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Breast cancer represents a leading cause of mortality among women. Consequently, early detection is crucial for effective screening and reducing the overall death rate. Therefore, the development of computer-aided diagnosis systems is essential for identifying breast cancer in its initial stages. Because of its affordability, ultrasound serves as a vital examination tool for breast cancer diagnosis. Although numerous learning-based methods have recently been introduced to categorize breast cancer using the breast ultrasound imaging (BUSI) dataset, manual processing remains a difficult and highly time-consuming task. This study presents an XAI-based framework utilizing an EfficientNet-integrated ResNet deep network to precisely categorize breast cancer as either benign or malignant. Initially, data augmentation is applied to expand the training sample size. To achieve this, three-pixel flip mathematical equations are implemented: horizontal, vertical, and 90°. Subsequently, two pre-trained deep learning models are utilized, selectively skipping specific layers before fine-tuning. These adjusted models are then trained via a deep transfer learning process to extract features from their deeper layers. The performance of these trained models is evaluated utilizing explainable artificial intelligence (XAI). Following this, a novel feature selection approach known as cuckoo search controlled standard error mean is introduced, which is grounded in the cuckoo search algorithm. This method isolates the optimal features, which are then combined using a novel parallel zero-padding maximum correlated coefficient fusion technique. Finally, the selection algorithm is re-applied to the integrated feature vector prior to classification via machine learning algorithms. Experimental evaluations of the proposed framework, performed on the publicly accessible BUSI dataset, achieved accuracies of 98.4% and 98% across two distinct experiments. Comparisons with contemporary techniques demonstrate that the proposed framework offers enhanced accuracy. Furthermore, it requires less execution time compared to the standalone original deep learning models.

Recently, significant efforts have been dedicated to enhancing anomaly detection methods, especially concerning high-dimensional and distributed clinical datasets. The analysis of patient information in clinical environments highlights a strong emphasis on improving diagnostic precision, tailoring treatment strategies, and maximizing resource efficiency to boost health outcomes. However, this field encounters distinct difficulties, including sporadic data gathering, variable data quality, and structural differences unique to individual patients. To overcome these obstacles, this paper introduces an innovative hybrid framework that merges stochastic and heuristic techniques for detecting anomalies in clinical patient data. This approach leverages an HPO-optimized Density-Based Spatial Clustering of Applications with Noise to group patient exercise data, thereby streamlining the detection of abnormalities. Following this, an Interquartile Range-based stochastic technique filters out untrustworthy data points, guaranteeing that healthcare practitioners and diagnostic systems are supplied with only highly relevant and precise data. The main goal of this research is to provide medical experts and scholars with a reliable instrument for handling vast, high-dimensional clinical datasets, allowing for the successful separation and elimination of deviant data observations. Additionally, an advanced regression model was constructed utilizing Automated Machine Learning (AutoML) to evaluate the effectiveness of the ensemble technique for detecting abnormal patterns. Multiple statistical error evaluation methods are employed to verify the success of this hybrid strategy in conjunction with AutoML. Empirical findings indicate that applying this cutting-edge hybrid framework to patient rehabilitation data significantly boosts AutoML performance, yielding an average improvement of 0.041 in the score, thereby outperforming conventional regression models.

As a leading risk factor for chronic illnesses, obesity highlights the critical need for prompt diagnosis and active management. This research introduces an innovative expert system developed to precisely categorise obesity severity and offer individualised treatment advice. Utilising the SEMMA data mining framework alongside the tidymodels ecosystem, the study assessed five machine learning algorithms: decision tree, random forest, multinomial logistic regression (MLR), Naive Bayes, and support vector machine (SVM). Achieving the highest predictive accuracy (97.48%), MLR was chosen as the definitive model. The platform incorporates an intuitive R Shiny interface, promoting seamless real-time user engagement. Treatment protocols are formulated via if-then rule-based logic to guarantee customised advice for every obesity classification. A comparative evaluation emphasises the system's exceptional diagnostic precision and its functional utility in recommending treatments. By improving accessibility—especially for marginalised rural communities—this system bolsters public health by facilitating preliminary diagnosis, specific interventions, and forward-looking obesity care.

Modern technological innovations and breakthroughs in the medical sector have significantly enhanced life expectancy. While smart healthcare systems are currently being transformed by artificial intelligence (AI) and the Internet of Things (IoT), the confidentiality and security of medical data continue to be a critical challenge. Reversible data hiding (RDH) is extensively investigated within medical environments to facilitate secure information transmission, as well as in fields such as cloud computing and satellite imagery transfer. The transmission of medical images serves as a cornerstone of the smart healthcare industry.For clinical images, even a marginal distortion during reconstruction can result in diagnostic errors by physicians, thereby endangering patient safety. Consequently, while numerous RDH methods have been developed, a limited number focus specifically on clinical imagery, particularly regarding high-quality DICOM formats.The introduced AdaBPE RDH framework provides a robust solution for securely sharing patient health reports (PHRs) and other confidential data with medical professionals. This strategy introduces a methodology that establishes an optimal balance between leveraging smooth pixels for maximized intra-block data embedding and ensuring lossless image reconstruction. Within this framework, an encrypted 16-bit DICOM image serves as the host medium to conceal the patient's confidential data. The algorithm divides the cover image into equally sized, non-overlapping blocks, adaptively embedding data into these encrypted sections based on the underlying characteristics of the original pixel values via an MSB prediction error approach.Performance evaluation is conducted using both 16-bit DICOM and 8-bit natural images. The proposed method successfully meets the core RDH benchmarks of BER = 0, PSNR = ∞, and SSIM = 1, yielding an average embedding capacity of 5.7067 bpp for premium medical images and 1.6769 bpp for natural scenes. The empirical findings demonstrate substantial advantages, outperforming existing state-of-the-art approaches.

The accurate determination of genotype and the clinical outlook for individuals with glioma present notable challenges, frequently relying on subjective evaluations and minimal scientific backing. This investigation intends to establish a dependable, noninvasive, preoperative learning framework based on a multi-modal MRI transformer to forecast IDH genotyping alongside glioma outcomes. This multi-institutional investigation gathered 563 individuals with glioma to construct an interpretable categorization model that leverages diverse preoperative imaging tracks, such as T1-weighted, T2-weighted, fluid-attenuated inversion recovery, contrast-enhanced T1-weighted, and diffusion-weighted imaging. The system utilizes a multi-task learning architecture designed to isolate and merge radiomic, deep learning, and clinical variables for IDH genotyping and glioma prognosis. Furthermore, a multi-modal transformer technique is incorporated to evaluate anatomical and functional MRI scans, thereby boosting the capabilities of the model. Empirical outcomes demonstrate that the framework achieves exceptional capability, outperforming prior investigations and alternative state-of-the-art approaches. The model attains an AUC of 91.40% for the IDH genotyping objective and 93.37% for the glioma prognosis objective. Subgroup evaluations indicate that the system displays heightened sensitivity toward IDH-mutant instances and identifies low-risk cohorts with greater precision than medium- or high-risk cohorts. This investigation intends to achieve precise IDH genotyping and glioma prognosis through an efficient classification methodology, delivering helpful diagnostic perspectives for clinical workflows and accelerating therapeutic determinations.

Equatorial plasma bubbles (EPBs) represent areas of reduced electron density that develop within Earth’s ionosphere as a result of Rayleigh–Taylor instability. These formations can trigger signal scintillation, which induces signal degradation and navigation positioning inaccuracies. EPBs can be identified via imagery gathered by All-Sky Imager (ASI) networks. The present research introduces an inexpensive, automated technique for identifying EPBs within ASI datasets, suitable for real-time monitoring as well as categorization tasks. This approach couples Two-Dimensional Principal Component Analysis (2DPCA) and Recursive Feature Elimination (RFE) alongside a Random Forest machine learning classifier to construct an Explainable Artificial Intelligence (XAI) framework. This framework extracts image characteristics to automate EPB detection using minimal dimensionality, yielding a compact and exceptionally rapid-to-train model designed to discern EPBs in recorded ASI frames. A collection of 2458 images, categorized into "Event" and "Empty" classes, was assembled to construct the database. This image repository was partitioned randomly into two distinct portions: an 80% training dataset and a 20% testing dataset. The developed XAI framework achieved a marginally better detection accuracy relative to the conventional 2DPCA approach while maintaining a notably smaller structural footprint. Additionally, the execution of the proposed framework was assessed and contrasted against alternative deep learning baselines (ResNet18, Inception-V3, VGG16, and VGG19) under identical environmental conditions.

The escalating complexity of malicious digital activities highlights the necessity for dependable and transparent intrusion detection systems (IDS) to preserve network integrity. Although machine learning frameworks like Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (K-NN), and XGBoost demonstrate exceptional capabilities in identifying malicious behavior, their inner mechanics grow increasingly opaque as structural complexity and predictive precision expand, creating challenges for high-stakes safety operations. Local Interpretable Model-agnostic Explanations (LIME) is frequently deployed to mitigate this issue; nevertheless, its dependency on Gaussian distributions when generating data variations often fails to capture the non-linear and disproportionate traits of frameworks like CIC-IDS-2018. To overcome these limitations, we introduce an adapted LIME variation strategy utilizing Weibull, Gamma, Beta, and Pareto distributions to more accurately represent the attributes of network telemetry. Our framework enhances the consistency of diverse machine learning systems optimized on CIC-IDS repositories, yielding more constructive and dependable rationales for predictive outputs. These proposed algorithmic adjustments yield a gain in explanatory fidelity of up to 78% relative to the standard normal distribution framework. Variations rooted in the Pareto distribution deliver the most optimal outcomes. Across all evaluated distributions, the Pareto framework consistently generated the peak explanation fidelity and consistency, notably for K-NN (R^2 = 0.9971, S = 0.9907) and DT (R^2 = 0.9267, S = 0.9797). This demonstrates that heavy-tailed distributions align effectively with actual network operational trends, minimizing the discrepancy in attribute significance justifications and rendering them more resilient.

In clinical environments, the transparency of artificial intelligence models is paramount, as understanding the rationale behind automated choices is vital. Although black-box AI frameworks exhibit remarkable efficacy, their deployment in healthcare remains impractical unless their underlying mechanisms can be clarified for medical professionals. Deep neural networks (DNNs) achieve state-of-the-art results, yet the justifications they provide are frequently incomprehensible to human observers. Conversely, traditional machine learning approaches and manually designed features extracted to capture distinct data characteristics offer greater transparency, though human constraints in feature engineering often limit their overall effectiveness. To bridge this gap, we present ExShall-CNN, a novel and transparent shallow convolutional neural network tailored for medical image analytics. This framework builds upon hand-crafted feature methodologies to preserve human interpretability, guaranteeing clear and verifiable decision-making. We introduce the explainable shallow convolutional neural network (ExShall-CNN), which merges the high performance of sophisticated deep architectures like U-Net with the inherent clarity of hand-crafted features for segmenting medical images. Grounded in contemporary machine learning progress, ExShall-CNN integrates widely utilized kernels while maintaining structural openness, allowing medical practitioners to visually inspect and comprehend its outputs. This balanced strategy delivers the precision expected of deep learning paradigms alongside the explainability required for clinical deployment.

Artificial neural networks (ANNs) have frequently been deployed for classification objectives within functional magnetic resonance imaging (fMRI) research, though such analyses traditionally isolate only specific regional sub-volumes of the brain. Recent methodology has integrated shallow neural networks (SNNs) with explainable artificial intelligence (xAI) frameworks to uncover underlying mechanisms of neural processing. Although prior research validated this paradigm utilizing fMRI data from motor execution tasks, the current investigation implements this framework on Theory of Mind (ToM) cognitive tasks, leveraging data extracted from the Human Connectome Project (HCP) Young Adult repository. Cognitive paradigms introduce greater computational complexity due to the fundamentally non-linear dynamics of neural functions. The HCP multimodal parcellation brain atlas is utilized to partition the cortex, thereby guiding the systematic training, pruning, and subsequent retraining of an SNN. Shapley values are subsequently computed to decode the retrained architecture, with the resulting outputs benchmarked against a General Linear Model (GLM) analysis for empirical validation. The baseline network demonstrated a classification accuracy of 88.2%, which decreased to 80.0% following network pruning, and subsequently recovered to 84.7% after the retraining phase. The SHAP-derived explanations demonstrated strong convergence with both GLM observations and established neuroanatomical regions associated with ToM. This fMRI investigation effectively navigated a highly complex cognitive paradigm, illustrating the efficacy of explainability techniques in deciphering non-linear neurological dynamics. These outcomes indicate that xAI platforms, particularly knowledge extraction frameworks, offer substantial utility for progressing mental health research and neural state decoding applications.

This research investigates and refines the MINDWALC node classification algorithm, specifically targeting its capacity to extract human-interpretable decision trees from knowledge graph (KG) architectures. We propose techniques to tailor MINDWALC for environments where the knowledge graph is bifurcated into internal background knowledge (domain-specific data) and instance knowledge (specific case data). While the core logic of MINDWALC—utilizing discriminative graph walks as features—is preserved, we introduce two primary enhancements. First, we implement relation-tail merging to empower the algorithm to process relation-modified nodes. Second, we propose flexible walking depths that, when combined with the original MINDWALC strategy, improve the detection of commonalities between node instances. Our findings suggest that these flexible walks can markedly increase classification accuracy in hierarchical or incomplete tree-like graphs, though performance varies on more heterogeneous KG structures. Ultimately, this work demonstrates that our refinements significantly boost MINDWALC’s utility in tree-structured environments, allowing the system to use background knowledge to bridge gaps in instance data in a transparent way. Initial evaluations on medical toy datasets suggest these optimizations could refine clinical decision-making, particularly in sectors requiring explainable artificial intelligence.

To overcome critical constraints regarding diagnostic accuracy and model transparency in medical imaging, this paper presents a novel framework termed the Pixel-Level Interpretability (PLI) model. The investigation primarily aims to benchmark PLI against Gradient-Weighted Class Activation Mapping (Grad-CAM) to attain superior localization correctness and highly detailed explainability. Operating on a VGG19 convolutional neural network infrastructure, the methodology incorporates three open-access COVID-19 chest X-ray datasets containing over 1000 categorized images; these underwent augmentation, normalization, and resizing steps to achieve generalizability and algorithmic stability. The experimental evaluation focused on vital performance metrics: structural similarity (SSIM), mean squared error (MSE), interpretability, computational speed, and diagnostic precision. The findings indicate that PLI drastically surpasses Grad-CAM across every evaluated metric. Specifically, PLI generated fine-grained, pixel-granular heatmaps that yielded reduced MSE, superior SSIM metrics, and expedited inference rates, demonstrating an capability to reveal minute diagnostic elements without sacrificing processing speed. Conversely, the explanations produced via Grad-CAM regularly lack the detailed resolution necessary for dependable clinical integration. By incorporating fuzzy logic to refine both numeric and visual explanations, PLI delivers transparent data that corresponds with clinical logic, thereby allowing healthcare professionals to execute decisions with greater certainty. Ultimately, this research validates PLI as a dependable framework for resolving deficiencies in AI explainability and clinical adoption. By simultaneously managing the dual demands of precision and transparency, PLI advances medical AI deployment and establishes a precedent for implementation within other high-stakes environments.

The medical industry in India has undergone substantial evolution driven by progress in technology and infrastructure. Yet, prominent hurdles remain when addressing critical problems like inadequate medical infrastructure for the nation's massive population, restricted accessibility, a deficit of qualified practitioners, and high-quality services. Artificial intelligence (AI)-based interventions offer the possibility to ease the burden on the Indian medical network; nevertheless, incorporating reliable AI into this domain is still obstructed by moral and legal barriers. The objective of this research is to critically analyze the contemporary state of AI system engineering in Indian medical care and its alignment with the moral and legal dimensions of AI, while also pinpointing the obstacles and prospects for implementing reliable AI within the Indian medical domain. This investigation assessed 15 papers chosen from an initial pool of 1136 papers collected across two digital databases, PubMed and Google Scholar, alongside project sites. This inquiry utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). The findings indicate that current publications primarily deployed traditional machine learning (ML) models and artificial neural networks (ANNs) for diverse operations, including drug discovery, disease monitoring networks, early sickness identification and diagnostic precision, and medical resource administration in India. This analysis uncovers a deficiency in the integration of reliable AI within Indian medical care and various obstructions linked to it. It examines pathways for engineering reliable AI within Indian medical environments, prioritizing patient protection, information confidentiality, and adherence to moral and statutory criteria.

The connection between the cognitive and emotional components of comprehension has remained largely unexamined because of a shortage of dependable techniques for assessing feelings and emotions during educational activities. Concentrating on five distinct stages of comprehension—nascent understanding, misunderstanding, confusion, emergent understanding, and deep understanding—this investigation presents an AI-based method to assess subjective comprehension by tracking the physiological activity visible in facial expressions. To examine these stages, 103 individuals worked on 15 riddles in a remote setting while their facial expressions were video recorded. Action units (AUs) for each stage occurrence were quantified utilizing AFFDEX software. Recurring AU configurations linked to each stage were subsequently pinpointed by implementing six supervised machine learning algorithms. Unique AU configurations were identified for all five stages, with gradient boosting machine and random forest models delivering the greatest predictive precision. These outcomes indicate that physiological activity can be utilized to dependably assess comprehension. Furthermore, they propose an innovative methodology for assessing and encouraging comprehension in instructional environments, as well as designing adaptive educational technologies and tailored learning interventions. Future research should investigate how the physiological indicators of comprehension stages simultaneously mirror and impact their related cognitive workflows, alongside the generalizability of these outcomes across varied demographics and educational environments (A suite of AI tools was employed in the development of this paper: (1) ChatGPT4o (for writing clarity and reference checking), (2) Grammarly (for grammar and editorial corrections), and (3) ResearchRabbit (reference management)).

Artificial intelligence (AI) applications in mental health possess immense potential to revolutionize clinical results, yet determining the most optimal strategies remains a persistent difficulty. This research fills this gap by assessing and establishing priorities among various AI-based mental health options. The evaluation is grounded in essential benchmarks: implementation feasibility, economic efficiency, scalability, adherence to ethical standards, user satisfaction, and overall clinical efficacy. A fuzzy multi-criteria decision-making (MCDM) framework, integrating fuzzy TOPSIS and fuzzy ARAS, was utilized to determine the ranking of the alternatives. A hybrid combination of these two approaches was subsequently implemented to reconcile variations in the results, as each methodology highlights distinct evaluative dimensions. Fuzzy TOPSIS, which privileges proximity to the ideal target, positioned the personalization of care (A5) as the primary alternative, yielding a closeness coefficient of 0.50, with user engagement (A2) following at 0.45. Similarly, fuzzy ARAS, which measures aggregate performance, ranked A5 as the leading option, demonstrating a total performance score of Si = 0.90 and a utility degree of Qi = 0.92. The integration of both frameworks offered a well-rounded analysis, wherein A5 maintained its premier ranking because of its superior performance in user satisfaction and clinical outcomes. These findings highlight the critical role that personalization and active engagement play in refining AI-enabled mental health platforms. This indicates that customized, patient-centric strategies are vital for maximizing therapeutic efficacy and sustaining user commitment.

This research undertook an assessment of Large Language Models (LLMs) within the domain of clinical diagnostics, specifically investigating their capacity to interpret symptom-centered inputs and generate precise clinical assessments. The investigation focused on evaluating several prominent architectures—including GPT-4, GPT-4o, Gemini, o1 Preview, and GPT-3.5—to gauge their proficiency in identifying clinical conditions solely from presented symptomatology. To guarantee clinical validity and appropriateness, symptom profiles were compiled from verified medical literature. Each architecture underwent testing within standardized environments to quantify diagnostic accuracy, precision, recall, and underlying logical processing. Specialized clinical scenarios were structured to examine performance boundaries across both routine and critical diagnostic conditions. The findings indicated that GPT-4 delivered the highest overall diagnostic accuracy, displaying a strong synergy with established medical logic. Conversely, Gemini demonstrated superior performance in high-stakes environments where exact decision-making is vital. Both GPT-4o and o1 Preview presented equitable metrics, maintaining a stable balance between precision and recall while managing real-time diagnostic scenarios. While possessing less advanced capabilities, GPT-3.5 remained functional for standard diagnostic duties. Ultimately, this investigation outlines the current capabilities and constraints of utilizing LLMs for clinical determination. Even though architectures like GPT-4 and Gemini show significant capability, critical barriers regarding data confidentiality, ethical protocols, and training bias correction must still be overcome. These insights offer strategic directions for the ethical incorporation of LLMs into diagnostic frameworks to optimize patient care outcomes.

This research introduces an innovative prediction framework utilizing a parallel multi-input Bi-GRU structure coupled with a sliding window-based Multi-Input Multi-Output (MIMO) forecasting approach. The primary novelty centers on feature-specific Bi-GRU encoding, which independently processes historical demand, meteorological variables, and calendar indicators prior to data fusion, thereby empowering the network to more effectively capture temporal correlations across disparate data streams. In contrast to preceding models, our methodology distinctively merges a structured feature-specific Bi-GRU encoding mechanism with interpretability via SHAP analysis, providing both superior predictive fidelity and transparent algorithmic decision-making suitable for practical deployment within electrical grids. The introduced Bi-GRU MIMO architecture operates as the foundational model. To rigorously evaluate its performance, an attention-enhanced LSTM model is incorporated solely as a baseline for comparative analysis. Empirical validation using actual data from Singapore’s National Electricity Market System (NEMS) demonstrates that the Bi-GRU MIMO framework enhances forecasting accuracy by 1.64% and decreases MAE by 2.19% relative to traditional models, underscoring its capability to improve decision-making in smart grids.

The escalating value placed on organizational human capital underscores the necessity for firms to optimize their systems for sourcing, evaluating, acquiring, onboarding, deploying, and retaining premium talent. Although embedding artificial intelligence (AI) within recruitment infrastructure promises unmatched workflow acceleration, operational scalability, minimized human subjectivity, and enhanced candidate sourcing, its deployment introduces distinct friction points that can degrade candidate attitudes and behavioral trajectories. Grounded in signaling and organizational justice frameworks, the deficit of interpersonal relational dynamics paired with the opaque nature of automated screening algorithms can prompt prospective candidates to question the integrity and structural fairness of the employment screening process. These institutional challenges risk modifying not only how job seekers deduce corporate principles, but also whether they choose to abandon the submission process altogether. Consequently, this investigation explores the comparative impact of AI-mediated versus human-mediated selection pathways on applicant trust, perceived procedural equity, corporate appeal, and subsequent intent to apply. Furthermore, we map the precise pathways through which trust and structural fairness mediate the link between the initial screening modality and early-stage candidate attraction and submission intentions. Data from an experimental student questionnaire indicate that job candidates view automated hiring protocols as significantly less credible, less equitable, and less appealing. Similarly, respondents exhibited depressed motivation to pursue open roles within enterprises leveraging automated recruitment architectures. Cultivating higher levels of institutional trust during the selection pathway and designing touchpoints that allow candidates to authentically showcase their professional capabilities can constructively reshape applicant attitudes and behavioral outcomes. These insights challenge the reductive assumption that operational velocity inherently yields strategic talent management success, emphasizing instead the vital role of interpersonal markers in securing top-tier talent within an automated recruitment environment.

The application of Artificial Intelligence (AI) tools to assist with systematic literature reviews (SLRs) is expanding rapidly, yet researchers still lack structured frameworks for assessing and incorporating these tools into their research practices. This paper introduces and implements a rubric-based framework designed to evaluate AI toolchains across essential SLR stages, such as literature searching, screening, and data synthesis. A mixed-methods study was executed with 63 postgraduate student researchers, who represent the early-stage investigators that serve as primary adopters of these digital workflows. The results demonstrate that workflows structured around the rubric enhanced the participants' perceived utility of AI utilities, provided organized criteria for comparative analysis, and promoted greater transparency during tool selection. Although formal indicators of inter-rater reliability (such as Cohen’s or Fleiss’ Kappa) were not calculated due to the fact that participants did not evaluate identical benchmark sets concurrently, the consistency of the rubric was qualitatively validated through pilot evaluations, iterative updates, and structured focus group discussions. These efforts confirm interpretative alignment among users while recognizing that future validation via independent assessors is required. This research contributes a validated assessment rubric, an operational example of an AI-augmented SLR workflow, and a discussion regarding the consequences for research training, study reproducibility, and cross-disciplinary translation.

Artificial intelligence decisions are often described as explainable when a system can produce reasons, feature contributions, counterfactuals, or other interpretive outputs. Such outputs, however, do not by themselves make an institutional decision answerable. This article develops a conceptual explainable AI governance framework that treats explainability as an organizational obligation connected to transparency duties, system documentation, evidence preservation, auditability, responsibility allocation, contestability, human review, and redress. The framework distinguishes technical explanation from the governance capacities required to identify who must justify a decision, what information must be recorded, how evidence can be examined, how affected stakeholders may challenge an outcome, and whether an organization can correct errors or remediate consequences. Transparency and documentation are organized around system, model, data, dataset, and decision records, together with data, model, and decision provenance. Auditability is connected to traceability, lifecycle records, risk documentation, monitoring, incident response, and clearly allocated organizational responsibility. Contestability is connected to decision justification, appeal and complaint pathways, competent and sufficiently independent human review, escalation, correction, recourse, and redress capacity. The framework also identifies governance gaps that arise when these functions are separated across the AI lifecycle, including documentation, evidence-trail, accountability, human-review, monitoring, and remediation gaps. Its principal contribution is an answerability-oriented governance architecture that links explanation to institutional evidence and response mechanisms while preserving explicit boundaries: transparency does not independently establish accountability, auditability does not prove responsibility, human involvement does not guarantee meaningful review, and a conceptual governance framework does not establish legal compliance, procedural fairness, ethical sufficiency, or deployment readiness.

Trustworthiness is frequently treated as a label attached to an intelligent system once selected technical, ethical, or governance requirements have been addressed. Such a binary interpretation is inadequate because system properties, operating conditions, organizational responsibilities, stakeholder expectations, and available assurance evidence change throughout the lifecycle. This article proposes a conceptual trustworthiness maturity model that interprets trustworthiness as an evolutionary system capability rather than a fixed attribute, certification outcome, or deployment decision. The model organizes progression across six levels: unstructured or ad hoc practices; foundational transparency and documentation; managed reliability, explainability, and human oversight; defined governance integration and accountability; continuous assurance and lifecycle monitoring; and adaptive trustworthiness learning and system updating. Foundational maturity requires accessible system, model, data, dataset, and decision documentation together with provenance, traceability, auditability, and maintained evidence trails. Higher capability requires context-sensitive evidence of reliability, validity, robustness, uncertainty, calibration, explanation quality, and meaningful human intervention. Advanced maturity additionally depends on responsibility mapping, organizational and institutional accountability, lifecycle governance, continuous monitoring, drift review, incident learning, assurance review, and controlled feedback updating. The principal contribution is an integrated maturity structure that connects technical, human, organizational, and lifecycle capabilities while retaining explicit boundaries against overclaiming. Maturity levels may help structure assessment planning, identify capability gaps, and clarify development priorities, but they do not independently prove trustworthiness, safety, ethical sufficiency, legal compliance, certification eligibility, or deployment readiness. The proposed model therefore supports conceptual maturity assessment and future empirical validation rather than operational prescription.

Foundation models have expanded the scope of artificial intelligence by supporting language generation, multimodal reasoning, knowledge-intensive assistance, decision support, and adaptable downstream applications. Their broad capabilities, however, do not establish that their outputs, explanations, internal processes, or deployment arrangements are trustworthy. This scoping review maps the research landscape concerning foundation-model trustworthiness across interpretability, explanation capabilities, hallucination, uncertainty, reliability, alignment, evaluation, and human oversight. The review uses structured search concepts, eligibility principles, evidence extraction, and dimension-based mapping to examine how peer-reviewed scholarship characterizes foundation models as general-purpose intelligent systems and how trustworthiness evidence is distributed across technical, human-centered, organizational, and governance domains. The synthesis indicates that interpretability methods can expose selected behavioral or representational features but do not independently demonstrate explanation fidelity, usefulness, or safety. Hallucination and factuality research identifies persistent risks involving fabrication, unsupported claims, weak grounding, and persuasive misinformation, while uncertainty methods remain constrained by calibration, context shift, and user-communication challenges. Alignment and post-training techniques may shape preferred behaviors but retain risks associated with proxy objectives, preference representation, and residual failure. Benchmarking, human evaluation, stress testing, and oversight provide partial evidence, yet their value depends on construct validity, domain relevance, intervention capacity, accountability, and lifecycle monitoring. The research landscape is therefore fragmented across disciplines and frequently model-centred rather than socio-technical. Key priorities include stronger foundation-model interpretability validation, consistent hallucination measurement, usable uncertainty communication, externally valid evaluation, meaningful oversight testing, multimodal analysis, and lifecycle governance. The review contributes an integrated evidence map while emphasizing that no single trustworthiness dimension certifies safety, reliability, accountability, or deployment readiness.

Explainable artificial intelligence has generated extensive technical and human-centered evaluation research, yet substantial uncertainty remains about what explanation evaluations actually demonstrate. This umbrella review synthesizes review-level evidence concerning the evaluation of fidelity, faithfulness, stability, robustness, comprehensibility, cognitive accessibility, decision usefulness, human trust, trust calibration, and reliance. Rather than reviewing explanation algorithms as methods, the article examines the constructs, metrics, study designs, and validity assumptions through which explanation quality has been assessed. The synthesis distinguishes technical correspondence with model behavior from human judgments of plausibility, understanding, usefulness, and satisfaction. It further separates human trust from system trustworthiness and behavioral reliance from beneficial decision support. Review-level evidence indicates that technical metrics are heterogeneous, terminology is inconsistently operationalized, and stable or plausible explanations are not necessarily faithful. Human-centered evaluations frequently rely on self-reported understanding, satisfaction, or trust, while objective comprehension, cognitive load, decision quality, appropriate reliance, overreliance, and underreliance receive less consistent assessment. Evidence linking technical explanation properties to downstream human and institutional outcomes remains limited, particularly across stakeholder groups, realistic decision contexts, repeated use, and changing system conditions. The umbrella synthesis therefore rejects single-metric interpretations of explanation quality and organizes evaluation as a multidimensional evidence problem involving technical validity, cognitive accessibility, decision consequences, calibrated trust, reliance behavior, accountability, and contestability. Its principal contribution is a consolidated evaluation agenda that clarifies what existing measures may support, what they do not establish, and which methodological connections require stronger user-centered, task-specific, longitudinal, and socio-technical validation.

Explainable artificial intelligence is often presented as a means of making intelligent systems more understandable, trustworthy, and usable, yet explanation availability does not reliably produce adoption, appropriate reliance, accountability, or improved decisions. This realist review examines why explainable AI is adopted, ignored, rejected, discontinued, overused, underused, or misused across organizational, professional, technical, and decision settings. It applies context–mechanism–outcome reasoning to interpret how explanation design interacts with workflow integration, user and domain expertise, decision risk, organizational incentives, governance arrangements, and human-oversight conditions. The synthesis focuses on mechanisms including perceived transparency, cognitive accessibility, mental-model formation, perceived usefulness, trust calibration, accountability confidence, contestability awareness, algorithm aversion, algorithm appreciation, automation bias, and explanation overload. These mechanisms may support selective reliance and meaningful challenge, but they may also generate false reassurance, superficial compliance, defensive rejection, or uncritical dependence. The review distinguishes adoption from acceptance, trust from trustworthiness, reliance from appropriate reliance, and explanation usefulness from decision quality. It identifies plausible configurations in which professional expertise, uncertainty communication, traceability, feedback, and contestability support careful use, while poor workflow fit, limited accessibility, weak governance, and automation pressure suppress scrutiny or encourage misuse. The main contribution is a bounded realist synthesis and design agenda for evaluating explainable AI through context-sensitive human responses and decision outcomes rather than explanation availability alone. The configurations are explanatory propositions requiring human-centered, organizational, and decision-context validation; they are not universal causal laws, implementation protocols, or evidence of deployment readiness.

Explainable artificial intelligence increasingly seeks to provide more than visualisations of feature importance or simplified descriptions of model outputs. For intelligent systems used in consequential settings, machine reasoning should be open to questioning, inspection, contextualisation, correction, and evaluation under uncertainty. This integrative review examines emerging approaches that may support this broader objective. It connects causal explainability and counterfactual reasoning with neuro-symbolic intelligence, knowledge-grounded inference, uncertainty-aware explanation, reliability evaluation, and human-centred interpretability. The review uses a structured integrative approach to compare how these paradigms represent causes, alternatives, rules, concepts, knowledge sources, inferential steps, confidence, and limitations. Causal and counterfactual approaches can support questions about mechanisms, contrasts, interventions, actionability, and recourse, but their conclusions depend on explicit assumptions, feasible alternatives, and appropriate causal evidence. Neuro-symbolic and knowledge-grounded approaches can make concepts, relations, rules, provenance, and intermediate reasoning more traceable, although symbolic structure or retrieved knowledge does not automatically ensure faithful, valid, or truthful explanation. Uncertainty-aware approaches can qualify predictions and explanations through uncertainty characterisation, calibration, confidence communication, robustness assessment, and human interpretation, but uncertainty estimates alone do not establish reliability. The synthesis indicates that no individual paradigm is sufficient. More defensible machine reasoning requires complementary evaluation of explanation fidelity, causal plausibility, knowledge validity, reasoning traceability, uncertainty communication, cognitive accessibility, contestability, and lifecycle accountability. Important research opportunities concern causal validation, feasible recourse, provenance governance, explanation uncertainty, hybrid-system evaluation, human-centred testing, and cross-paradigm assessment. The review contributes an integrated account of how machine reasoning may become not merely visible, but meaningfully open to question.

Foundation models are increasingly embedded in information access, content production, analysis, communication, and decision-support processes. Their broad capabilities have intensified interest in alignment, yet alignment alone cannot establish responsible use. A model may appear responsive to instructions or preferences while remaining opaque, weakly grounded, unreliable under changing conditions, overconfident, difficult to contest, or insufficiently governed. This article proposes a conceptual governance and technical safeguard framework for responsible foundation models. The framework treats foundation models as components of decision infrastructure situated within socio-technical and institutional environments rather than as isolated predictive tools. It integrates eight interdependent areas: model and alignment safeguards; explainability and interpretability; knowledge grounding and provenance; reliability, uncertainty, and hallucination containment; human control and escalation; institutional accountability; auditability and documentation; and lifecycle monitoring and governance updating. Explainability requirements are differentiated from interpretability, transparency, explanation usefulness, and human understanding, while knowledge grounding is separated from factuality, truthfulness, source quality, and provenance. Reliability is addressed through calibration, robustness assessment, boundary testing, uncertainty communication, failure containment, and continuing monitoring. Human oversight is defined through authority, competence, review capacity, escalation pathways, contestability, and recourse rather than nominal human presence. Institutional responsibility is connected to role allocation, evidence trails, documentation, review authority, and consequences. The principal contribution is an integrated framework that clarifies how technical and governance safeguards may support responsible foundation-model assessment without treating alignment, explanation, grounding, testing, oversight, or auditing as independently sufficient. The framework requires empirical validation, domain-specific evaluation, human-centred assessment, institutional governance, and residual-risk management before it can inform consequential operational decisions.

Generative artificial intelligence can produce fluent, context-sensitive, and apparently authoritative responses even when relevant knowledge is incomplete, evidence is unavailable, or generated claims are false. This creates an epistemic safety problem concerning not only what a system produces, but also what it appears to know, how its claims are supported, how uncertainty is expressed, and when answering should be limited. This article develops a conceptual safety framework organized around four operational principles: ground, qualify, verify, and abstain. The framework treats hallucination as a multidimensional risk involving fabrication, factuality failure, groundedness failure, unsupported claims, false confidence, source and context errors, reasoning errors, citation errors, overgeneralization, confabulation, speculative output, and incomplete evidence. Grounding links material claims to relevant and sufficiently reliable evidence; qualification communicates uncertainty, assumptions, scope, and evidential limitations; verification examines claims, sources, citations, and inferential consistency; and abstention enables refusal, deferral, escalation, or constrained output when evidence or system competence is insufficient. Human review, meaningful oversight, decision boundaries, failure containment, incident monitoring, and lifecycle updating operate across these principles rather than functioning as final guarantees. The main contribution is an integrated generative AI safety framework that connects output-level hallucination control with source quality, knowledge verification, uncertainty communication, review quality, response limitation, and downstream risk containment. The framework is intended to structure domain-specific assessment and future research. It does not independently establish factual reliability, ethical sufficiency, legal compliance, operational safety, certification, or deployment readiness.

Agentic artificial intelligence extends beyond conventional model-output generation by interpreting objectives, decomposing tasks, maintaining contextual information, selecting tools, interacting with external environments, and executing sequences of actions that may create downstream consequences. These capabilities complicate the question of who controls an agent and expose limitations in governance approaches based only on alignment, instruction following, output review, or nominal human supervision. This article develops a conceptual governance architecture for trustworthy agentic AI. The approach treats agentic AI as an autonomous decision system embedded within a wider socio-technical environment and organizes governance around explicit control points rather than a single supervisory mechanism. It distinguishes governance requirements for goal formation, goal interpretation, instruction hierarchy, task boundaries, tool access, memory retention, environmental interaction, action verification, failure containment, human approval, override capacity, responsibility allocation, auditability, and lifecycle review. The architecture proposes that consequential actions should be connected to authorized goals, bounded permissions, traceable memory and data inputs, pre-action checks, post-action evaluation, escalation pathways, and accountable human or institutional actors. It also establishes explicit sufficiency boundaries: goal alignment does not guarantee appropriate behavior; tool restrictions do not eliminate harmful use; memory controls do not guarantee reliable context; verification does not certify safe action; and human override does not independently establish meaningful control. The principal contribution is an integrated architecture-level account of how technical, human, and organizational controls may be connected across the agent lifecycle. The architecture supports conceptual analysis, governance planning, and future empirical evaluation, but does not establish legal compliance, operational safety, ethical sufficiency, institutional accountability, or deployment readiness.

Multimodal intelligent systems combine evidence from text, vision, audio, sensors, and structured data, but their explanations often remain fragmented into separate saliency maps, feature scores, textual rationales, attention visualisations, or confidence indicators. Such outputs may describe isolated aspects of a model while obscuring how modality-specific evidence was represented, transformed, fused, and communicated. This article develops a conceptual multimodal explainability architecture organised around the principle of following evidence across modalities. The approach begins by distinguishing the evidential and representational characteristics of textual, visual, acoustic, temporal-sensor, and structured inputs. It then identifies attribution difficulties created by early, intermediate, late, and hybrid fusion, including modality dominance, missing evidence, conflicting inputs, representation alignment, and uncertainty about joint contributions. Evidence traceability is treated as an end-to-end requirement connecting input provenance, modality-specific representation, fusion operations, attribution processes, consistency assessment, uncertainty communication, and decision explanation. The architecture further incorporates human interpretation because technically detailed multimodal explanations may remain cognitively inaccessible, visually persuasive, incomplete, or poorly calibrated to user expertise and decision context. Its central contribution is a layered framework linking modality inventory, evidence classification, representation mapping, fusion attribution, provenance, consistency review, human-centred presentation, evaluation, and feedback updating. The architecture is intended to structure research and design reasoning rather than prescribe a particular model, explanation technique, evaluation metric, or deployment procedure. It does not establish that an explanation is faithful, that users understand it, that reliance is appropriate, or that the underlying intelligent system is trustworthy or ready for operational use.

Explainable artificial intelligence increasingly supports decisions whose consequences extend beyond prediction, yet many explanations remain grounded in associations, feature contributions, saliency, or local perturbations. Such outputs may clarify model behaviour without establishing what would happen under intervention, why an outcome arose through a particular mechanism, or whether a proposed change is feasible and decision-valid. This article develops a conceptual causal explainability framework for moving from prediction-oriented explanation toward intervention-aware reasoning. The framework begins by separating predictive, associative, and model-behaviour claims from causal questions. It then connects explicit causal assumptions with causal structures, mechanistic reasoning, interventional explanation, counterfactual reasoning, confounding assessment, identifiability, sensitivity, external validity, uncertainty communication, and human interpretation. Particular attention is given to the distinction between model counterfactuals and real-world recourse, the risks created by unmeasured confounding, selection and collider bias, and the need to define decision boundaries before explanations inform action. Human users are treated as active interpreters whose causal literacy, expertise, cognitive constraints, and susceptibility to false causal confidence affect explanation usefulness. The main contribution is an integrated framework that organizes the evidential transition from prediction to intervention while preserving explicit no-guarantee boundaries concerning causal truth, intervention validity, recourse, identifiability, human understanding, decision validity, and deployment readiness. The framework is intended to support disciplined conceptual development and evaluation planning, not to prescribe a universal causal model, intervention procedure, or operational decision system.

Intelligent systems increasingly provide predictions, recommendations, and explanations in decisions where uncertainty can materially affect interpretation and action. Yet many explainable systems foreground predicted outcomes or feature contributions while inadequately communicating what the model does not know. This article proposes a conceptual uncertainty-aware explainable decision-intelligence framework organized around four connected responsibilities: calibrating confidence, disclosing ambiguity, communicating decision-relevant risk, and supporting selective prediction or abstention when available evidence is insufficient. The framework treats uncertainty as a trustworthiness requirement rather than a secondary technical output. It distinguishes aleatoric uncertainty arising from irreducible variability, epistemic uncertainty associated with limited knowledge or evidence, and distributional uncertainty arising when operating conditions depart from those represented during development. It further connects confidence calibration and miscalibration review with prediction qualification, rejection, abstention, deferral, and human review. Ambiguity disclosure and risk communication are positioned as interpretive functions that should clarify scope limitations, competing interpretations, possible consequences, and appropriate next actions without implying that communication alone guarantees understanding. Human decision support is addressed through escalation pathways, oversight, trust calibration, and evaluation of overreliance, underreliance, automation bias, and cognitive burden. The principal contribution is an integrated framework that links uncertainty identification, calibrated interpretation, selective system behavior, human review, monitoring, and feedback updating. The framework supports conceptual development and domain-specific evaluation but does not independently establish correctness, safety, decision validity, ethical sufficiency, legal compliance, or deployment readiness.