SynthesisFrontiers in artificial intelligence2024
Human-centered evaluation of explainable AI applications: a systematic review.
Synthesis in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- X-THREAT framework for adaptive and explainable deep learning-based minority-class and zero-day cyber threat detection.Scientific reports · 2026Article
- Explainable AI for chest radiographs: sex-stratified fairness auditing in CNN-based pneumonia detection.Scientific reports · 2026Article
- A Double-Edged Algorithm Attitude: How Appreciation and Aversion Shape Students' AI Learning Anxiety in Higher Education.Behavioral sciences (Basel, Switzerland) · 2026Article
- Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design.JMIR mental health · 2026Article
- Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals' perspectives.PLOS digital health · 2026Article
- New-generation AI-driven intelligent decision-making and inventory optimization in the full lifecycle of complex product manufacturing integrating LSTM and Q-learning.Scientific reports · 2026Article
- Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.Communications medicine · 2026Article
- Emergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach.Journal of clinical medicine · 2026Article
- Adapting quality function deployment to translate patient feedback into prioritized technical requirements for healthcare artificial intelligence.Scientific reports · 2026Article
- Human-centered evaluation of statistical parametric mapping and explainable machine learning for outlier detection in plantar pressure data.Scientific reports · 2026Article
- Artificial intelligence-driven multimodal fusion for precision diagnosis and personalized management of breast cancer.Oncology reviews · 2026Review
- Audience-Customized Translation of Rule-Based Evidence with Large Language Models Across Multiplexer Benchmarks.Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference · 2026Article
- Health literacy and attitudes toward clinical AI: a cross-sectional study in Istanbul.Frontiers in digital health · 2026Article
- Designing effective explainable AI: a human-centered evaluation of explanation formats in financial decision-making.Frontiers in artificial intelligence · 2026Article
- How generative AI shapes designers' cultural heritage revitalization intention: process and content cues in AI-assisted cultural heritage design.Frontiers in psychology · 2026Article
- Which explanations do clinicians prefer? A comparative evaluation of XAI understandability and actionability in predicting the need for hospitalization.BMC medical informatics and decision making · 2025Article
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
Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there's been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation "good" from a user's perspective, i.e., what makes an explanation
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What Socratic 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 Socratic graph.