ArticleBioengineering (Basel, Switzerland)2025
What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach.
Article 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 29 papers, 1 of them a synthesis that pooled it.
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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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Patients' Perspectives on Applications of AI and Personalized Medicine in Life-Threatening Heart Disease: European Cross-Sectional Patient Questionnaire Study.Journal of medical Internet research · 2026Article
- Algorithmic Prognostication in Female Oncofertility Counseling: Ethical Challenges of Bias, Autonomy, and Predictive Uncertainty.Healthcare (Basel, Switzerland) · 2026Review
- Investigating the Potential Effects of Medical AI Systems on Physician Autonomy: Pretest of a Semistructured Qualitative Interview Guide.JMIR formative research · 2026Article
- Ethical Aspects of Artificial Intelligence in Precision Medicine for Obesity Management.JMA journal · 2026Review
- Moral diversity and the challenge of responsibility in AI-CDSS.Philosophy, ethics, and humanities in medicine : PEHM · 2026Article
- Beyond Implementation: Neuroanesthesiologists as Artificial Intelligence CoCreators.Journal of neurosurgical anesthesiology · 2026Article
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Article
- Eye-Tracking Evidence That Verifiable Explanations Support Visual Evidence Checking in AI-Assisted Chest Radiograph Interpretation.Journal of eye movement research · 2026Article
- Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- Emerging Urinary Biomarkers and Innovative Technologies for the Early Detection and Personalized Management of Chronic Kidney Disease.International journal of molecular sciences · 2026Review
- The Right to Understand in Health Care AI.Journal of medical Internet research · 2026Article
- "Black box" artificial intelligence for mortality prediction: a mixed-methods study of palliative care team, patient, and caregiver perspectives.Annals of palliative medicine · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- The Long road to AI-physician partnership starts with learners: a map for AI in medical education.BMJ digital health & AI · 2026Review
- 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
- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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1 author.
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Abstract
This article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS). After introducing the concept of explainability in AI and providing a short overview of AI-based clinical decision support systems (CDSSs) and the role of explainability in CDSSs, four use cases of AI-based CDSSs will be presented. The examples were chosen to highlight different types of AI-based CDSSs as well as different types of explanations: a machine language (ML) tool that lacks explainability; an approach with post hoc explanations; a hybrid model that provides medical knowledge-based explanations; and a causal model that involves complex moral concepts. Then, the role, relevance, and implications of explainability in the context of the use cases will be discussed, focusing on seven explainability-related aspects and themes. These are: (1) The addressees of explainability in medical AI; (2) the relevance of explainability for medical decision making; (3) the type of explanation provided; (4) the (often-cited) conflict between explainability and accuracy; (5) epistemic authority and automation bias; (6) Individual preferences and values; (7) patient autonomy and doctor-patient relationships. The case-based discussion reveals that the role and relevance of explainability in AI-based CDSSs varies considerably depending on the tool and use context. While it is plausible to assume that explainability in medical AI has positive implications, empirical data on explainability and explainability-related implications is scarce. Use-case-based studies are needed to investigate not only the technical aspects of explainability but also the perspectives of clinicians and patients on the relevance of explainability and its implications.
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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.