SynthesisBMC medical imaging2026
Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.
Synthesis in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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
17 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development.Journal of the American College of Emergency Physicians open · 2026Article
- Artificial Intelligence Reading of Postoperative Cochlear Implant X-Rays.Journal of clinical medicine · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence-based ultrasound diagnosis of Hashimoto's thyroiditis: a systematic review and meta-analysis.Thyroid research · 2026Review
- Kernelized approach enables explainable gene prioritizations for complex traits.bioRxiv : the preprint server for biology · 2026Article
- Explainable Two-Stage Xception-Swin Transformer Learning for Body-Part-Aware Fracture Detection in Musculoskeletal X-Rays.Journal of imaging · 2026Article
- Clinical Use of Infrared Thermography: Where Are We and Where Are We Going.Medicina (Kaunas, Lithuania) · 2026Article
- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.NPJ precision oncology · 2026Article
- Review
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Evidence-Guided Diagnostic Reasoning for Pediatric Chest Radiology Based on Multimodal Large Language Models.Journal of imaging · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- Explainable AI in breast cancer ultrasound imaging: current developments and challenges.Frontiers in digital health · 2026Review
- Artificial intelligence for precision therapeutics in age-related macular degeneration: current advances, challenges, and future directions.Frontiers in artificial intelligence · 2026Review
- Deep learning-based multiclass classification for the diagnosis of hand arthritis on digital radiography: a multicenter study.Frontiers in medicine · 2026Article
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
Explainable Artificial Intelligence (XAI) is crucial for enhancing transparency and trustworthiness, as well as for developing AI-based diagnostic systems in medical imaging to achieve clinical acceptability and reliability. This synthesis aligns with the current context of XAI in medical imaging on four key dimensions: trends in techniques, their application to clinical use cases or with human subjects, and the associated problems. In radiology and pathology, we report on image analysis based on current literature from credible databases. This review extends on the existing surveys by explicitly addressing the focus on feature selection (FS), graph neural networks (GNNs), and multimodal transformers and combining them to a cohesive XAI taxonomy and matches the techniques to the specific impact points in the clinical workflow in radiology and pathology. We present saliency maps, attention mechanisms, and gradient-based and rule-based elucidations of deep learning (DL) models in today’s healthcare environment. Finally, the results show that XAI significantly enhances overall clinical decision-making by making the high-level reasoning of the model readily available to users, thereby increasing their confidence in clinical decisions despite the remaining obstacles, including standardization, interpretability, data bias, and complicated data integration. From 980 records, 289 duplicates were removed, 691 screened, 209 excluded, 482 full texts assessed, 263 excluded with 219 yielding 133 included studies. Exploring this third dimension within an XAI environment identifies research gaps and paves the way for robust medical imaging XAI solutions.Clinical trial number Not applicable.
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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 Socratic graph.