AI-Driven Techniques for Lupus Detection and Management Challenges and Future Directions

The rapidly aging global population presents significant challenges to healthcare systems worldwide. This review explores the transformative role of artificial intelligence (AI) in addressing the complex healthcare needs of the elderly. AI applications in elderly health care span various domains, including diagnostics and disease prediction, personalized treatment planning, remote monitoring, cognitive assistance, fall prevention, and medication management. The integration of AI technologies offers numerous benefits, such as improved diagnostic accuracy, enhanced personalization of care, continuous health monitoring, and support for independent living. However, the implementation of AI in elderly health care also faces challenges related to data privacy, ethical considerations, technology adoption, and regulatory frameworks. Future directions in this field include advancements in natural language processing, multi-modal data integration, and the development of explainable AI systems. As AI continues to evolve, its potential to improve the quality of life and health outcomes for older adults is substantial. This review underscores the importance of collaborative efforts between healthcare providers, technologists, policymakers, and elderly individuals to fully realize the benefits of AI in elderly health care when addressing associated challenges.

The integration of Artificial Intelligence (AI) into healthcare systems is revolutionising the way medical data is processed, analysed, and utilised to improve patient outcomes. "From Data to Care: The Role of Artificial Intelligence in Modern Healthcare Systems" explores the transformative impact of AI technologies on healthcare practices, focusing on the transition from raw medical data to actionable insights for patient care. AI-driven tools, such as machine learning algorithms, natural language processing, and predictive analytics, are enabling more accurate diagnoses, personalised treatment plans, and efficient healthcare delivery. By enhancing the ability to process vast amounts of data, AI supports clinicians in making data-driven decisions, optimising workflows, and improving patient safety. This paper also addresses the challenges of implementing AI in healthcare, including data privacy concerns, regulatory hurdles, and the need for healthcare professionals to adapt to AI-enhanced environments. The potential for AI to drive innovation in preventive care, remote monitoring, and decision support systems positions it as a pivotal technology for the future of healthcare. Ultimately, AI is not just about improving clinical outcomes, but also about enhancing the overall patient experience, making healthcare systems more accessible, efficient, and effective.

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.

Artificial Intelligence (AI) is transforming healthcare delivery through its applications in early disease detection and personalized treatment planning. This comprehensive technical article examines the current landscape of AI integration in medical practice, highlighting how advanced algorithms analyze complex healthcare data to identify disease indicators earlier than conventional methods and develop individualized therapeutic approaches. It covers supervised, unsupervised, and reinforcement learning techniques being applied across various medical domains, particularly in oncology and cardiovascular disease. By leveraging diverse data sources—including electronic health records, medical imaging, genomic information, and wearable device data—AI systems demonstrate promising capabilities in revolutionizing diagnostic accuracy, treatment selection, and chronic disease management. The article also addresses significant challenges in implementing healthcare AI, including data quality concerns, integration difficulties, regulatory uncertainties, and ethical considerations. As healthcare organizations navigate these implementation barriers, emerging approaches such as federated learning, explainable AI, and continuous learning systems offer potential solutions to expand AI adoption while ensuring equitable, transparent, and clinically valuable applications.

Early warning score (EWS) have become an essential component of patient safety strategies in healthcare environments worldwide. These systems aim to identify patients at risk of clinical deterioration by evaluating vital signs and other physiological parameters, enabling timely intervention by rapid response teams. Despite proven benefits and widespread adoption, conventional EWS have limitations that may affect their ability to effectively detect and respond to patient deterioration. There is growing interest in integrating continuous multimodal monitoring technologies and advanced analytics, particularly artificial intelligence (AI) and machine learning (ML)-based approaches, to address these limitations and enhance EWS performance. This review provides a comprehensive overview of the current state and potential future directions of AI-based bio-signal monitoring in early warning system. It examines emerging trends and techniques in AI and ML for bio-signal analysis, exploring the possibilities and potential applications of various bio-signals such as electroencephalography, electrocardiography, electromyography in early warning system. However, significant challenges exist in developing and implementing AI-based bio-signal monitoring systems in early warning system, including data acquisition strategies, data quality and standardization, interpretability and explainability, validation and regulatory approval, integration into clinical workflows, and ethical and legal considerations. Addressing these challenges requires a multidisciplinary approach involving close collaboration between healthcare professionals, data scientists, engineers, and other stakeholders. Future research should focus on developing advanced data fusion techniques, personalized adaptive models, real-time and continuous monitoring, explainable and reliable AI, and regulatory and ethical frameworks. By addressing these challenges and opportunities, the integration of AI and bio-signals into early warning systems can enhance patient monitoring and clinical decision support, ultimately improving healthcare quality and safety. In conclusion, integrating AI and bio-signals into the early warning system represents a promising approach to improve patient care outcomes and support clinical decision-making. As research in this field continues to evolve, it is crucial to develop safe, effective, and ethically responsible solutions that can be seamlessly integrated into clinical practice, harnessing the power of innovative technology to enhance patient care and improve individual and population health and well-being.

Sepsis is a leading cause of mortality in intensive care units (ICUs), and its rapid progression poses significant challenges for early detection. Traditional scoring systems, such as SOFA and APACHE II, provide clinical benchmarks but often fail to capture subtle early signs of deterioration. Machine learning (ML) and deep learning (DL) models have demonstrated strong predictive performance; however, their “black-box” nature limits transparency, clinician trust, and adoption in real-world ICU settings. Explainable artificial intelligence (XAI) clarifies how predictions are derived. This systematic review examines studies published between 2020 and 2025 that applied XAI methods, including SHAP, LIME, Grad-CAM, and sensitivity analysis, to predict sepsis onset and ICU mortality. We analyzed the datasets used (e.g., MIMIC-III/IV, Emory University Hospital, Ruijin Hospital), model architectures, interpretability strategies, and the clinical features most strongly associated with predictions. Findings indicate that XAI-enhanced models not only maintain high predictive accuracy but also highlight clinically meaningful indicators such as respiratory rate, blood urea nitrogen (BUN), urine output, and the Glasgow Coma Scale (GCS), thereby improving clinician confidence and facilitating adoption. Despite these advances, challenges remain, including limited prospective evaluation, inconsistent interpretability metrics, and variable integration into clinical workflows. We conclude by recommending future research priorities, including real-world validation, user-centered design, and multimodal data integration, to ensure that XAI can reliably support timely and informed decision-making in critical care environments.

Artificial intelligence (AI) technology has shown tremendous contributions in various applications like speech recognition, expert systems, computer vision, robotics, and gaming. machine learning (ML) and deep learning (DL) algorithms under AI address problems such as prediction, classification, and regression. AI has touched many domains. The results or the predictions generated by these algorithms are not easily accepted by the user. Especially, the Healthcare domain is facing a great challenge in accepting the results or the predictions with the concern, Are AI results reliable, correct, and ethical? Doctors or medical practitioners are not ready to treat patients based on results or suggestions generated by AI algorithms. Hence, a technology that can explain how the results returned by AI algorithms are trustworthy, transparent, and interpretable was strongly needed. This need has given rise to the latest technology-explainable artificial intelligence (XAI). With the use of XAI, all the predictions, classifications made by AI algorithms are explainable, auditable, comprehensive, validating, and socially acceptable. This paper discusses explaining the results of breast cancer prediction as a case study. The results show that such an explanation will build trust in the doctors and hence will increase the acceptance of the AI-based systems.

Leukemia, a heterogeneous group of blood cancers, poses significant challenges to global health due to its complexity, diverse risk factors, and variable outcomes. Accurate and early diagnosis is critical but remains a significant hurdle, particularly in low-resource settings. Recent advancements in artificial intelligence (AI) and image processing offer transformative solutions to improve leukemia detection and classification, addressing limitations in traditional diagnostic methods. This study systematically reviewed over 25,000 scientific articles sourced from Scopus, employing a PRISMA-guided methodology to ensure a comprehensive and rigorous analysis. The analysis focused on the application of AI, particularly convolutional neural networks (CNNs), in diagnosing four primary leukemia types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML). It also examined global epidemiological trends, risk factors, and disparities in healthcare access. Key risk factors for leukemia include genetic syndromes like Down syndrome, environmental exposures to toxins such as benzene, ionizing radiation, and viral infections. Socio-economic disparities and geographical differences significantly impact leukemia incidence and outcomes. AI-based models, especially CNNs, demonstrated enhanced accuracy, speed, and reliability in diagnosing leukemia compared to traditional methods. However, challenges such as data variability, model scalability, and unequal access to AI technologies continue to hinder widespread adoption. AI and image processing technologies hold immense potential to revolutionize leukemia diagnostics by enabling early detection, precise classification, and personalized treatment planning. Addressing critical challenges, including data standardization and equitable access to these technologies, will be vital for global application. This review highlights the transformative role of AI in improving leukemia outcomes and advancing precision medicine worldwide.

Artificial intelligence (AI) and explainable artificial intelligence (XAI) are advancing rapidly, with the potential to deliver significant benefits to modern society. The healthcare sector, in particular, has experienced transformative changes; overall, these technologies are helping to address numerous challenges, such as cancer cell detection, tumour zone identification in animal bodies, predictions of major and minor diseases, diagnosis, and more. This article provides an in‐depth and detailed overview of AI and XAI, focusing on recent trends and their implications for advancing Healthcare 5.0 applications. Initially, the study examines the key concepts and exceptional features of AI, XAI, and Healthcare 5.0. Additional emphasis is placed on state‐of‐the‐art practices currently being implemented in healthcare, particularly those involving AI and XAI. Subsequently, it establishes a coherent link between AI and XAI in Healthcare 5.0, grounded in contemporary advancements. Based on the findings, algorithms are recommended to address initial obstacles to integrating AI into the Healthcare 5.0 framework. Proposals for further enhancing Healthcare 5.0 performance through the integration of XAI and its unique features are discussed in detail. The work also provides in‐depth implementation strategies and highlights model‐specific trends within AI and XAI frameworks in Healthcare 5.0. Particular attention is given to AI model predictions in healthcare settings, emphasising their contributions to improved patient feedback and the delivery of more sophisticated care. Most importantly, this research highlights the potential for AI and XAI to support sustainable advancements in Healthcare 5.0 applications. Finally, significant issues are analysed, and an open discussion is presented on future guidelines for the blending of AI with XAI, and Healthcare 5.0 applications.

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry.

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.

Artificial intelligence (AI) has the potential to transform medical informatics by supporting clinical decision-making, reducing diagnostic errors, and improving workflows and efficiency. However, successful integration of AI-based decision support systems depends on careful consideration of human-AI collaboration, trust, skill maintenance, and automation bias. This work proposes five central questions to guide future research in medical informatics and human-computer interface (HCI). We focus on AI-based clinical decision support systems, including computer vision algorithms for medical imaging (radiology, pathology), natural language processing for structured and unstructured electronic health record (EHR) data, and rule-based systems. Relevant data modalities include clinician-acquired images, EHR text, and increasingly, patient-generated content in telehealth contexts. We review existing evidence regarding diagnostic errors across specialties, the effectiveness and risks of AI tools in reducing perceptual and interpretive errors, and the human factors influencing diagnostic decision-making in AI-enabled contexts. We synthesize insights from medicine, cognitive science, and HCI to identify gaps in knowledge and propose five key questions for continued research. Diagnostic errors remain common across medicine, with AI offering potential to reduce both perceptual and interpretive errors. However, the impact of AI depends critically on how and when information is presented. Studies indicate that delayed or toggleable cues may outperform immediate ones, but attentional capture, overreliance, and bias remain significant risks. Explainable AI provides transparency but can also bias decisions. Long-term reliance on AI may erode clinician skills, particularly for trainees and in low-prevalence contexts. Historical failures of computer-aided diagnosis in mammography highlight these challenges. Effective AI integration requires human-centered and adaptive design. Five central research questions address: (1) what type and format of information AI should provide; (2) when information should be presented; (3) how explainable AI affects diagnostic decisions; (4) how AI influences automation bias and complacency; and (5) the risks of skill decay due to reliance on AI. Each question underscores the importance of balancing efficiency, accuracy, and clinician expertise while mitigating bias and skill degradation. AI holds promise for improving diagnostic accuracy and efficiency, but realizing its potential requires post-deployment evaluation, equitable access, clinician oversight, and targeted training. AI must complement, rather than replace, human expertise, ensuring safe, effective, and sustainable integration into diagnostic decision-making. Addressing these challenges proactively can maximize AI's potential across healthcare and other high-stakes domains.

Employee attrition is a critical challenge for organizations, impacting productivity, operational costs, and workforce stability. Traditional approaches to managing attrition rely on reactive strategies, often failing to provide predictive insights. The advent of Artificial Intelligence (AI) has transformed attrition management by enabling data-driven decision-making, predictive analytics, and proactive employee engagement.This research explores the role of AI in predicting and managing employee attrition through machine learning algorithms, natural language processing (NLP), and AI-driven sentiment analysis. AI models analyze vast datasets, including employee performance metrics, engagement surveys, and organizational culture indicators, to identify early warning signs of attrition. Predictive analytics empowers HR professionals to implement targeted retention strategies, enhance employee experience, and reduce voluntary turnover.Furthermore, AI-driven chatbots and virtual HR assistants contribute to employee satisfaction by offering personalized career development suggestions, real-time feedback, and mental well-being support. Explainable AI (XAI) frameworks ensure transparency in AI-driven decisions, fostering trust between employees and organizations. Despite AI’s potential, ethical concerns, data privacy, and algorithmic biases remain key challenges that require robust governance frameworks.This study provides a comprehensive analysis of AI applications in attrition management, highlighting case studies from multinational corporations that have successfully integrated AI for workforce retention. The findings underscore AI's transformative potential in HRM, enabling organizations to shift from reactive to proactive attrition management strategies. The paper concludes with future research directions on AI’s evolving role in predictive HR analytics and its integration with emerging technologies like blockchain and the metaverse for enhanced workforce planning.

Artificial Intelligence (AI) has emerged as a transformative force in the healthcare domain, revolutionizing various aspects of medical research, diagnostics, treatment, and patient care. This paper provides an overview of recent developments and applications of AI in healthcare, highlighting its potential to enhance efficiency, accuracy, and accessibility in medical practices. The integration of machine learning algorithms, natural language processing, and computer vision techniques has enabled AI systems to analyze vast amounts of medical data, support clinical decision-making, and personalize treatment plans. Additionally, AI-powered technologies play a crucial role in predictive analytics, early disease detection, and the optimization of healthcare workflows. Despite the promising advancements, challenges related to data privacy, ethical considerations, and regulatory frameworks need to be addressed to fully harness the benefits of AI in healthcare.

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).