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Among the most critical and fatal medical conditions are brain malignancies, making early identification vital for enhancing survival rates. Currently, magnetic resonance imaging (MRI) stands as the premier diagnostic tool for early detection because of its exceptional capability to capture soft tissue details. Nevertheless, relying solely on human interpretation of brain MRI scans introduces risks of oversight, which are frequently exacerbated by the fatigue and varying expertise of radiologists. To mitigate these limitations, computer-aided diagnosis (CAD) systems have taken on an increasingly vital role. Leveraging state-of-the-art computer vision methodologies like deep learning, these systems generate highly precise diagnoses from clinical imagery, thereby boosting both precision and consistency in clinical settings. This study introduces an innovative CAD architecture designed for multi-class classification of brain malignancies. The system evaluates six pre-trained deep learning networks as its foundational architecture, integrating robust data preprocessing and augmentation protocols to optimize computational throughput. To counter the opaque "black box" nature of deep learning architectures and improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to illuminate the network's deductive steps when identifying anomalies in MRI data. Furthermore, an accessible, web-based Brain Tumor Detection System was constructed using the Streamlit framework, highlighting its utility for clinical environments and offering meaningful support to medical professionals. All experimental simulations were carried out using an open benchmark repository, proving that the introduced system delivers state-of-the-art diagnostic capabilities, achieving accuracy levels near 99% with the ResNet-50, Xception, and InceptionV3 architectures.

Global emergency departments and urgent care facilities regularly experience prolonged waiting periods that surpass patient expectations, primarily driven by unexpected spikes in patient volumes. The resulting delays caused by insufficient staffing during these high-demand intervals have been associated with detrimental clinical consequences. Prior investigations into predicting patient volumes have predominantly relied on traditional statistical approaches. Furthermore, these efforts have mainly focused on short-range predictions, which offer minimal utility for the long-term scheduling of medical staff. This paper contributes to a growing domain of research aimed at investigating the capacity of machine learning frameworks to produce precise patient volume projections. Utilizing a decade's worth of data from two major urgent care centers, this study constructs long-range patient flow predictions extending up to one quarter in advance by evaluating a selection of modern, high-performing algorithms. A unique aspect of this research is the integration of eXplainable Artificial Intelligence (XAI) frameworks, specifically LIME and Shapley, to conduct a comprehensive assessment of internal model dynamics that are typically obscured. These diagnostic instruments allowed us to examine how effectively the predictive frameworks adjusted to volatile demand shifts induced by COVID-19 pandemic lockdowns, while also determining the most significant predictor variables to gain deep operational insights. The empirical findings indicate that an innovative ensemble combining gradient boosting techniques with advanced univariate frameworks like Prophet yielded the most precise and dependable performance overall. On average, this methodology produced a 16%–30% improvement over the current internal forecasting techniques utilized for predicting daily patient volumes 90 days into the future.

In recent years, considerable attention has been directed toward clarifying complex, opaque systems, commonly referred to as black-box models, including deep neural networks and random forests. So-called model-agnostic techniques generally approximate the outputs of these non-transparent systems using an interpretable architecture, ignoring the internal mechanisms of the primary black-box system itself. Consequently, it remains an important inquiry whether the straightforward training of transparent, white-box architectures ought to be favored over the post-hoc approximation of these opaque models. This study presents the findings of an empirical investigation comparing post-hoc explanatory methods against inherently interpretable architectures across multiple datasets, focusing on both rule-based and feature-based frameworks. The empirical outcomes indicate that directly trained transparent models frequently approximate black-box models with an accuracy that matches or exceeds that of post-hoc surrogate models, despite lacking direct access to the underlying black-box structure.

We introduce and execute a transparent machine learning classification model designed for Explainable AI (XAI) that leverages expressive Boolean formulas. Potential domains of use include credit risk evaluation and medical diagnostics. The system utilizes a Boolean formula to establish a rule with adjustable complexity (or interpretability) for categorizing input data. This formula accommodates any operator applicable to single or multiple Boolean variables, offering superior expressivity relative to more rigid rule- and tree-based methodologies. Native local optimization methods are used to train the classifier, allowing for an efficient search of the feasible formula space. Fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers—potentially enhanced by custom hardware or quantum systems—can identify shallow rules. By executing non-local moves that optimize across subtrees of the complete Boolean formula, we merge the expressivity and efficiency of the native local optimizer with the rapid processing capabilities of these devices. We present comprehensive numerical benchmarking data against multiple baseline models using recognized public datasets. The findings indicate that the native local rule classifier performs competitively with alternative models. Incorporating non-local moves yields comparable outcomes with a reduced number of iterations. Consequently, deploying specialized or quantum hardware could provide substantial acceleration by rapidly generating non-local moves.

Explainable artificial intelligence increasingly offers diverse ways to communicate how intelligent systems operate, yet explanation selection is still frequently driven by method availability or model compatibility rather than stakeholder need. This article develops a stakeholder-oriented taxonomy for determining which explanation type may be appropriate for whom, at which decision moment, and for what purpose. Explanation is treated as a multiform system capability encompassing different scopes, reasoning functions, presentation modes, interaction patterns, and quality requirements. The taxonomy distinguishes global explanations concerned with model-level behaviour from local explanations directed at individual predictions, while clarifying the roles and limitations of feature attribution, saliency, surrogate models, rule extraction, transparency, fidelity, stability, and completeness. It further differentiates counterfactual, contrastive, and causal explanations according to their contributions to alternative reasoning, actionability, contestability, recourse, and accountability. Example-based, case-based, prototype, rule-based, natural-language, interactive, dialogue-based, adaptive, and personalized explanations are positioned according to user expertise, cognitive accessibility, task demands, and interaction context. The proposed taxonomy connects explanation families with stakeholder roles, pre-decision, during-decision, and post-decision timing, explanation purpose, decision consequence, uncertainty, trust calibration, human oversight, and accountability requirements. Its central contribution is a structured selection logic that moves beyond technical method categories toward stakeholder–purpose–timing–risk alignment. The taxonomy can guide explanation design and evaluation but does not independently establish understanding, fairness, recourse, accountability, legal sufficiency, trustworthiness, or deployment readiness. Stakeholder-specific and decision-context-specific validation therefore remains necessary.