## 3 Background and Related Work
### Introduction to XAI in Healthcare  
AI is transforming healthcare by enhancing diagnostic accuracy, optimizing treatment plans, and improving operational efficiency. However, the increasing complexity of DL models has raised concerns about their lack of interpretability, particularly in high-stakes medical applications [37]. XAI aims to address this challenge by making AI-driven decisions transparent, interpretable, and trustworthy, enabling clinicians to understand and validate model outputs [38].  
One of the earliest discussions on AI interpretability in healthcare emphasized the need for trust and human oversight in decision-making [39]. More recent studies have reinforced this view, highlighting that black-box AI models can struggle with clinician adoption unless their reasoning aligns with medical knowledge [40]. In response, various XAI techniques have been developed to improve interpretability, including:  
* **Feature Attribution Methods:** Techniques like Shapley Additive Explanations (SHAP) [41] and Local Interpretable Model-agnostic Explanations (LIME) [38] assign importance scores to input features, showing how each factor contributes to a prediction.
* **Global vs. Local Explanations:** XAI methods can be broadly categorized into global and local explanations [23]. Global explanations provide insights into the overall behavior of a model, showing how features generally influence predictions across the entire dataset. Methods such as Partial Dependence Plots (PDPs) illustrate the average impact of a feature, helping clinicians understand the general trends learned by the model. However, global explanations may not always be useful for individual patient decisions. In contrast, local explanations focus on interpreting a single prediction for a specific patient. Techniques like LIME aim to identify which features contributed most to a particular decision. For example, in an emergency setting, a local explanation might reveal that a patient's elevated heart rate and abnormal oxygen saturation were the primary drivers behind an AI model predicting a high likelihood of hospital admission. These patient-specific insights enhance clinical decision-making by allowing physicians to verify whether the model's reasoning aligns with their medical judgment.
* **Example-Based Explanations:** Aims to improve the interpretability of AI models by presenting specific instances from the dataset that are similar to the case under consideration [42]. This approach allows clinicians to compare a current patient's data with past cases, facilitating a more intuitive understanding of the model's predictions. For instance, a study [43] developed an oral cancer screening system using Case-Based Reasoning with DL. The system retrieves similar past cases to provide visual explanations, aligning with clinician reasoning. The DL model integrates medical knowledge, improving accuracy (85%) and interpretability.  
In emergency medicine, where decisions must be made rapidly, XAI can play a crucial role in ensuring that predictive models align with clinical expectations. Recent studies (e.g., [PERSON] et al. [44] and [PERSON] et al. [45]) demonstrate that integratingexplainability improves physician trust and adoption, particularly when model insights are validated against expert knowledge. As AI continues to evolve in healthcare, XAI will remain a key factor in bridging the gap between machine intelligence and human expertise.