SynthesisBMC medical informatics and decision making2025
The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI).BMC psychiatry · 2025Pooled it
- Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Article
- Predictive Models for Toxicities after CAR T-cell Therapy: Challenges and Opportunities.Blood cancer discovery · 2026Review
- Prevalence and chronology of colibactin-associated mutational processes and their microbiome spectra in Japanese colorectal cancer.Nature genetics · 2026Article
- Classification of Alcohol Use Disorder With Explainable AI.Alcohol, clinical & experimental research · 2026Article
- Development and external validation of an online interpretable machine-learning model for predicting delirium risk in acute heart failure.The Journal of international medical research · 2026Article
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Article
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Latent profiles of nurses' attitudes toward artificial intelligence in nursing and associated factors: a cross-sectional study.BMC nursing · 2026Article
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Mapping behavior change techniques and health data combinations in virtual agents for chronic condition management: A systematic scoping review.PLOS digital health · 2026Article
- DCBM-Tri: a dual-channel bilinear mapping triplet model for early recognition of acute kidney injury in imbalanced cohorts.Scientific reports · 2026Article
- Article
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Review
- Development and internal validation of a machine learning-based model for predicting postoperative complications after primary liver cancer resection.BMC surgery · 2026Article
15 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Explainable Artificial Intelligence (XAI) enhances transparency and interpretability in AI models, which is crucial for trust and accountability in healthcare. A potential application of XAI is disease prediction using various data modalities. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol, synthesizing findings from 30 selected studies to examine XAI's evolving role in disease prediction. It explores commonly used XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), and their impact across medical fields in disease prediction. The review highlights key gaps, including limited dataset diversity, model complexity, and reliance on single data types, emphasizing the need for greater interpretability and data integration. Addressing these issues is crucial for advancing AI in healthcare. This study contributes by outlining current challenges and potential solutions, suggesting directions for future research to develop more reliable and robust XAI methods.
Indexed as
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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.