ReviewPhysica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)2021
Artificial intelligence and machine learning for medical imaging: A technology review.
Review in Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 6 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
122 citing papers in PubMed, 6 syntheses or guidelines pooled it, 411 citations in OpenAlex.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.PloS one · 2026Pooled it
- Artificial intelligence-based models for quantification of intra-pancreatic fat deposition and their clinical relevance: a systematic review of imaging studies.European radiology · 2026Pooled it
- A systematic literature review: exploring the challenges of ensemble model for medical imaging.BMC medical imaging · 2025Pooled it
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.Journal of imaging informatics in medicine · 2026Review
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
- Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community.Clinical and translational radiation oncology · 2026Article
- Video bronchoscope-assisted tracheal intubation in pediatric difficult airways: a single-center retrospective case series.World journal of pediatrics : WJP · 2026Article
- Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.Scientific reports · 2026Article
- Advancements in Ultrasound Gel Pad Technologies: Enhancing Diagnostic Precision, Procedural Efficiency, and Therapeutic Applications.Gels (Basel, Switzerland) · 2026Review
- Novel two-stage deep learning framework for automated pressure injury classification.BMJ health & care informatics · 2026Article
- Deep transfer learning for breast cancer detection in underserved regions.Frontiers in oncology · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Artificial Intelligence in Neuro-Ophthalmology for Optic Disc Pathologies and Neurodegenerative Disease.Eye and brain · 2026Review
- Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.International journal of biomedical imaging · 2026Review
- Artificial intelligence in gastric cancer research: a bibliometric and visualized analysis from 1993 to 2026.Frontiers in oncology · 2026Review
- AI Integration in Medical Imaging Education: A Nationwide Cross-Sectional Survey.Radiology research and practice · 2026Article
- On the translational potential of atlases in precision oncology.Translational cancer research · 2025Review
62 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
14 authors at 7 institutions in 3 countries.
Funding
Abstract
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
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.