ArticleEthics and information technology2022
Putting explainable AI in context: institutional explanations for medical AI.
Article in Ethics and information technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.BMC medical ethics · 2024Pooled it
- Enhancing decision-making in surgery for a large temporocorneal meningioma through an explainable human-AI collaboration: a case report.Journal of medical case reports · 2026Article
- GIVersa-Endoscopy: A Large Language Model-Based Artificial Intelligence Assistant for Endoscopy Sedation Triage.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Article
- Personalized health monitoring using explainable AI: bridging trust in predictive healthcare.Scientific reports · 2025Article
- On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.BMC medical informatics and decision making · 2025Review
- Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Article
- A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: a retrospective model development and validation study.Frontiers in immunology · 2025Article
- Explainable and reproducible AI: culturally responsive AI for health equity in minoritized groups.Frontiers in digital health · 2025Article
- [Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Shallow and deep learning classifiers in medical image analysis.European radiology experimental · 2024Review
- Bias in AI-based models for medical applications: challenges and mitigation strategies.NPJ digital medicine · 2023Article
- "Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations.AI & society · 2022Article
- In Defense of Post Hoc Explanations in Medical AI.The Hastings Center reportArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a current debate about if, and in what sense, machine learning systems used in the medical context need to be explainable. Those arguing in favor contend these systems require post hoc explanations for each individual decision to increase trust and ensure accurate diagnoses. Those arguing against suggest the high accuracy and reliability of the systems is sufficient for providing epistemic justified beliefs without the need for explaining each individual decision. But, as we show, both solutions have limitations-and it is unclear either address the epistemic worries of the medical professionals using these systems. We argue these systems do require an explanation, but an
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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.