Search Results (41)

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Introduction: The rapid adoption of telerehabilitation in physiotherapy and occupational therapy has transformed healthcare delivery, offering new opportunities for patient-centered care. However, its implementation raises critical ethical and equity-related questions that require proactive strategies to ensure fair and responsible practices. This review examines how ethical disparities and equity-related challenges are reflected in the existing literature on telerehabilitation. Objective: To investigate the presence of ethical-disparity and equity-related aspects in the provision of telerehabilitation in physiotherapy and occupational therapy as reflected in the literature. Data Sources: A rapid review methodology was employed to explore ethical and equity-related challenges in telerehabilitation. The search included articles published in English and French between 2010 and 2023 from the Medline and Embase databases. Study Selection: Articles were selected based on their relevance to ethical and equity considerations in telerehabilitation. A total of 1750 sources were initially identified, with 67 articles meeting the eligibility criteria for inclusion in this review. Data Extraction: Data were extracted based on variables such as age, gender, ethnicity, morbidity, cost, privacy, confidentiality, and autonomy. The data extraction and analysis were guided by the Progress Plus and Metaverse Equitable Rehabilitation Therapy frameworks. Data Synthesis: The findings were analyzed and discussed using a narrative synthesis approach. The results highlighted key ethical considerations, including adverse events, patient autonomy, and privacy issues. Equity-related aspects were examined, access to rehabilitation services and gender considerations. Disparities in technology access, socioeconomic status, and ethnicity were also identified. Conclusions: This rapid review highlights the growing relevance of ethical and equity considerations in the design and delivery of telerehabilitation within physiotherapy and occupational therapy. The findings show inconsistent reporting and limited depth in addressing key domains such as patient autonomy, privacy, and adverse events, alongside disparities related to age, gender, socioeconomic status, and geographic access. Although telerehabilitation holds promise for expanding access, particularly in underserved areas, this potential remains unevenly realized. The review underscores the critical need for structured, equity-driven, and ethically grounded frameworks such as the Metaverse Equitable Rehabilitation THerapy (MERTH) framework to guide future implementation, research, and policy. Full article

Ensuring the trustworthiness of artificial intelligence (AI) systems is critical as they become increasingly integrated into domains like healthcare, finance, and public administration. This paper explores frameworks and metrics for evaluating AI trustworthiness, focusing on key principles such as fairness, transparency, privacy, and security. This study is guided by two central questions: how can trust in AI systems be systematically measured across the AI lifecycle, and what are the trade-offs involved when optimizing for different trustworthiness dimensions? By examining frameworks such as the NIST AI Risk Management Framework (AI RMF), the AI Trust Framework and Maturity Model (AI-TMM), and ISO/IEC standards, this study bridges theoretical insights with practical applications. We identify major risks across the AI lifecycle stages and outline various metrics to address challenges in system reliability, bias mitigation, and model explainability. This study includes a comparative analysis of existing standards and their application across industries to illustrate their effectiveness. Real-world case studies, including applications in healthcare, financial services, and autonomous systems, demonstrate approaches to applying trust metrics. The findings reveal that achieving trustworthiness involves navigating trade-offs between competing metrics, such as fairness versus efficiency or privacy versus transparency, and emphasizes the importance of interdisciplinary collaboration for robust AI governance. Emerging trends suggest the need for adaptive frameworks for AI trustworthiness that evolve alongside advancements in AI technologies. This paper contributes to the field by proposing a comprehensive review of existing frameworks with guidelines for building resilient, ethical, and transparent AI systems, ensuring their alignment with regulatory requirements and societal expectations. Full article

The rapid integration of the Internet of Medical Things (IoMT) into healthcare systems raises urgent demands for secure communication mechanisms capable of protecting sensitive patient data. Quantum key agreement (QKA), a collaborative approach to key generation based on quantum principles, provides an attractive alternative to traditional quantum key distribution (QKD), as it eliminates dependence on a trusted authority and ensures equal participation from all users. QKA demonstrates particular suitability for IoMT’s decentralized medical networks by eliminating trusted authority dependence while ensuring equitable participation among all participants. This addresses fundamental challenges where centralized trust models introduce vulnerabilities and asymmetric access patterns that compromise egalitarian principles essential for medical data sharing. However, practical QKA applications in IoMT remain limited, particularly for schemes that avoid complex entanglement operations and authenticated classical channels. Among the few QKA protocols employing Grover’s search algorithm (GSA), existing proposals potentially suffer from limitations in fairness and security. In this paper, the author proposes an improved GSA-based QKA protocol that ensures fairness, security, and correctness without requiring an authenticated classical communication channel. The proposed scheme guarantees that each participant’s input equally contributes to the final key, preventing manipulation by any user subgroup. The scheme combines Grover’s algorithm with the decoy photon technique to ensure secure quantum transmission. Security analysis confirms resistance to external attacks, including intercept-resend, entanglement probes, and device-level exploits, as well as insider threats such as parameter manipulation. Fairness is achieved through a symmetric protocol design rooted in quantum mechanical principles. Efficiency evaluation shows a theoretical efficiency of approximately 25%, while eliminating the need for quantum memory. These results position the proposed protocol as a practical and scalable solution for future secure quantum communication systems, particularly within distributed IoMT environments. Full article

This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through advanced natural language processing capabilities. Current implementations demonstrate LLMs’ promising applications across clinical decision support, medical education, diagnostics, and patient care, while highlighting critical challenges in privacy, ethical deployment, and factual accuracy that require resolution for responsible integration into healthcare systems. This paper provides a comprehensive understanding of the background of healthcare LLMs, the evolution and architectural foundation, and the multimodal capabilities. Key methodological aspects—such as domain-specific data acquisition, large-scale pre-training, supervised fine-tuning, prompt engineering, and in-context learning—are explored in the context of healthcare use cases. The paper highlights the trends and categorizes prominent application areas in medicine. Additionally, it critically examines the prevailing technical and social challenges of healthcare LLMs, including issues of model bias, interpretability, ethics, governance, fairness, equity, data privacy, and regulatory compliance. The survey concludes with an outlook on emerging research directions and strategic recommendations for the development and deployment of healthcare LLMs. Full article

Pneumonia remains a leading cause of respiratory morbidity and mortality, underscoring the need for rapid and accurate diagnosis to enable timely treatment and prevent complications. This study introduces PELM (Pneumonia Ensemble Learning Model), a novel deep learning framework for automated pneumonia detection using chest X-ray (CXR) images. The model integrates four high-performing architectures—InceptionV3, VGG16, ResNet50, and Vision Transformer (ViT)—via feature-level concatenation to exploit complementary feature representations. A curated, large-scale dataset comprising 50,000 PA-view CXR images was assembled from NIH ChestX-ray14, CheXpert, PadChest, and Kaggle CXR Pneumonia datasets, including both pneumonia and non-pneumonia cases. To ensure fair benchmarking, all models were trained and evaluated under identical preprocessing and hyperparameter settings. PELM achieved outstanding performance, with 96% accuracy, 99% precision, 91% recall, 95% F1-score, 91% specificity, and an AUC of 0.91—surpassing individual model baselines and previously published methods. Additionally, comparative experiments were conducted using tabular clinical data from over 10,000 patients, enabling a direct evaluation of image-based and structured-data-based classification pipelines. These results demonstrate that ensemble learning with hybrid architectures significantly enhances diagnostic accuracy and generalization. The proposed approach is computationally efficient, clinically scalable, and particularly well-suited for deployment in low-resource healthcare settings, where radiologist access may be limited. PELM represents a promising advancement toward reliable, interpretable, and accessible AI-assisted pneumonia screening in global clinical practice. Full article

The deployment of emotion-recognition technologies expands across healthcare education and gaming sectors to improve human–computer interaction. These systems examine facial expressions together with vocal tone and physiological signals, which include pupil size and electroencephalogram (EEG), to detect emotional states and deliver customized responses. The technology provides benefits through accessibility, responsiveness, and adaptability but generates multiple complex ethical issues. The combination of emotional profiling with biased algorithmic interpretations of culturally diverse expressions and affective data collection without meaningful consent presents major ethical concerns. The increased presence of these systems in classrooms, therapy sessions, and personal devices makes the potential for misuse or misinterpretation more critical. The paper integrates findings from literature review and initial emotion-recognition studies to create a conceptual framework that prioritizes data dignity, algorithmic accountability, and user agency and presents a conceptual framework that addresses these risks and includes safeguards for participants’ emotional well-being. The framework introduces structural safeguards which include data minimization, adaptive consent mechanisms, and transparent model logic as a more complete solution than privacy or fairness approaches. The authors present functional recommendations that guide developers to create ethically robust systems that match user principles and regulatory requirements. The development of real-time feedback loops for user awareness should be combined with clear disclosures about data use and participatory design practices. The successful oversight of these systems requires interdisciplinary work between researchers, policymakers, designers, and ethicists. The paper provides practical ethical recommendations for developing affective computing systems that advance the field while maintaining responsible deployment and governance in academic research and industry settings. The findings hold particular importance for high-stakes applications including healthcare, education, and workplace monitoring systems that use emotion-recognition technology. Full article

Parkinson’s disease (PD) is an age-related neurological disorder with increasing incidence and modifiable risk factors. People exposed to pesticides have up to a 2-fold higher risk of developing PD. Use of personal protective equipment (PPE) when using pesticides can lower an individual’s exposure. We examined awareness of the relationship between pesticides and PD risk in individuals working/living on farms in Ireland. We also investigated the practice of behaviours aimed at mitigating exposure, such as using PPE. An online survey was completed by a sample of the farming community (n = 707) attending agricultural fairs, and included demographics, lifetime/current residence/work on farms, pesticide contact, PPE use, PD diagnosis, and awareness of pesticide–PD association. Among participants, 51% worked/lived on farms and 62% reported contact with pesticides. Only 69% of those with pesticide contact reported using PPE, with gloves (57%) and masks (50%) most commonly used. Only 22% were aware of an association between PD and pesticides, and awareness did not increase PPE use. Among people with PD, only 40% had knowledge of the risk. We found that in a highly agricultural economy, occupational exposure to pesticides is common, but mitigation behaviours are not optimal. Educational campaigns to improve awareness of health risks from pesticides and to encourage PPE use could lower the personal and healthcare burden of PD and other health outcomes. Full article

Work is a key social determinant of mental health, and adverse organizational conditions in healthcare settings increase psychosocial risks. Leadership influences workplace well-being, yet its impact on mental health and gender inequalities remains underexplored. Despite the feminization of the health sector, disparities persist in leadership access, role expectations, and work–family reconciliation, exacerbating occupational stress. Aims: This study examines leadership practices in public hospitals, focusing on their relationship with mental health, organizational dimensions (recognition and role stress), and gender disparities. It explores the perspectives of both workers and managers to understand how leadership shapes workplace conditions and well-being. Methods: A qualitative, cross-sectional study was conducted as part of the FONDECYT project 1220547. Semi-structured interviews were conducted with 64 workers from public hospitals in Santiago, Chile, including clinical and administrative staff. The analysis supported by Grounded Theory identified key categories: constructive and destructive leadership, recognition, role stress, and gender disparities in leadership. Results: Constructive leadership—characterized by communication, fairness, and recognition—was linked to a healthier work environment and improved well-being. In contrast, destructive leadership (characterized by abuse of power and imposition, or inaction, lack of support, and absence of effective direction) contributed to role stress, workplace mistreatment, and job dissatisfaction. Recognition was a crucial but insufficient motivator, as the lack of formal mechanisms led to frustration. Role stress emerged as a significant risk for well-being, with subordinates experiencing overload, ambiguity, and conflicting expectations. Gender inequalities persisted as women faced more tremendous barriers to leadership and difficulties balancing work and family responsibilities. Workers and managers had differing perspectives, with subordinates prioritizing fairness and recognition while managers emphasized operational constraints. Conclusions: Leadership training should emphasize trust, equity, and recognition to enhance workplace well-being. Institutional policies must address role stress, strengthen formal recognition systems, and promote gender equity in leadership. Future research should integrate quantitative methods to explore leadership’s impact on organizational conditions and mental health outcomes. Full article

Background: Neuroenhancement in sports, through pharmacological and non-pharmacological methods, is a complex and highly debated topic with no definitive regulatory framework established by the World Anti-Doping Agency (WADA). The hypothesis that dermatological changes could serve as observable biomarkers for neurodoping introduces a novel and promising approach to detecting and understanding the physiological impacts of cognitive enhancers in athletes. As neurodoping methods become increasingly sophisticated, developing objective, reliable, and non-invasive detection strategies is imperative. Utilizing dermatological signs as a diagnostic tool for internal neurophysiological changes could offer critical insights into the safety, fairness, and ethical considerations of cognitive enhancement in competitive sports. A systematic correlation between skin manifestations, the timeline of neurodoping practices, and the intensity of cognitive enhancement methods could provide healthcare professionals valuable tools for monitoring athletes’ health and ensuring strict compliance with anti-doping regulations. Methods: Due to the limited body of research on this topic, a systematic review of the literature was conducted, spanning from 2010 to 31 December 2024, using databases such as PubMed, Science Direct, and Google Scholar. This study followed the 2020 PRISMA guidelines and included English-language articles published within the specified period, focusing on skin lesions as adverse reactions to pharmacological and non-pharmacological neuroenhancement methods. The research employed targeted keywords, including “skin lesions AND rivastigmine”, “skin lesions AND galantamine”, “skin lesions AND donepezil”, “skin lesions AND memantine”, and “skin lesions AND transcranial direct electrical stimulation”. Given the scarcity of studies directly addressing neurodoping in sports, the search criteria were broadened to include skin reactions associated with cognitive enhancers and brain stimulation. Eighteen relevant articles were identified and analyzed. Results: The review identified rivastigmine patches as the most used pharmacological method for neuroenhancement, with pruritic (itchy) skin lesions as a frequent adverse effect. Donepezil was associated with fewer and primarily non-pruritic skin reactions. Among non-pharmacological methods, transcranial direct current stimulation (tDCS) was notably linked to skin burns, primarily due to inadequate electrode–skin contact, prolonged exposure, or excessive current intensity. These findings suggest that specific dermatological manifestations could serve as potential indicators of neurodoping practices in athletes. Conclusions: Although specific neuroenhancement methods demonstrate distinctive dermatological side effects that might signal neurodoping, the current lack of robust clinical data involving athletes limits the ability to draw definitive conclusions. Athletes who engage in neurodoping without medical supervision are at an elevated risk of adverse dermatological and systemic reactions. Skin lesions, therefore, could represent a valuable early diagnostic marker for the inappropriate use or overuse of cognitive-enhancing drugs or neuromodulation therapies. The findings emphasize the need for focused clinical research to establish validated dermatological criteria for detecting neurodoping. This research could contribute significantly to the ongoing neuroethical discourse regarding the legitimacy and safety of cognitive enhancement in sports. Full article

Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical and social challenges. To address these challenges, this research utilizes two prominent fairness libraries, Fairlearn by Microsoft and AIF360 by IBM. These libraries offer comprehensive frameworks for fairness analysis, providing tools to evaluate fairness metrics, visualize results, and implement bias mitigation algorithms. The study focuses on assessing and mitigating biases for unstructured datasets using Computer Vision (CV) and Natural Language Processing (NLP) models. The primary objective is to present a comparative analysis of the performance of mitigation algorithms from the two fairness libraries. This analysis involves applying the algorithms individually, one at a time, in one of the stages of the ML lifecycle, pre-processing, in-processing, or post-processing, as well as sequentially across more than one stage. The results reveal that some sequential applications improve the performance of mitigation algorithms by effectively reducing bias while maintaining the model’s performance. Publicly available datasets from Kaggle were chosen for this research, providing a practical context for evaluating fairness in real-world machine learning workflows. Full article

Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, and societal challenges. Key concerns include data privacy risks, algorithmic bias, and regulatory gaps that struggle to keep pace with AI advancements. This study aims to synthesize a multidisciplinary framework for trustworthy AI in healthcare, focusing on transparency, accountability, fairness, sustainability, and global collaboration. It moves beyond high-level ethical discussions to provide actionable strategies for implementing trustworthy AI in clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, and Web of Science. Studies were selected based on relevance to AI ethics, governance, and policy in healthcare, prioritizing peer-reviewed articles, policy analyses, case studies, and ethical guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives from clinicians, ethicists, policymakers, and technologists, offering a holistic “ecosystem” view of AI. No clinical trials or patient-level interventions were conducted. Results: The analysis identifies key gaps in current AI governance and introduces the Regulatory Genome—an adaptive AI oversight framework aligned with global policy trends and Sustainable Development Goals. It introduces quantifiable trustworthiness metrics, a comparative analysis of AI categories for clinical applications, and bias mitigation strategies. Additionally, it presents interdisciplinary policy recommendations for aligning AI deployment with ethical, regulatory, and environmental sustainability goals. This study emphasizes measurable standards, multi-stakeholder engagement strategies, and global partnerships to ensure that future AI innovations meet ethical and practical healthcare needs. Conclusions: Trustworthy AI in healthcare requires more than technical advancements—it demands robust ethical safeguards, proactive regulation, and continuous collaboration. By adopting the recommended roadmap, stakeholders can foster responsible innovation, improve patient outcomes, and maintain public trust in AI-driven healthcare. Full article

Artificial Intelligence (AI) is reshaping healthcare through advancements in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and generate new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions. Full article

This study aims to investigate the influence of leadership on the self-efficacy of healthcare professionals. Additionally, it seeks to explore whether stress mediates the relationship between leadership and self-efficacy. Specifically, our study is focused on both transactional leadership and laissez-faire leadership, which are commonly practiced by healthcare professionals due to the settings of healthcare environments. This study utilized a structured questionnaire for measuring the leadership, stress, and self-efficacy of healthcare professionals. Data collection involved respondents rating these statements on a Likert scale. The sample consisted of 395 participants employed in healthcare organizations in Serbia. The analysis employed partial least squares structural equation modeling (PLS-SEM). The research findings indicate that laissez-faire leadership is positively associated with stress, while no significant negative impact on self-efficacy was observed. Transactional leadership did not demonstrate a significant relationship with reduced stress but was found to positively influence self-efficacy. Moreover, stress was identified as negatively impacting self-efficacy and mediated the association between laissez-faire leadership and self-efficacy, although no mediating effect was found for transactional leadership. This study underscores the critical role of leadership style in shaping the well-being and self-efficacy of healthcare professionals. By understanding how different leadership approaches impact employee stress and job satisfaction, healthcare organizations can tailor their management practices to foster a supportive work environment and enhance overall performance. The results emphasize the need for leaders to balance organizational objectives with employee needs, demonstrating effective communication and adaptability to promote a positive workplace culture. Full article

Background: This paper reviews cost containment policies to control pharmaceutical expenditure either by regulating the pharmaceutical industry or targeting the demand side. Methods: The method used was the narrative literature review of studies which assessed the effect of pharmaceutical cost containment policies. Results: Governments worldwide have implemented a great variety of policy measures to manage pharmaceutical expenditure while ensuring fair access to essential medicines. Cost-sharing schemes, value-based pricing, reimbursement, reference pricing, payback mechanisms and the substitution of original drugs with generics and biosimilars are pivotal in these efforts, albeit with differing effectiveness across healthcare systems. Overall, it appears that any gains may be outweighed by the unfavorable effects of policies impacting patients. Although interventions have been created to improve physicians’ prescribing practice, they often achieve very minor benefits and at considerable cost. Policy measures pertaining to the regulation of the supply side must be supported by thorough evaluation in order to ascertain costs and effects and guarantee that unintended consequences are minimized. Conclusions: Policymakers frequently enact numerous laws and regulations to control pharmaceutical expenditure, even if there is limited evidence that they are cost-effective. The most crucial component of any policy’s success, regardless of the one selected, is its evaluation. Further research is needed to develop context-specific guidance that balances cost containment, equity and sustainability. Full article