ReviewBioengineering (Basel, Switzerland)2025
Beyond Post hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency, Interpretability, and Explainability.
Review in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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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.
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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.
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Who cites it
28 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.Journal of evaluation in clinical practice · 2026Review
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- DriverNet: A Clinical and MRI-Based Framework for Noninvasive Pre-Treatment Molecular Triage in NSCLC Brain Metastases.Diagnostics (Basel, Switzerland) · 2026Article
- A transformer-based multimodal deep learning model for preoperative prediction of Ki-67 expression level in glioma.BMC medical imaging · 2026Article
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.Multidisciplinary respiratory medicine · 2026Article
- Explainable Agentic Artificial Intelligence in Healthcare: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Review
- Interpretable deep learning radiomics fromBMC medical imaging · 2026Article
- Optimizing Radiographic Diagnosis Through Signal-Balanced Convolutional Models.Journal of imaging · 2026Article
- Use of Automation Technologies and Data Mining in Speech Recognition for Autism.Brain and behavior · 2026Review
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- A lightweight region of interest-level adjudication framework with hard-negative mining and confidence-aware fusion for pediatric fracture detection.Frontiers in artificial intelligence · 2026Article
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- TIC-XNet: a structured evidence translation framework for interpretable multimodal pediatric tic event detection with improved temporal alignment and fidelity.Frontiers in psychiatry · 2026Article
- Scene graph-guided uncertainty decomposition improves confidence calibration in surgical visual question answering.Frontiers in medicine · 2026Article
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- Biomarkers in axial spondyloarthritis diagnosis: from clinical signs to multi-omics integration.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic capabilities, yet the black-box nature of deep learning models poses significant challenges for clinical adoption. Current explainable AI (XAI) approaches, including SHAP, LIME, and Grad-CAM, predominantly focus on post hoc explanations that may inadvertently undermine clinical decision-making by providing misleading confidence in AI outputs. This paper presents a systematic review and meta-analysis of 67 studies (covering 23 radiology, 19 pathology, and 25 ophthalmology applications) evaluating XAI fidelity, stability, and performance trade-offs across medical imaging modalities. Our meta-analysis of 847 initially identified studies reveals that LIME achieves superior fidelity (0.81, 95% CI: 0.78-0.84) compared to SHAP (0.38, 95% CI: 0.35-0.41) and Grad-CAM (0.54, 95% CI: 0.51-0.57) across all modalities. Post hoc explanations demonstrated poor stability under noise perturbation, with SHAP showing 53% degradation in ophthalmology applications (ρ = 0.42 at 10% noise) compared to 11% in radiology (ρ = 0.89). We demonstrate a consistent 5-7% AUC performance penalty for interpretable models but identify modality-specific stability patterns suggesting that tailored XAI approaches are necessary. Based on these empirical findings, we propose a comprehensive three-pillar accountability framework that prioritizes transparency in model development, interpretability in architecture design, and a cautious deployment of post hoc explanations with explicit uncertainty quantification. This approach offers a pathway toward genuinely accountable AI systems that enhance rather than compromise clinical decision-making quality and patient safety.
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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 OpenQuestion graph.