# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
#### 4.2.2 Healthcare  
In the healthcare sector, where patient safety and regulatory compliance are paramount, the selection of appropriate XAI frameworks is critical. LIMEs, GraphLIMEs, and DeepLIFT stand out as the top contenders, each offering tailored solutions to address the unique challenges of healthcare decision-making. LIMEs' ability to provide explanations at an individual level facilitates the interpretation of model predictions for specific patients, aiding healthcare professionals in making informed decisions regarding diagnosis and treatment. GraphLIMEs' extension to graph neural networks enables the interpretation of complex medical data, enhancing the transparency and reliability of AI-assisted medical diagnoses. Additionally, DeepLIFT's feature importance scores provide valuable insights  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{c}{**Flexible**} & \multicolumn{1}{c}{**Simple to Complex**} & \multicolumn{1}{c}{**Complex**} \\ \hline \multicolumn{2}{c}{TNTRules} & LOREs & OAK4 XAI \\ \cline{2-4} \multicolumn{2}{c}{CIAMP} & DALEX & InterpretML \\ \cline{2-4} \multicolumn{2}{c}{DLIME} & CEM & DeepLIFT \\ \cline{2-4} \multicolumn{2}{c}{TreeSHAPs} & Alteryx & ATN \\ \cline{2-4} \multirow{4}{*}{**Flexible**} & Skater & GraphLIMEs & TCAV \\ \cline{2-4} \multicolumn{2}{c}{CausalSHAPs} & Explain-IT & \\ \cline{2-4} \multirow{4}{*}{**Flexible**} & Captum & Anchors & \\ \cline{2-4} \multicolumn{2}{c}{RISE} & SHAPs & \\ \cline{2-4} \multicolumn{2}{c}{INNVestigate} & GRAD-CAM & \\ \cline{2-4} \multicolumn{2}{c}{Kernel SHAPs} & Integrated Gradients & \\ \cline{2-4} \multicolumn{2}{c}{AX360} & LIMEs & \\ \cline{2-4} \multicolumn{2}{c}{EBM} & LRP & \\ \cline{2-4} \multicolumn{2}{c}{What-if} & \\ \cline{2-4} \multicolumn{2}{c}{Eli5} & \\ \hline \hline \end{tabular}
\end{table}
Table 6: A comparative analysis of model complexity of various XAI frameworks.  
into the significance of medical indicators, facilitating the development of accurate and reliable diagnostic models. By leveraging these frameworks, healthcare practitioners can enhance patient care and safety, ensuring compliance with the regulatory standards and best practices.  
XAI has been increasingly integrated into the healthcare practices in medicine, where AI has greatly improved medical image analysis. The recent efforts have focused on combining AI's accuracy with enhanced model interpretability. Ref. [77] emphasizes the importance of transparent AI in healthcare decision-making and observed over 200 papers employing XAI in deep learning-based medical image analysis, highlighting the prevalence of visual explanations, such as saliency maps [78], over textual and example-based explanations.  
Ref. [79] investigated the application of XAI in psychiatry and mental health, emphasizing the heightened need for explainability and understandability due to the complex probabilistic relationships among syndromes, outcomes, disorders, and symptoms. They proposed the TIFU (Transparency and Interpretability For Understandability) framework, which underscores the importance of making models comprehensible to users through transparency and interpretability. Their study highlights XAI's key roles in the prediction and discovery within mental health, stressing the necessity for understandability in clinical settings where the stakes are high. The authors advocate for AI tools that support clinicians without adding unnecessary complexity.