ReviewFrontiers in big data2021
Principles and Practice of Explainable Machine Learning.
Review in Frontiers in big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers, 4 of them syntheses 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
121 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine Learning Applications in Non-Contact Lower Limb Sports Injury Prediction: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review.Journal of medical Internet research · 2024Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence.Frontiers in medicine · 2022Pooled it
- Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values.Journal of chemical information and modeling · 2026Article
- Evaluation of the explainability and reliability of two surrogate models for predicting outflow of combined sewer systems.Water science and technology : a journal of the International Association on Water Pollution Research · 2026Article
- Integrating explainable AI and multi-objective optimization for wildfire resource allocation under fixed staffing constraints: a decision-oriented framework.Scientific reports · 2026Article
- Article
- AI-driven integration of Framingham Heart Study data with machine learning, deep learning, and explainable AI for enhanced pharmaceutical marketing.Scientific reports · 2026Article
- The Trust-Aware XAI (TAXAI) framework: a quantitative model for interpretable and reliable clinical AI systems.Scientific reports · 2026Article
- Article
- Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.Communications medicine · 2026Article
- A systematic exploration of digital biomarkers for the detection of depressive episodes in bipolar disorder.Npj mental health research · 2026Article
- Machine learning model optimization with optuna for accurate prediction of strength and crack behavior in prestressed concrete beams.Scientific reports · 2026Article
- The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.International journal of analytical chemistry · 2026Review
- Designing effective explainable AI: a human-centered evaluation of explanation formats in financial decision-making.Frontiers in artificial intelligence · 2026Article
- Machine learning-based models for predicting the efficacy and safety of recombinant human interleukin-11 in the treatment of cancer therapy-induced thrombocytopenia: exploration and preliminary validation from a multicenter retrospective study.Frontiers in cell and developmental biology · 2026Article
- Through the Speech and Vocal Signals Hidden Secrets: An Explainable Methodology for Neurological Diseases Early Detection.Journal of healthcare informatics research · 2025Article
- Deep learning model for differentiating thyroid eye disease and orbital myositis on computed tomography (CT) imaging.Orbit (Amsterdam, Netherlands) · 2025Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
61 more citing papers are in PubMed but not listed here.
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
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with a significant challenge: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods-machine learning (ML) and pattern recognition models in particular-so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models are increasingly deployed in a wide range of businesses. However, with the increasing prevalence and complexity of methods, business stakeholders in the very least have a growing number of concerns about the drawbacks of models, data-specific biases, and so on. Analogously, data science practitioners are often not aware about approaches emerging from the academic literature or may struggle to appreciate the differences between different methods, so end up using industry standards such as SHAP. Here, we have undertaken a survey to help industry practitioners (but also data scientists more broadly) understand the field of explainable machine learning better and apply the right tools. Our latter sections build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions. From an organization viewpoint, after motivating the area broadly, we discuss the main developments, including the principles that allow us to study transparent models vs. opaque models, as well as model-specific or model-agnostic post-hoc explainability approaches. We also briefly reflect on deep learning models, and conclude with a discussion about future research directions.
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.