Enterprise AI Analysis
The Role of Explainable Artificial Intelligence in Disease Prediction
Article Title: The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions
Primary Authors: Razan Alkhanbouli, Hour Matar Abdulla Almadhaani, Farah Alhosani, Mecit Can Emre Simsekler
Publication Details: Published 04 March 2025 in BMC Medical Informatics and Decision Making
This systematic literature review highlights how Explainable Artificial Intelligence (XAI) is transforming disease prediction by enhancing transparency and interpretability in AI models. Crucial for trust and accountability in healthcare, the study synthesizes findings from 30 selected studies, exploring common XAI methods like SHAP and LIME and their impact across medical fields. It addresses key gaps such as limited dataset diversity and model complexity, advocating for greater interpretability and data integration to advance AI in healthcare with reliable and robust XAI methods.
Executive Impact & Key Findings
Leveraging XAI in medical diagnostics can significantly enhance precision, trust, and operational efficiency. Explore the critical metrics shaping this evolving landscape.
0
Studies Analyzed
0
Recent Publication Growth (2022-2023)
0
Top XAI Method (SHAP)
0
Global Research Contribution
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Takeaway:
XAI is essential for trust and ethical considerations in AI-driven healthcare, transforming diagnosis and treatment planning by making 'black box' models interpretable.
Detailed Explanation:
The theoretical background emphasizes XAI's crucial role in making AI models transparent and understandable, addressing the 'black box' challenge. It's vital for gaining healthcare professionals' trust and adhering to regulatory and ethical standards. XAI helps in understanding how AI arrives at conclusions, which is especially important in personalized medicine for accurate diagnoses and tailored treatments. Methods like SHAP and LIME provide local and global interpretability, enhancing the comprehensibility of complex AI models in sensitive medical contexts.
Key Takeaway:
A rigorous PRISMA-guided Systematic Literature Review (SLR) of 30 studies published between 2018-2023 was conducted to identify reliable evidence on XAI in disease prediction.
Detailed Explanation:
The study utilized a Systematic Literature Review (SLR) following PRISMA guidelines to identify and synthesize findings on XAI in disease prediction. A systematic search was conducted across Scopus, PubMed, and Web of Science databases, focusing on peer-reviewed articles published between 2018 and 2023. Articles were selected based on their contribution to disease prediction using XAI techniques, ensuring a comprehensive analysis of XAI's advantages and disadvantages in healthcare.
Key Takeaway:
SHAP (38%) and LIME (26%) are the most frequently used XAI methods across 30 studies, demonstrating XAI's adaptability in diagnosing diverse conditions like cancer and cardiovascular diseases.
Detailed Explanation:
The analysis revealed that SHAP (38%) and LIME (26%) are the most prevalent XAI methods. Other methods like Grad-CAM, Fuzzy logic, and PDP were also used. The studies covered various disease categories including cardiovascular, cancers and tumors, neurological, infectious, metabolic, endocrine, and respiratory diseases. The number of publications significantly increased from 2019 to 2023, with 80% published in 2022-2023, indicating a rapid growth in XAI research for medical diagnostics. Authors from 29 countries contributed to this body of work.
Key Takeaway:
The increasing interest in XAI reflects healthcare's need for transparent AI, but challenges remain in standardization, clinician education, and regulatory frameworks for ethical deployment.
Detailed Explanation:
The growing volume of literature on XAI signifies a readiness for wider adoption in healthcare, driven by the need for transparent AI systems in disease prediction and diagnosis. While SHAP and LIME have emerged as dominant methods, the field requires further refinement, standardization, and educational initiatives to equip clinicians. Regulatory guidelines are crucial for ethical and safe deployment, highlighting the need for ongoing research and cross-disciplinary collaboration to advance XAI's transformative role.
Key Takeaway:
Key gaps include limited data diversity, model complexity/interpretability trade-offs, and integration into clinical workflows, requiring solutions like diverse datasets, SHAP/LIME, and user-centered AI interfaces.
Detailed Explanation:
The review identified several gaps: limited datasets impacting generalizability, challenges in balancing model complexity with interpretability, and the need for XAI models to align closely with clinical diagnostic processes. Proposed solutions include assembling diverse datasets through global partnerships and techniques like SMOTE/ADASYN, leveraging SHAP and LIME for interpretability, deploying big data analytics, and creating user-friendly AI interfaces and coaching systems to enhance clinician trust and workflow integration. These strategies are vital for ensuring AI applications are transparent, verifiable, and effectively integrated into patient care.
| Method | Key Strength | Primary Application Areas |
|---|---|---|
| SHAP |
|
|
| LIME |
|
|
| Grad-CAM |
|
|
| Partial Dependence Plots (PDP) |
|
|
| Genetic Programming |
|
|
| Fuzzy Logic |
|
|
Systematic Literature Review Process
The study followed a rigorous PRISMA-guided systematic literature review process to ensure comprehensive and unbiased data collection and analysis.
Identification (76 records)
→
Screening (60 records after duplicates removed)
→
Eligibility Assessment (41 reports)
→
Included in Review (30 studies)
7
Primary Disease Categories Addressed
The review categorized diseases into seven main groups, showcasing the broad applicability of XAI in medical diagnostics. These include Cardiovascular, Cancers & Tumors, Neurological, Infectious, Metabolic & Endocrine, Respiratory, and Other Conditions.
| Area | Identified Gap | Proposed Solution |
|---|---|---|
| Model Scope |
|
|
| Modeling Approach |
|
|
| Technology |
|
|
| Implementation |
|
|
XAI in Action: Colorectal Cancer Diagnosis
The paper highlights a study where an explainable classifier, utilizing histopathological images, was developed to predict 8 varieties of colorectal cancer. This model, by providing interpretable decisions, enhances accountability and trust in AI-driven diagnostics.
The Enterprise Challenge: Traditional 'black box' AI models struggle with trust and accountability in critical medical diagnoses, making it difficult for healthcare professionals to understand the underlying logic.
Our AI-Powered Solution: An XAI-enabled classifier for colorectal cancer. This model uses histopathological data and provides clear explanations for its predictions, addressing the 'black box' problem. This helps clinicians understand why a specific diagnosis is made, improving adoption and patient safety.
Measurable Outcome: Improved accountability and trust in AI decision-making for colorectal cancer diagnosis, enabling healthcare professionals to make more informed and confident decisions.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating explainable AI solutions. Adjust the parameters below to see tailored results.
Estimated Annual Cost Savings
$0
Estimated Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A strategic approach to integrating Explainable AI within your enterprise, ensuring transparency, trust, and measurable outcomes.
Phase 01: Discovery & Strategy
Conduct an in-depth analysis of existing systems and identify key areas where XAI can drive significant impact. Define clear objectives, KPIs, and a phased implementation strategy tailored to your enterprise needs.
Phase 02: Pilot & Proof-of-Concept
Deploy XAI models in a controlled environment, focusing on a specific disease prediction task. Evaluate performance, interpretability, and user acceptance among clinical professionals. Gather feedback for iterative refinement.
Phase 03: Integration & Scaling
Seamlessly integrate validated XAI solutions into existing clinical workflows and IT infrastructure. Develop user-friendly interfaces and provide comprehensive training to healthcare staff, ensuring broad adoption and ethical deployment.
Phase 04: Monitoring & Optimization
Establish continuous monitoring of AI model performance, interpretability, and impact on patient outcomes. Implement mechanisms for ongoing feedback and regular updates to ensure long-term reliability and accuracy.
Ready to Transform Your Enterprise with XAI?
Unlock the full potential of explainable artificial intelligence for disease prediction and enhanced decision-making. Schedule a personalized consultation with our experts today.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat