The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions

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)...

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Published in:BMC medical informatics and decision making Vol. 25; no. 1; pp. 110 - 17
Main Authors: Alkhanbouli, Razan, Matar Abdulla Almadhaani, Hour, Alhosani, Farah, Simsekler, Mecit Can Emre
Format: Journal Article
Language:English
Published: London BioMed Central 04.03.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Accountability
Artificial Intelligence
Clinical medicine
Data integration
Decision making
Diagnosis
Disease
Disease prediction
Disease recognition
Diseases
Explainable artificial intelligence
Forecasts and trends
Health aspects
Health care
Health Informatics
Healthcare AI
Humans
Information Systems and Communication Service
Literature reviews
Machine learning
Management of Computing and Information Systems
Medical imaging equipment
Medical personnel
Medicine
Medicine & Public Health
Physiology
Predictions
Research methodology
Systematic Review
Transparency
United Arab Emirates
XAI
United Arab Emirates
Decision support systems
XAI
Machine learning
Explainable artificial intelligence
Healthcare AI
Patient safety
Risk management
Disease recognition
Disease prediction
ISSN:1472-6947, 1472-6947
Online Access:Get full text
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Summary: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.
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ISSN:1472-6947
1472-6947
DOI:10.1186/s12911-025-02944-6