ArticleEthics and information technology2022
Explanatory pragmatism: a context-sensitive framework for explainable 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 10 papers.
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
10 citing papers in PubMed.
- Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.Journal of the American Medical Informatics Association : JAMIA · 2024Trial
- Evidence-Based Medicine Meets Artificial Intelligence: Reframing Critical Appraisal in Emergency Medicine.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026Article
- Hybrid transformer-fuzzy framework for interpretable sentiment classification in deepfake social media content.Scientific reports · 2026Article
- An explainable machine learning framework utilizing ultrasound radiomics for the preoperative differentiation between granulomatous lobular mastitis and breast cancer.Frontiers in oncology · 2026Article
- Article
- Knowledge Versus Understanding: What Drives Moral Progress?Ethical theory and moral practice : an international forum · 2025Article
- Explainable AI in medicine: challenges of integrating XAI into the future clinical routine.Frontiers in radiology · 2025Review
- Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection.Heliyon · 2024Article
- A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.Frontiers in oncology · 2022Article
- Putting explainable AI in context: institutional explanations for medical AI.Ethics and information technology · 2022Article
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.
Funding
Abstract
Explainable artificial intelligence (XAI) is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the problem we seek to address in this paper. We outline a framework, called
Indexed as
Identifiers
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.