ArticleArtificial intelligence review2023
Explainable AI for clinical and remote health applications: a survey on tabular and time series data.
Article in Artificial intelligence review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07658586 (A Comprehensive Study of Liver Cancer Diagnosis and Prognosis Prediction Based on Artificial Intelligence and Multimodal Data), which is not on this map. Cited by 57 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.
A Comprehensive Study of Liver Cancer Diagnosis and Prognosis Prediction Based on Artificial Intelligence and Multimodal Data
Who cites it
57 citing papers in PubMed.
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- iS2C2: a cointelligent platform for mechanistic discovery of disease cellular crosstalk.Signal transduction and targeted therapy · 2026Article
- PFAS exposure and hypertension in US adults: A multi-method, cross-sectional analysis of NHANES 2005 to 2014.Medicine · 2026Article
- Explainable Agentic Artificial Intelligence in Healthcare: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Combined explainable deep learning model to predict pediatric sleep apnea from ECG and SpOMeasurement : journal of the International Measurement Confederation · 2026Article
- Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.Drugs & aging · 2026Review
- Artificial intelligence in clinical physiology: System-wise applications in diagnostics, monitoring, and medical education.Journal of family medicine and primary care · 2026Article
- Lung cancer risk prediction using interpretable ensemble models on lifestyle and clinical data.PloS one · 2026Article
- Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark-Based Machine Learning.Scientifica · 2026Article
- Explainability in action: A metric-driven assessment of local explanations for healthcare tabular models.PloS one · 2026Article
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
- AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.Frontiers in pharmacology · 2026Review
- ATF-MGIAM: Medically-guided interpretable attention mapping for robust pertussis cough sound recognition.PloS one · 2026Article
- Atherogenic index of plasma and risk of acute kidney injury in critically ill patients: a multi-cohort study with machine learning and SHAP analysis.Renal failure · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system's predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.
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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.