Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection

As machine learning models become increasingly prevalent in palliative care, explainability has become a critical factor in their successful deployment in this sensitive field, where decisions can profoundly impact patient health and quality of life. To address these concerns, Explainable AI (XAI) aims to make complex AI models more understandable and trustworthy. This study aims to assess the current state of machine learning models in palliative care, specifically focusing on their compliance with the principles of XAI. A comprehensive literature search in four databases was conducted to identify articles on machine learning in palliative care studies published until May 2024, followed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. The Checklist for Assessment of Medical Artificial Intelligence was used to evaluate the quality of the studies. Mortality and survival prediction were the primary focus areas in 15 (54%) of the included 28 studies. Regarding data explainability, 20 studies (71%) documented their data preprocessing methods. However, a notable concern is that 45% of the studies did not address handling missing data. Across these studies, 74 machine learning algorithms were employed. Complex models, including Random Forest, Support Vector Machines, Gradient Boosting Machines, and Deep Neural Networks, were predominantly used (64%) due to their high predictive power, achieving AUC values between 0.82 and 0.96. Post-hoc explanation techniques were applied in only 11 studies, using seven different XAI techniques, focusing on global explanations to enhance understanding of model behavior. Given the critical role of AI-driven decisions in patient care, adopting XAI techniques is essential for fostering trust and usability. Although progress has been made, significant gaps persist. A main challenge remains the trade-off between model performance and interpretability, as highly accurate models often lack the transparency required to build trust in clinical settings. Additionally, complex models frequently provide inadequate explanations for their outputs, lack consistent documentation, and have limited XAI applications, reducing the interpretability of machine learning studies for clinicians and decision-makers.

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) across healthcare domains has significantly improved diagnostic accuracy, operational efficiency, and patient outcomes (Rajkomar et al., 2019; Jiang et al., 2017). Despite these benefits, concerns regarding the lack of transparency and interpretability in AI systems remain critical, particularly in high-stakes environments such as healthcare (Doshi-Velez & Kim, 2017; Tjoa & Guan, 2020). Many AI models operate as “black boxes,” making it difficult for stakeholders to understand the rationale behind their decisions, thereby raising issues of trust, accountability, and ethical compliance (Arrieta et al., 2020; Floridi et al., 2018). Healthcare Business Process Outsourcing (BPO) organizations increasingly utilize AI-driven tools to automate medical document processing for insurance claims. These documents, including prescriptions, lab reports, and radiology records, must be classified and chronologically organized to reflect patient history. Traditional ML approaches such as Count Vectorization and TF-IDF combined with classification algorithms achieve high accuracy; however, they rely heavily on word frequency, which can introduce bias and lead to misclassification (Wang et al., 2018; Obermeyer et al., 2019). To address these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising solution that enhances transparency and interpretability in AI systems (Ribeiro et al., 2016; Lundberg & Lee, 2017). This study presents a systematic review of XAI techniques in healthcare, focusing on their application in medical document classification. It further identifies key challenges, including the lack of standardized evaluation metrics, and proposes future research directions to enhance transparency, fairness, and reliability in AI-driven healthcare systems (Arrieta et al., 2020; Tjoa & Guan, 2020).

Artificial intelligence (AI) is revolutionizing the world we live in. It holds significant promise for improving performance across various sectors. Researchers worldwide have been working on developing solutions that leverage this technology for application by diverse user groups. A key category of AI is healthcare AI; smart solutions designed to address the needs of various stakeholders in the sector, from medical practitioners to patients. Crucial for any AI deployment are good adoption rates from the intended users and support from key stakeholders. Therefore, understanding the factors that influence its adoption is essential. This paper aims to provide insights through a systematic literature review of research related to healthcare AI adoption. Specifically, it reports on research works from ASEAN countries published over the past decade, between 2014 and 2024, identified through a systematic search of the Lens.org database. The search string focused on terms such as “Artificial Intelligence,” “Medical,” “Healthcare,” “Explainable,” “Transparency,” “Trust,” and “Adoption.” The motivation for this search stems from developments in Explainable Artificial Intelligence (XAI). XAI is vital for fostering trust and facilitating the widespread adoption of AI-driven clinical decision support systems (CDSS) in healthcare. While AI has the potential to augment clinical decision-making, the lack of transparency in traditional “black box” models can undermine trust and hinder adoption. XAI techniques aim to make AI systems more interpretable by providing explanations and visualizations of the decision-making process. This transparency is essential for overcoming skepticism and encouraging the adoption of CDSS in clinical practice. The findings of this review will offer insights into the current research landscape in the region and inform future research on the adoption and use of healthcare AI. These issues are particularly relevant in the ASEAN context, where varying levels of digital literacy and healthcare infrastructure necessitate explainable and transparent AI to build user trust and support effective integration.

Background:Brain tumors represent a diverse group of central nervous system neoplasms that require precise diagnosis and characterization for effective treatment. Advances in neuroimaging have improved the detection of these tumors; however, conventional interpretation methods remain time-intensive and subject to variability. Artificial intelligence (AI), particularly through machine learning and deep learning, has emerged as a powerful tool in neuro-oncological imaging, offering automation, consistency, and enhanced diagnostic performance. Objective:This review aims to provide a comprehensive overview of AI-assisted imaging tools used in the detection and characterization of brain tumors. It highlights key AI technologies, their clinical applications, performance compared to human experts, and the emerging trends shaping the future of AI in neuro-oncology. Methods:A narrative review was conducted of recent peer-reviewed studies focused on the application of AI in brain tumor imaging. Emphasis was placed on machine learning and deep learning models used for tumor segmentation, histopathologic and molecular subtype prediction, prognostic modeling, and treatment response monitoring. Technical challenges, ethical concerns, and regulatory considerations were also examined. Results:AI models have demonstrated high accuracy in tasks such as tumor segmentation, classification of tumor types and grades, non-invasive prediction of molecular markers (e.g., IDH mutation, MGMT methylation), and survival prediction. Emerging techniques such as federated learning, multimodal data integration, and explainable AI are addressing key limitations, including data privacy, generalizability, and clinical trust. Conclusion:AI-assisted imaging holds considerable promise in improving the accuracy, speed, and personalization of brain tumor diagnosis and management. For widespread clinical adoption, future efforts should focus on multi-institutional collaboration, prospective validation, regulatory alignment, and clinician education. With continued advancement, AI can become a valuable adjunct in the neuro-oncology diagnostic arsenal, ultimately contributing to better patient outcomes.

The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress. This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for a comprehensive literature review, analyzing over 9,350 publications from 2004 to 2023. Utilizing BERTopic modeling, 14 key themes in AI-driven cybersecurity were identified. Topics were clustered and validated through a combination of algorithmic and expert-driven evaluations, focusing on semantic relationships and coherence scores. AI applications in cybersecurity are concentrated around intrusion detection, malware classification, federated learning in privacy, IoT security, UAV systems and DDoS mitigation. Emerging fields such as adversarial machine learning, blockchain and deep learning are gaining traction. Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats. Global trends indicate significant contributions from the US, India, UK, and China, highlighting geographical diversity in research priorities. While AI enhances cybersecurity efficacy, challenges such as computational resource demands, adversarial vulnerabilities, and ethical concerns persist. More research in trustworthy AI, standardizing AI-driven methods, legislations for robust privacy protection amongst others is emphasized. The study also highlights key current and future areas of focus, including quantum machine learning, explainable AI, integrating humanized AI and deepfakes.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery.

Artificial intelligence (AI) has, in the recent past, experienced a rebirth with the growth of generative AI systems such as ChatGPT and Bard. These systems are trained with billions of parameters and have enabled widespread accessibility and understanding of AI among different user groups. Widespread adoption of AI has led to the need for understanding how machine learning (ML) models operate to build trust in them. An understanding of how these models generate their results remains a huge challenge that explainable AI seeks to solve. Federated learning (FL) grew out of the need to have privacy-preserving AI by having ML models that are decentralized but still share model parameters with a global model. This study sought to examine the extent of development of the explainable AI field within the FL environment in relation to the main contributions made, the types of FL, the sectors it is applied to, the models used, the methods applied by each study, and the databases from which sources are obtained. A systematic search in 8 electronic databases, namely, Web of Science Core Collection, Scopus, PubMed, ACM Digital Library, IEEE Xplore, Mendeley, BASE, and Google Scholar, was undertaken. A review of 26 studies revealed that research on explainable FL is steadily growing despite being concentrated in Europe and Asia. The key determinants of FL use were data privacy and limited training data. Horizontal FL remains the preferred approach for federated ML, whereas post hoc explainability techniques were preferred. There is potential for development of novel approaches and improvement of existing approaches in the explainable FL field, especially for critical areas. OSF Registries 10.17605/OSF.IO/Y85WA; https://osf.io/y85wa.

Artificial Intelligence (AI) is revolutionizing the prevention and control of breast cancer by improving risk assessment, prevention, and early diagnosis.Considering an emphasis on AI applications across the women's breast cancer spectrum, this review summarizes developments, existing applications, and future potential prospects. We conducted an in-depth review of the literature on AI applications in breast cancer risk prediction, prevention, and early detection from 2000 to 2025, with particular emphasis on Explainable AI (XAI), deep learning (DL), and machine learning (ML). We examined algorithmic fairness, model transparency, dataset representation, and clinical performance indicators. As compared to traditional methods, AI-based models continuously enhanced risk categorization, screening sensitivity, and early detection (AUCs ranging from 0.65 to 0.975). However, challenges remain in algorithmic bias, underrepresentation of minority populations, and limited external validation. Remarkably, 58% of public datasets focused on mammography, leaving gaps in modalities such as tomosynthesis and histopathology. AI technologies have an enormous number of opportunities for enhancing the diagnosis and treatment of breast cancer. However, transparent models, inclusive datasets, and standardized frameworks for explainability and external validation should be given the greatest attention in subsequent studies to ensure equitable and effective implementation.

Artificial intelligence (AI) is the most looked up technology with a diverse range of applications across all the fields, whether it is intelligent transportation systems (ITS), medicine, healthcare, military operations, or others. One such application is autonomous vehicles (AVs), which comes under the category of AI in ITS. Vehicular Adhoc Networks (VANET) makes communication possible between AVs in the system. The performance of each vehicle depends upon the information exchanged between AVs. False or malicious information can perturb the whole system leading to severe consequences. Hence, the detection of malicious vehicles is of utmost importance. We use machine learning (ML) algorithms to predict the flaw in the data transmitted. Recent papers that used the stacking ML approach gave an accuracy of 98.44%. Decision tree-based random forest is used to solve the problem in this paper. We achieved accuracy and F1 score of 98.43% and 98.5% respectively on the VeRiMi dataset in this paper. Explainable AI (XAI) is the method and technique to make the complex black-box ML and deep learning (DL) models more interpretable and understandable. We use a particular model interface of the evaluation metrics to explain and measure the model’s performance. Applying XAI to these complex AI models can ensure a cautious use of AI for AVs.

This article explores the application of artificial intelligence (AI) methods for demand forecasting in logistics systems. The purpose of this article is to develop a scientific and methodological approach to optimizing supply chain management based on artificial intelligence models, which improves the efficiency of planning, forecasting, and coordinating logistics processes. The methodological basis of the study is a scientific and methodological approach to optimizing supply chain management, which combines systemic, process, and intellectual-analytical levels of analysis of logistics processes. The systemic approach allows us to consider the supply chain as a complex of interrelated elements (manufacturers, warehouses, transport hubs, distributors, consumers), whose activities are aimed at achieving a common goal – increasing the efficiency and adaptability of the logistics system. The process approach involves structuring logistics operations in the form of continuous processes of planning, forecasting, transportation, storage, and distribution. The intellectual-analytical approach, which is central to this study, is implemented through the use of artificial intelligence models to improve decision-making, increase the accuracy of forecasts, and minimize risks in supply chains. The study examines the main approaches, including machine learning, neural networks, and time series analysis. A review of current literature (2019–2025) is provided, highlighting contemporary trends and challenges in this field. The analytical part includes a comparison of the effectiveness of different AI models based on their accuracy and computational complexity. An experimental hybrid LSTM CNN architecture with an attention mechanism and external factors (weather, calendar events) has been developed, and a comparative analysis with the basic ARIMA, Prophet, and Gradient Boosting models has been performed. A methodology for demand forecasting using a combination of recurrent neural networks (RNN) and gradient boosting algorithms has been proposed. The conclusions confirm the feasibility of integrating AI solutions into logistics systems to optimize logistics processes and outline directions for further research, in particular the application of graph neural networks and explainable AI.

Foodborne illnesses caused by harmful bacteria are a major global concern that poses a threat to public health. Traditional detection techniques are frequently time-consuming, labor-intensive, and perhaps inaccurate. The food industry can benefit from the use of artificial intelligence (AI) for the quick, precise, and automated detection of these microorganisms. Foodborne illnesses caused by harmful bacteria are a major public health concern worldwide. Traditional methods for identifying these bacteria are often time-consuming, costly, and may be inaccurate. The food industry can leverage artificial intelligence (AI) to quickly, accurately, and automatically identify these microorganisms. Machine learning (ML) methods such as support vector machines, random forests, and nearest neighbors are used in this context. AI models are capable of identifying pathogens like Salmonella, Listeria, and E. coli with accuracy rates of over 90%. AI facilitates automation and non-destructive testing, which minimizes human labor and errors. Integrating AI with sensor networks makes it possible to monitor food production in real time. The difficulties include securing high-quality datasets, enhancing model transparency, establishing uniform procedures, and securing regulatory approval. Ongoing research aims to solve these problems by developing better data sharing, explainable AI, and standards.

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Modern organizations are implementing artificial intelligence (AI) applications to improve human resource management practices through increased decision transparency and improved efficiency outcomes. Decision flexibility, employee acceptance issues, and the risk of bias create significant challenges in AI adoption in HR. This study examines the implementation of AI in the HR function to evaluate its impact on operational efficiency and employee well-being, using a comprehensive theoretical framework integrating Task-Technology Fit, Sociotechnical Systems, Job Demands-Resources, and Explainable AI. The study implemented a mixed-methods design involving explanatory sequential elements to analyze the effectiveness of AI systems and employee perceptions. Quantitative analysis revealed that AI technology improves HR efficiency by up to 45% despite posing challenges in decision-making flexibility. Qualitative exploration identified differences in AI acceptance across organizational levels, with 80% of employees indicating limited understanding of AI decision mechanisms and 60% concerned about potential bias. The study findings suggest that optimal AI implementation requires a collaborative human-AI model that combines algorithmic analysis with human oversight, especially for complex scenarios that require contextual interpretation. This study highlights the importance of algorithmic transparency and targeted communication strategies to increase technology adoption. Organizations should develop comprehensive Explainable AI mechanisms that effectively communicate decision rationales in accessible language, while simultaneously implementing monitoring protocols to evaluate and address potential patterns of bias. This research contributes to the understanding of specific factors that influence successful AI implementation in the HR context, emphasizing that technology optimization and human-centered design represent complementary priorities.

The operational efficiency of many industrial processes is greatly affected by condition monitoring, which has become more and more important in the detection and forecast of electrical machine failures. Early identification of possible problems and prompt and precise diagnosis reduce unscheduled downtime, lower maintenance costs, and prevent catastrophic failures. Traditional human-dependent diagnostic techniques are changing as a result of advances in artificial intelligence (AI), opening the door to automated and predictive maintenance plans. This paper provides a detailed examination of artificial intelligence (AI) applications in the prediction of electrical device failures, with a focus on techniques such as fuzzy systems, expert systems, artificial neural networks (ANNs), and complex machine-learning algorithms. These methods use both historical and present data to identify and predict problems and allow timely actions. The study looks at implementation challenges for AI-based diagnostic systems, including data dependencies, processing demands, and model interpretability, in addition to highlighting recent advances such as digital twins, explainable AI, and IoT integration. This review highlights the revolutionary potential of artificial intelligence (AI) in improving the sustainability, efficiency, and dependability of electrical machine systems, especially in the context of rotating machines, by addressing existing constraints and suggesting future research routes.

Advancing AI in healthcare: A comprehensive review of best practices