ReviewComputers in biology and medicine2019
Radiological images and machine learning: Trends, perspectives, and prospects.
Review in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 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
67 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Enhancing PET/CT Radiomics Robustness Through Graph Signal Processing.Diagnostics (Basel, Switzerland) · 2026Article
- Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer.NPJ precision oncology · 2026Article
- Clinical MRI-radiomics model based on support vector machine to predict the efficacy of first transarterial chemoembolization for unresectable intrahepatic cholangiocarcinoma.La Radiologia medica · 2026Article
- Automatic bone age assessment: a deep learning case study on the Brazilian population with a supporting mobile application prototype.Scientific reports · 2026Article
- Supervised-Learning-Driven Interrogation of Organ-on-a-Chip Quality from Microscopy Images.Chem & bio engineering · 2025Article
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- From reactive oxygen to cancer cell death: the science behind photodynamic therapy.Lasers in medical science · 2025Review
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Diagnostic value of neutrophil-to-lymphocyte ratio, fibrinogen-to-albumin ratio and red blood cell distribution width in tuberculosis combined with other bacterial infections.BMC pulmonary medicine · 2025Article
- Machine learning empowered coherent Raman imaging and analysis for biomedical applications.Communications engineering · 2025Review
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- How to select predictive models for decision-making or causal inference.GigaScience · 2025Article
- AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.Abdominal radiology (New York) · 2025Review
- Article
- 3D bone shape from CT-scans provides an objective measure of osteoarthritis severity: Data from the IMI-APPROACH study.Osteoarthritis imaging · 2024Article
- Review
- Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis.Imaging science in dentistry · 2024Review
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
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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.