ReviewFrontiers in radiology2025
Explainable AI in medicine: challenges of integrating XAI into the future clinical routine.
Review in Frontiers in radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing.Sensors (Basel, Switzerland) · 2025Pooled it
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate MRI.Abdominal radiology (New York) · 2026Review
- Toward a Conceptual Multiscale Framework for Predictive Radiobiology: Integrating Genomic Damage, Network Rewiring, and Tissue Microenvironment.International journal of molecular sciences · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- Review
- Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions.Health science reports · 2026Review
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
- Risk Stratification for In-Hospital Mortality in Alzheimer's Disease Using Interpretable Regression and Explainable AI.Geriatrics (Basel, Switzerland) · 2026Article
- Emergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach.Journal of clinical medicine · 2026Article
- Evaluating Explainability: A Framework for Systematic Assessment of Explainable AI Features in Medical Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Machine Learning Models Utilizing Oxidative Stress Biomarkers for Breast Cancer Prediction: Efficacy and Limitations in Sentinel Lymph Node Metastasis Detection.Biomedicines · 2025Article
- Artificial Intelligence for Predicting Difficult Airways: A Review.Journal of clinical medicine · 2025Review
- Pseudo datasets estimate feature attribution in artificial neural networks.Scientific reports · 2025Article
- The Convergence of Polymer Science and Predictive Modeling for Noninvasive Glucose Monitoring.Pharmaceutics · 2025Review
- HIV-1 and Artificial Intelligence: From Molecular Insight to Population Impact.Journal of AIDS and HIV treatment · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Future AI systems may need to provide medical professionals with explanations of AI predictions and decisions. While current XAI methods match these requirements in principle, they are too inflexible and not sufficiently geared toward clinicians' needs to fulfill this role. This paper offers a conceptual roadmap for how XAI may be integrated into future medical practice. We identify three desiderata of increasing difficulty: First, explanations need to be provided in a context- and user-dependent manner. Second, explanations need to be created through a genuine dialogue between AI and human users. Third, AI systems need genuine social capabilities. We use an imaginary stroke treatment scenario as a foundation for our roadmap to explore how the three challenges emerge at different stages of clinical practice. We provide definitions of key concepts such as genuine dialogue and social capability, we discuss why these capabilities are desirable, and we identify major roadblocks. Our goal is to help practitioners and researchers in developing future XAI that is capable of operating as a participant in complex medical environments. We employ an interdisciplinary methodology that integrates medical XAI, medical practice, and philosophy.
Indexed as
Identifiers
What Socratic 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 Socratic graph.