ReviewFrontiers in medicine2023
Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.
Review in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 4 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
42 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- Which explainable AI methods in medical imaging are clinically impactful? A systematic literature review addressing the clinician's perspective.Frontiers in artificial intelligence · 2026Pooled it
- Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Artificial intelligence in psychiatric care and education: a qualitative study of factors influencing adoption in Singapore.International journal of medical education · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Article
- Temporal Convolutional Neural Network Analysis of Magnetocardiography Signals for Detection of Pulmonary Hypertension.Bioengineering (Basel, Switzerland) · 2026Article
- Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer, Dictionary Version PM1.0.Journal of imaging informatics in medicine · 2026Article
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- MRI-based habitat and peritumoral radiomics for predicting HER2 status in breast cancer: a multicenter study.Scientific reports · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Instance-level quantitative saliency in multiple sclerosis lesion segmentation.Scientific reports · 2026Article
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- Evaluating Explainability: A Framework for Systematic Assessment of Explainable AI Features in Medical Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- Bridging semantics and clinical fidelity: a section-based assessment of a vision-language model (RadVLM) for chest x-ray report generation.Frontiers in digital health · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Traditional machine learning in biomedical image analysis: before you go too deep.Frontiers in artificial intelligence · 2026Review
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rational: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made. Methods: PubMed, Embase.com and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging. Results: A total of 75 articles were included of which 54 and 17 articles described Conclusion: There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included.
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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.