Leveraging explainable artificial intelligence for transparent and trustworthy cancer detection systems
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View abstract on PubMed
Summary
This summary is machine-generated.Explainable AI (XAI) enhances transparency in artificial intelligence (AI) for cancer detection. This review highlights XAI
Area Of Science
- Oncology
- Artificial Intelligence
- Medical Diagnostics
Background
- Timely cancer detection is crucial for improving patient outcomes.
- Artificial Intelligence (AI), particularly Deep Learning (DL), shows promise in cancer diagnostics but faces transparency challenges.
- Explainable AI (XAI) offers solutions by enhancing the interpretability and transparency of AI models.
Purpose Of The Study
- To systematically review recent applications of XAI in cancer detection across various cancer types.
- To categorize XAI techniques based on cancer type, interpretability methods, dataset usage, simulation environments, and security considerations.
- To identify current challenges and research gaps in trustworthy and interpretable AI for oncology.
Main Methods
- Systematic literature review of recent XAI applications in cancer detection.
- Categorization of studies by cancer type (breast, skin, lung, colorectal, brain, etc.).
- Analysis of interpretability methods, dataset utilization, simulation environments, and security aspects.
Main Results
- Convolutional Neural Networks (CNNs) are used in 31% of models.
- SHAP is the most common interpretability framework (44.4%).
- Python is the leading programming language (32.1%), with security issues addressed in only 7.4% of studies.
Conclusions
- XAI is increasingly important for transparent and interpretable AI in cancer diagnostics.
- Further research is needed to address security concerns and advance trustworthy AI applications in oncology.
- This review provides a roadmap for future research in explainable AI for cancer detection.
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