Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (726 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Examination of any part of the body for diagnostic purposes by means of x-rays or gamma rays, recording the image on a sensitized surface (such as photographic film).
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Diagnostic X-Ray; Diagnostic X Ray; Diagnostic X-Rays; X-Rays, Diagnostic; X-Ray, Diagnostic; X Ray, Diagnostic; Diagnostic X-Ray Radiology; Diagnostic X Ray Radiology.
Where it fits in the MeSH classification
- Broader topics
- Diagnostic Imaging
- Narrower topics
- Absorptiometry, Photon; Age Determination by Skeleton; Angiography; Arthrography; Electrokymography; Fluoroscopy; Hysterosalpingography; Lymphography; Mammography; Microradiography; Neuroradiography; Pneumoradiography; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted
- See also
- Contrast Media; Extravasation of Diagnostic and Therapeutic Materials; Technology, Radiologic
Related concepts
- X-Rays
- Gamma Rays
Research landscape
PubMed indexes 1,288,422 records tagged with the MeSH heading “Radiography” (counts retrieved 2026-11-04). 181,763 of them (14.1%) were published from 2020 onward, and the busiest period so far is through 1999 with 510,313 records.
Reviews account for 107,030 records (8.3%) and meta-analyses for 5,272. Randomized controlled trials number 21,165 (1.6%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 510,313 |
| 2000-2009 | 247,021 |
| 2010-2019 | 349,325 |
| 2020 onward | 181,763 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 107,030 | 8.3% |
| Meta-analyses | 5,272 | 0.4% |
| Randomized controlled trials | 21,165 | 1.6% |
Method: PubMed query "Radiography"[MeSH Terms] with publication-type and date filters, retrieved 2026-11-04.
Reading the evidence in diagnostics and medical technology
A test or device is judged by how well it detects a condition, how often it gives false alarms or misses cases, and whether using it improves outcomes for people. Accuracy depends on who is tested and how common the condition is, so a test that performs well in a specialist hospital can perform differently in the community. Check the reference standard, the population studied, the sensitivity and specificity and whether results were confirmed in a separate group.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning | 2026 | Research article Design not recorded in structured metadata | 6 | cc by PMID 42754575 · DOI · PMC13586212 |
| An institutional experience of a scripting-driven automation of the emergent palliative radiotherapy workflow | 2026 | Research article Design not recorded in structured metadata | 1 | cc by PMID 42775593 · DOI · PMC13598930 |
| Incidental findings from panoramic radiographs: A systematic review with meta-analysis | 2026 | Meta-analysis Design not recorded in structured metadata | 1 | cc by PMID 42782538 · DOI · PMC13612610 |
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Foundational artificial intelligence models are gaining traction in various applications, including medical fields like radiology.
- Closing excerpt
- With RayDINO and small task-specific adapters, we reach state-of-the-art results and improve generalization to unseen populations while mitigating bias, illustrating the versatility and robustness of foundation models.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (138 words)
Foundational artificial intelligence models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, leaving their generalisability and biases unexplored. Here we introduce RayDINO, a large-scale self-supervised visual encoder for chest X-rays trained on 840,000 images and evaluated on 82,000 images sourced from 12 publicly available datasets. We compare RayDINO to previous state-of-the-art models across nine radiology tasks, from classification and dense segmentation to text generation, and provide an in-depth analysis of population, age and sex biases of our model. Our findings suggest that self-supervision enables patient-centric analysis useful in clinical workflows, allowing for a more holistic interpretation of X-rays. With RayDINO and small task-specific adapters, we reach state-of-the-art results and improve generalization to unseen populations while mitigating bias, illustrating the versatility and robustness of foundation models.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42754575 · DOI · PMC13586212
An institutional experience of a scripting-driven automation of the emergent palliative radiotherapy workflow [2]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundEmergency palliative radiotherapy is often delivered under time-constrained and high-stress conditions, frequently bypassing conventional computed tomography (CT) simulation and relying on nonstandard workflows.
- Closing excerpt
- By automating deterministic planning tasks and preserving human oversight for clinical decision-making, the proposed approach improves consistency and staff confidence and has potential to improve safety and accuracy of palliative treatments.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (317 words)
BackgroundEmergency palliative radiotherapy is often delivered under time-constrained and high-stress conditions, frequently bypassing conventional computed tomography (CT) simulation and relying on nonstandard workflows. These factors increase variability and the potential for error.PurposeThis work describes the development, implementation, and early clinical evaluation of a simulation-free, cone-beam CT (CBCT) based emergency radiotherapy workflow that leverages treatment planning system (TPS) scripting to standardize and automate deterministic steps while preserving clinical oversight.MethodsAn ESAPI scripting-driven emergency workflow was developed within the TPS to automate prescription handling, plan generation, beam configuration, dose calculation, and setup preparation using CBCT images. Safety checks were implemented in the form of prompts with the appropriate actions required. Alternatively, manual input was retained for image verification and treatment field borders definition. The workflow was implemented across multiple clinical sites within a single healthcare system and evaluated through dosimetric comparison of CT- and CBCT-based planning. Metric parameters such as workflow time, reductions in manual interactions, and pre- and post-training survey outcomes of automated 3D workflow were compared to the same parameters of the conventional 2D workflow.ResultsThe automated workflow successfully generated clinically acceptable emergency treatment plans with minimal manual input. CBCT-based dose calculations demonstrated agreement with CT-based planning within 2.5% when heterogeneity corrections were enabled and within 4.7% when disabled. Automation eliminated 33 manual data entry steps present in the 2D workflow and enabled treatment delivery with average on-table time of approximately an hour. Survey results demonstrated strong staff support for automation, with over 90% anticipating improvement prior to training and 100% reporting perceived improvement following training. Staff comfort and confidence improved post-training, and concerns regarding workflow clarity and safety decreased.ConclusionsA scripting-based, simulation-free emergency radiotherapy workflow was developed for safe and standardized palliative treatments while maintaining established clinical safeguards. By automating deterministic planning tasks and preserving human oversight for clinical decision-making, the proposed approach improves consistency and staff confidence and has potential to improve safety and accuracy of palliative treatments.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42775593 · DOI · PMC13598930
Incidental findings from panoramic radiographs: A systematic review with meta-analysis [3]
Abstract reading summary — source excerpts
- Opening excerpt
- PurposeThe use of radiographs is widely spread, both among general dentists and specialists, since it provides a view of the oral and perioral tissues using a relatively easy-to-acquire and low-dose image.
- Closing excerpt
- When a dental panoramic image is justified, it should be adequately assessed for incidental findings by either a specialist oral and maxillofacial radiologist or a dentist with appropriate training and experience.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (271 words)
PurposeThe use of radiographs is widely spread, both among general dentists and specialists, since it provides a view of the oral and perioral tissues using a relatively easy-to-acquire and low-dose image. The aim of this systematic review was to critically appraise and summarize evidence from clinical studies on the prevalence of incidental findings from panoramic radiographs.MethodSystematic literature searches without restrictions were undertaken in eight databases from inception up to January 2026 for studies reporting on incidental findings from panoramic radiographs of any dental patients. After duplicate study selection, data extraction, and risk-of-bias assessment using a custom tool based on the Joanna Briggs Institute's tool for prevalence studies, random-effects meta-analyses were performed, followed by subgroup, meta-regression, and sensitivity analyses.ResultsTwenty-five studies covering a total of 66 882 patients (44.1% male; average age 28.9 years) were included. The prevalence of incidental findings ranged from 5.2% to 88.1%, with average distribution prevalences between 22.9% and 53.1%, but heterogeneity was high across studies. Incidental findings were most often related to airways (30.9%), followed by teeth (12.2%), soft-tissue calcifications (9.4%), spine (7.4%), radiopacities (7.1%), and temporomandibular joint (1.8%). However, several methodological issues were present in the included studies (incomplete reporting of patient- or radiography-related details, incomplete categorization and reporting of the severity of findings, small sample sizes, and issues with research transparency).ConclusionEvidence indicates that incidental findings are common in panoramic radiographs of dental patients. Although many incidental findings are clinically insignificant, some may have important implications. When a dental panoramic image is justified, it should be adequately assessed for incidental findings by either a specialist oral and maxillofacial radiologist or a dentist with appropriate training and experience.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42782538 · DOI · PMC13612610
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis [4]
- Research article Association of Pericoronary Adipose Attenuation With Major Adverse Cardiovascular Events and High-Risk Plaque [5]
- Research article Evolution of deep learning tooth segmentation from CT/CBCT images: a systematic review and meta-analysis [6]
- Research article Advanced Analyses of Coronary Computed Tomography Angiography to Predict Future Cardiac Events: A Meta-Analysis [7]
- Research article Coronary Artery Calcium Density and Risk of Cardiovascular Events: A Systematic Review and Meta-Analysis [8]
- Research article Peri-ictal imaging abnormalities in non-convulsive status epilepticus: A systematic review and meta-analysis comparing magnetic resonance imaging and computed tomography perfusion [9]
References
- Moutakanni T, Bojanowski P, Chassagnon G, Hudelot C, Joulin A, LeCun Y, et al.. Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning. . 2026. PMID 42754575 · DOI · PMC13586212
- Momin S, Schreibmann E, Roper J, Ghavidel B, Washington M, Kayode O, et al.. An institutional experience of a scripting-driven automation of the emergent palliative radiotherapy workflow. . 2026. PMID 42775593 · DOI · PMC13598930
- Imboden-Ilia A, Awaad H, Inglezos E, Eliades T, Papageorgiou SN. Incidental findings from panoramic radiographs: A systematic review with meta-analysis. . 2026. PMID 42782538 · DOI · PMC13612610
- Khubrani YH, Thomas D, Slator PJ, White RD, Farnell DJJ. Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis. Dentomaxillofac Radiol. 2025. PMID 39656957 · DOI · PMC11979759
- Tan N, Marwick TH, Dey D, Chan W, Nerlekar N. Association of Pericoronary Adipose Attenuation With Major Adverse Cardiovascular Events and High-Risk Plaque. JACC Cardiovasc Imaging. 2025. PMID 40643422 · DOI
- Kot WY, Au Yeung SY, Leung YY, Leung PH, Yang WF. Evolution of deep learning tooth segmentation from CT/CBCT images: a systematic review and meta-analysis. BMC Oral Health. 2025. PMID 40420051 · DOI · PMC12107724
- Bianchini E, Alqahtani F, Alsubai S, Del Sole PA, Elzomor H, Sharif R, et al.. Advanced Analyses of Coronary Computed Tomography Angiography to Predict Future Cardiac Events: A Meta-Analysis. JACC Cardiovasc Imaging. 2025. PMID 40758074 · DOI
- Yong Y, Giovannucci J, Pang SN, Hong W, Han D, Berman DS, et al.. Coronary Artery Calcium Density and Risk of Cardiovascular Events: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging. 2025. PMID 39243235 · DOI
- Bosque-Varela P, Kuchukhidze G, Hahn W, Jünemann C, Machegger L, Möller L, et al.. Peri-ictal imaging abnormalities in non-convulsive status epilepticus: A systematic review and meta-analysis comparing magnetic resonance imaging and computed tomography perfusion. Epilepsia. 2025. PMID 40827398 · DOI · PMC12779322
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.