ArticleEuropean radiology experimental2022
Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks.
Article in European radiology experimental, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Integrating Heterogeneous Real-World Cancer Data for Semantic Interoperability in Oncology and Medical Imaging: Development and Validation of the Cancer Image Europe Hyperontology.Journal of medical Internet research · 2026Article
- Bridging the AI chasm in oncology: a standardized platform to enable the in silico clinical validation of AI models within the CHAIMELEON Project.European radiology experimental · 2026Article
- A framework for a national cancer imaging repository in Nigeria.Scientific reports · 2026Article
- The ICGC ARGO data dictionary for standardizing global cancer clinical data.Scientific data · 2025Article
- Foundation models for radiology-the position of the AI for Health Imaging (AI4HI) network.Insights into imaging · 2025Review
- AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons.Scientific data · 2025Article
- Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure.Insights into imaging · 2025Article
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project.GigaScience · 2025Article
- Construction of medical scientific data repositories in China: analysis of survey and recommendations.Frontiers in artificial intelligence · 2025Article
- How the First Medical Imaging Cancer Atlas EUCAIM Was Populated: The Experience of a Reference Hospital.Open research Europe · 2025Article
- Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology.Insights into imaging · 2024Article
- Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology.Insights into imaging · 2024Article
- Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa.JMIR formative research · 2024Article
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.Insights into imaging · 2024Article
- Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine.Journal of translational medicine · 2024Review
- New Challenges for Anatomists in the Era of Omics.Diagnostics (Basel, Switzerland) · 2023Review
- Attention-Based Deep Learning System for Classification of Breast Lesions-Multimodal, Weakly Supervised Approach.Cancers · 2023Article
- Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects.European radiology experimental · 2023Review
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.
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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.