SynthesisBMC medical ethics2024
The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.
Synthesis in BMC medical ethics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
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
24 citing papers in PubMed.
- A Multimodal Therapist-Supervised Robotic Gait-Training Platform with Phase-Synchronized Infrared Thermal Biofeedback: A Concept-Level Technical and Translational Healthcare Framework.Healthcare (Basel, Switzerland) · 2026Article
- Segmentation-Free Preoperative 3D MRI Classification of Low-Grade Versus High-Grade Glioma Using Task-Oriented Neural Architecture Search.Journal of imaging · 2026Article
- Guiding Policymakers Toward Better AI Ethics Integration in Healthcare Regulation-Lessons from Singapore.Journal of clinical medicine · 2026Review
- Explainable Artificial Intelligence in Mammography: A Systematic Review of Methods, Evaluation Practices, and Clinical Readiness.Diagnostics (Basel, Switzerland) · 2026Review
- Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions.Health science reports · 2026Review
- Holistic AI in medicine; improved performance and explainability.NPJ digital medicine · 2026Article
- Hyper-selective explainability: an empirical case study of the utility of explainability in a clinical decision support system.AI and ethics · 2026Article
- Healthcare Systems at the Intersection of Just Culture and Artificial Intelligence: Emerging Challenges for Nursing Management.Risk management and healthcare policy · 2026Article
- Clinical nursing interns' perceptions of artificial intelligence-assisted tools in human-AI collaboration: a qualitative persona-based study.Frontiers in public health · 2026Article
- Health literacy and attitudes toward clinical AI: a cross-sectional study in Istanbul.Frontiers in digital health · 2026Article
- Interpretable AI for neural signal decoding in dementia: an EEG ensemble approach to differential diagnosis.Frontiers in neuroinformatics · 2026Article
- Optimistic and ambivalent attitudes toward clinical artificial intelligence.Turkish journal of medical sciences · 2026Article
- Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Artificial intelligence-driven transformative applications in disease diagnosis technology.Medical review (2021) · 2025Review
- Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.JCO oncology practice · 2025Review
- Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis.Bioengineering (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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite continuous performance improvements, especially in clinical contexts, a major challenge of Artificial Intelligence based Decision Support Systems (AI-DSS) remains their degree of epistemic opacity. The conditions of and the solutions for the justified use of the occasionally unexplainable technology in healthcare are an active field of research. In March 2024, the European Union agreed upon the Artificial Intelligence Act (AIA), requiring medical AI-DSS to be ad-hoc explainable or to use post-hoc explainability methods. The ethical debate does not seem to settle on this requirement yet. This systematic review aims to outline and categorize the positions and arguments in the ethical debate.
methodsWe conducted a literature search on PubMed, BASE, and Scopus for English-speaking scientific peer-reviewed publications from 2016 to 2024. The inclusion criterion was to give explicit requirements of explainability for AI-DSS in healthcare and reason for it. Non-domain-specific documents, as well as surveys, reviews, and meta-analyses were excluded. The ethical requirements for explainability outlined in the documents were qualitatively analyzed with respect to arguments for the requirement of explainability and the required level of explainability.
resultsThe literature search resulted in 1662 documents; 44 documents were included in the review after eligibility screening of the remaining full texts. Our analysis showed that 17 records argue in favor of the requirement of explainable AI methods (xAI) or ad-hoc explainable models, providing 9 categories of arguments. The other 27 records argued against a general requirement, providing 11 categories of arguments. Also, we found that 14 works advocate the need for context-dependent levels of explainability, as opposed to 30 documents, arguing for context-independent, absolute standards.
conclusionsThe systematic review of reasons shows no clear agreement on the requirement of post-hoc explainability methods or ad-hoc explainable models for AI-DSS in healthcare. The arguments found in the debate were referenced and responded to from different perspectives, demonstrating an interactive discourse. Policymakers and researchers should watch the development of the debate closely. Conversely, ethicists should be well informed by empirical and technical research, given the frequency of advancements in the field.
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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.