate Imaging Reporting and Data System for predicting clinically significant prostate cancer.Translational andrology and urology · 2026Article
- When intelligence meets radiology: the dual impact of ai on radiologists' workload, burnout, and economic value.Abdominal radiology (New York) · 2026Review
- Automated CT Pulmonary Angiography-Derived Periclot Radiomics for CTEPH Detection.Pulmonary circulation · 2026Article
- Multiparametric Abdominal Ultrasound as a Source of Imaging Biomarkers in Predictive and Personalized Medicine: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Toxicities of Antibody-Drug Conjugates in Breast Cancer: From Mechanistic Insights to Clinical Management.Pharmaceutics · 2026Review
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images.Journal of imaging informatics in medicine · 2026Article
- Advances in Biosimilars: A Systematic Review of Machine Learning Applications.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial intelligence and radiomics in drug-induced interstitial lung disease.ERJ open research · 2026Review
- Integrated use of natriuretic peptides and DASI for cardiac reserve evaluation in high-risk surgical patients: a narrative review.Journal of anesthesia, analgesia and critical care · 2026Review
- Metabolic [¹⁸F]F-AraG PET imaging of T Cell activation: a functional complement to cell-specific immune tracers.EJNMMI research · 2026Review
- Biomarkers of Treatment Response in Paediatric Medulloblastoma.Diagnostics (Basel, Switzerland) · 2026Review
- A Systematic Review of Deep Learning Approaches for Hepatopancreatic Tumor Segmentation.Journal of imaging · 2026Review
- Augmented Prediction of N Parameter in Breast Cancer: Is It Possible with Shear-Wave Elastography Ultrasound Radiomics?Cancers · 2026Article
- A Lung Ultrasound Radiomics-Based Machine Learning Model for Diagnosing Acute Heart Failure in the Emergency Department.Diagnostics (Basel, Switzerland) · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes.
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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.