Electrophysiological measurement of the visual system: advances, applications, and the role of artificial intelligence

Role of artificial intelligence in financial leasing: Assessing bias and real-time value

Artificial intelligence (AI) is rapidly transforming human resource management, particularly in recruitment and selection. This study examines the multifaceted role of AI-driven tools in modern hiring processes, analyzing their practical applications, underlying technologies, organizational benefits, ethical challenges, legal implications, and future trajectory. Drawing on empirical studies, industry reports, theoretical frameworks, and real-world case studies from organizations including Unilever, IBM, and Amazon, this research provides a comprehensive assessment of how organizations across industries are integrating AI into talent acquisition pipelines. The findings reveal that while AI significantly enhances efficiency, scalability, and data-driven decision-making, it also introduces substantial risks related to algorithmic bias, privacy concerns, reduced human oversight, legal exposure, and negative candidate experience. The paper synthesizes these findings into a strategic framework and concludes with actionable recommendations for organizations seeking to adopt AI in recruitment responsibly, ethically, and equitably.

Purpose This article integrates insights from bricolage theory and the dynamic capability (DC) framework to explore the potentialities and dangers of artificial intelligence (AI) in the informal sector, where microenterprises could harness its powers to transform their business models and scale, or risk falling further behind in the wake of AI-enabled disruption. Design/methodology/approach This article takes a conceptual approach complemented with case illustrations. In the first part, it draws on bricolage and DCs theories to introduce nine new propositions that explicate the dynamic, sometimes bidirectional, relationships, between AI, digital bricolage, DCs and enterprise growth and competitiveness. In the second part, it highlights three illustrative cases of microenterprises to further elucidate these relationships. Findings This study proposes a novel framework integrating AI, digital bricolage and DCs to enhance the performance of informal microenterprises. It highlights the role of digital bricolage as a mechanism for adapting existing resources to develop AI capabilities, and the complementary role of DC in deploying AI for growth, scaling and competitiveness. The study demonstrates AI's role in strengthening opportunity sensing, seizing and transformative capacities that differentiate struggling enterprises from thriving ones, while also addressing critical limitations such as infrastructural inequities and fragmented skills. Practical implications The study offers valuable practical implications for fostering inclusive digital transformation in informal microenterprises. It highlights the role of digital bricolage in enabling resource-constrained entrepreneurs to creatively adapt and deploy AI for value creation, operational efficiency and agility. Policymakers and practitioners can leverage these insights to address barriers such as infrastructural inequities and skill gaps, fostering AI adoption. This approach supports sustainable competitiveness and market integration for marginalised enterprises. Originality/value This study proposes a novel framework integrating AI, digital bricolage and DCs to explicate the mechanisms and processes through which informal microenterprises achieve differential outcomes that propel some microenterprises to growth and scaling, on the one hand, while leaving others to fall further behind. To the best of our knowledge, this is the first article that aims to unpack the double-edged sword of AI as both a potential leveller and stratifier in the informal sector.

Artificial intelligence (AI) is rapidly evolving worldwide, enabling greater flexibility and applicability to the field of language translation within healthcare. Australia is currently one of the most culturally and linguistically diverse countries in the world, creating a growing pressure on translators to ensure there is equitable and culturally safe access to healthcare services. Emerging research supports the use of AI as an adjunct to the current translator framework in being able to support low-risk communication tasks, such as appointment coordination or simple instructions, but it should be avoided in high-stakes contexts including informed consent or complex management discussions. Importantly, human oversight is needed to ensure clinical accuracy and patient safety during all translations.

Abstract: Myopia and pathological myopia (PM) have been recognized as one of the leading causes of visual impairment globally. Optical coherence tomography (OCT) provides high-resolution imaging of retinal and choroidal structural changes and plays an increasing role in the diagnosis and prognostication of PM and myopia-related complications. Recent advances in OCT technology have produced a potential platform for artificial intelligence (AI), particularly deep learning (DL), to enhance diagnostic accuracy and prognostic capabilities. First, AI-assisted detection of myopia based on OCT-derived biomarkers such as retinal curvature, optic nerve morphology, and inner retinal thinning have the potential to detect high myopia. However, precise refractive error estimation or differentiation of lower-grade myopia remains modest. Future integration of OCT angiography may refine the prediction of myopia progression. Second, AI may improve automated segmentation and quantification of the choroid, with DL algorithms consistently delineating choroidal boundaries and quantifying region-specific choroidal thicknesses. Recent algorithms have extended beyond basic segmentation to choroidal sublayer segmentation and calculating choroidal vascularity indices, enhancing structural characterization in myopic eyes. Third, AI methods have advanced the detection of PM-related OCT lesions, reliably identifying critical lesions including myopic traction maculopathy, myopic choroidal neovascularization, and dome-shaped macula. Recent models have also shown the ability to categorize disease severity according to validated clinical frameworks, such as the Atrophy–Traction–Neovascularization and myopic tractional maculopathy staging systems. Despite these advances, current AI methods face challenges including inconsistent OCT protocols, limited longitudinal data, inadequate external validation, and difficulties handling poor-quality scans. Addressing these limitations could facilitate clinical integration, enhancing early diagnosis, prognostication, and possibly, personalized myopia management in the future.

Artificial Intelligence (AI) has become a game changer in the digital media sector, changing how content is created, enhanced, and distributed. Modern digital media platforms increasingly rely on AI-driven techniques to automate creative workflows, improve content quality, and personalize user experiences. This paper investigates the impact of AI on digital media content creation, focusing on AI-assisted text, image, video, and audio generation. A qualitative research approach based on existing literature and secondary data analysis is employed to examine the advantages, challenges, and ethical implications of AI-generated media. The study highlights how AI enhances productivity, reduces production costs, and democratizes creativity, while also raising concerns related to authenticity, intellectual property, and algorithmic bias. The findings emphasize the need for responsible AI adoption to ensure transparency, trust, and ethical compliance in digital media ecosystems.

Artificial intelligence (AI) permeates every aspect of peoples' life. Regretfully, there have already been multiple reports of AI systems discriminating against people. Academics have issued warnings about the possibility that AI would reinforce or even replicate current disparities. This research demonstrates how women's possibilities in emerging nations are influenced by AI. Women's status in developing nations, like India and South Asia, is that they are viewed as domestic workers and are underrepresented in positions of authority or responsibility. In the workplace, discrimination against and underemployment of women affects them disproportionately. This essay estimates how AI can worsen women's subpar status in developed nations. This article offers a superb analysis of how the rapid progress of technology in our society and business has exacerbated gender inequality and feminism in emerging nations.

This article explores the use of artificial intelligence in higher education external quality assessment. Specifically, the study demonstrates the feasibility and accuracy of predicting potential non-full compliance (defined as quality risk) in the quality assessment of higher education degree programmes. Using data from the Catalan University Quality Assurance Agency (Catalonia, Spain) covering the period 2015–2023, the study applies machine learning techniques to identify hidden patterns between 74 quality indicators and the accreditation outcomes of 1.416 degree programme evaluations. This research provides an innovative perspective to optimise and complement institutional assessment processes that are widely established at the international level.

The contemporary digital landscape is witnessing a profound paradigm shift as Artificial Intelligence (AI) moves from a peripheral technological tool to a core driver of organizational strategy. This study provides an extensive analysis of how AI-driven technologies are simultaneously revolutionizing Human Resource Management (HRM) and Marketing, two traditionally human-centric domains. In the realm of HRM, AI is fundamentally altering the "employee lifecycle" by automating and optimizing critical functions such as automated recruitment screening, data-driven performance management, real-time employee engagement monitoring, and predictive talent retention modeling. By leveraging machine learning and natural language processing, HR departments can transition from administrative roles to strategic partners, using data to anticipate workforce needs rather than merely reacting to them.

Artificial Intelligence (AI) is transforming nursing education by enhancing teaching and learning through personalized instruction, simulation-based training, and automated assessment. AI technologies support the development of clinical reasoning, critical thinking, and evidence-based practice among nursing students. They also assist educators in curriculum design, evaluation, and academic support. Despite its benefits, challenges such as ethical concerns, data privacy, and overreliance on technology must be addressed. Effective integration of AI requires proper training, validation, and adherence to educational and ethical standards. Overall, AI serves as a valuable tool to complement traditional teaching and prepare nursing students for modern healthcare environments.

Artificial intelligence (AI) is increasingly transforming intensive care medicine by enabling advanced analysis of complex clinical data generated in intensive care units (ICUs). This review explores current and emerging applications of AI in ICU practice, including sepsis prediction, mechanical ventilation management, acute kidney injury (AKI) forecasting, haemodynamic monitoring, and prognostication. AI-based models have demonstrated the ability to improve early detection of complications, support clinical decision-making, and optimise resource utilisation. However, challenges such as limited interpretability, data integration constraints, and the need for prospective validation continue to hinder widespread clinical adoption. A comprehensive narrative review was conducted using publications from January 2015 to June 2025. Combinations of the terms "artificial intelligence", "machine learning", "deep learning", "intensive care unit", "critical care", "clinical decision support", and "sepsis prediction" were used to search PubMed, Scopus, and Google Scholar. Peer-reviewed original research, systematic reviews, and meta-analyses reporting on the practical uses or clinical validation of AI tools in ICUs were given precedence, while studies focusing solely on algorithm development without clinical integration were excluded. Sepsis, mechanical ventilation, AKI, haemodynamic monitoring, and prognostication are among the thematic areas of application that organise the review. AI has shown significant utility across ICU domains, including early prediction of complications, forecasting mechanical ventilation duration, risk stratification, haemodynamic instability alerts, and mortality prognostication. Models trained on real-world ICU datasets have demonstrated high predictive accuracy and potential for early intervention. However, challenges such as model interpretability, data fragmentation, and ethical concerns remain.

The global human resources (HR) landscape is currently undergoing a foundational paradigm shift, driven by the rapid integration of Artificial Intelligence (AI) and Machine Learning (ML). This research paper explores the transformative role of AI in recruitment and talent acquisition, moving from administrative automation to predictive strategic intelligence. By analyzing the recruitment lifecycle—from sourcing and screening to candidate engagement and final selection—this study deconstructs how AI algorithms optimize efficiency, reduce time-to-hire, and potentially mitigate (or inadvertently amplify) human bias. Utilizing a Qualitative Systematic Literature Review (SLR) of academic studies and industry data from 2020–2026, the findings suggest that AI-driven recruitment can increase recruiter productivity by up to 40% and improve the quality of hire through data-driven matching. However, the study also highlights significant ethical challenges, including algorithmic transparency and data privacy. The research concludes that the most effective recruitment models of the future will be "Augmented Intelligence" systems, where AI handles high-volume processing while human recruiters focus on emotional intelligence, cultural fit, and strategic relationship building. Keywords: Artificial Intelligence, Recruitment, Talent Acquisition, Machine Learning, Algorithmic Bias, HR Technology, Candidate Experience, Predictive Analytics.

Introduction: The Fourth Industrial Revolution has greatly impacted working styles in organizations because of developments in areas like automation, robotics, sensors, data analysis, and artificial intelligence. HR processes are being digitized slowly in multinational companies and even in India’s topmost manufacturing industries, consulting firms, IT services, and IT-enabled services. The digitization process of e-HRM has made life easier by eliminating repetitive jobs, thus making HR activities more efficient. The purpose of this study is to examine how effective the e-HRM process has been for businesses and how AI has impacted HR activities like recruitment and training. Methods: This study is based on Descriptive and exploratory research methodology. The data were collected from 48 HR professionals from various Information Technology sectors through a purposive sampling method with a semi-structured questionnaire. Results: This study found that the role of artificial intelligence in screening and scrutinising resumes is appreciable, playing a significant role in analysing employee efficiency through emails and text messages for better people management. So artificial intelligence leads to effective e-HRM practices. Discussion: HR 4.0 has a lot of advantages for both HR departments and the organisation as a whole. These beneficial outcomes are particularly on attracting and retaining the best talent in the job market, continuous improvement in employee performance through training and expansion of strategic power in people management through technology.

AI-driven innovations for dental implant treatment planning: A systematic review.

The role of artificial intelligence in scientific research: a classification framework with case-based empirical insights

The role of artificial intelligence (AI) in day-to-day work has grown exponentially in recent years, and the drastic shift from the traditional mode of carrying out tasks and functions to embedding state-of-the-art technology into everything we do has become inevitable. The COVID-19 pandemic ignited the legal profession into rethinking, revamping and redesigning a traditionally formal system into one that was more contemporary, with the focus on innovation, evolution and creativity in modern times. The trend of evolution, creativity, and digitalisation has continued post-COVID, and the emergence of artificial intelligence in most sectors worldwide has seen computers impersonate human activities and change the way the world operates. The evolution of machine learning, computing power and the Internet through artificial intelligence, which is at the core of the fourth industrial revolution, has seen the need to continue revamping and rethinking the way we do things when operating in these changing times. AI, through technology, has seen knowledge machines move beyond analytical to predictive and prescriptive applications, shaping the new economy and marketplace. There is no doubt that AI is also transforming the legal profession in ways that one can hardly imagine. This note looks at the impact of AI on legal practice, highlighting the opportunities for greater efficiency, productivity and accuracy while also looking at some of the challenges posed by AI, such as privacy issues and ethical considerations. In addition, the note looks at how AI can be incorporated into the LLB curriculum with a strong focus on deep and lifelong learning for law students. The note concludes with a recommendation for the legal sector and all parties involved in advancing the legal profession to embrace AI while mitigating its potential risks.

Acute pancreatitis (AP) is a significant global health burden, with pancreatic fluid collections (PFCs) posing major diagnostic and therapeutic challenges. Distinguishing fluid-only pseudocysts from debris-containing walled-off necrosis is critical for management but is often difficult with conventional imaging. Contrast-enhanced computed tomography underestimates necrotic debris, while magnetic resonance imaging (MRI) is costly and time-consuming and endoscopic ultrasound is invasive. Artificial intelligence (AI), particularly deep learning and radiomics, is emerging as a powerful tool to overcome these limitations. AI algorithms can automate the segmentation of the pancreas and PFCs, provide objective quantification of necrotic debris and predict disease severity. Furthermore, AI-driven techniques can accelerate MRI acquisition times and potentially generate synthetic images, reducing scanner dependency. This review synthesizes AI's role in augmenting pancreatic imaging in PFC, covering its applications in segmentation and volumetry, image generation, outcome prediction and workflow optimization and discusses challenges and future directions for its clinical integration.

The Society for Craniofacial Genetics and Developmental Biology (SCGDB) held its 48th Annual Meeting at the University of Minnesota in Minneapolis on September 29-October 1, 2025. On the first day of the meeting, Drs. Timothy Cox and Jennifer Fish were honored with Excellence in Craniofacial Research Awards for their exceptional contributions to the field of craniofacial biology. The following 2 days of the meeting featured five sessions that highlighted new discoveries in human genetics, systems biology of craniofacial development and disease, evolutionary connections, signaling mechanisms, and a special session on clinical and patient perspectives. The meeting also featured workshops on scientific writing and the role of artificial intelligence in advancing research and care. A poster session facilitated dynamic and insightful interactions among the 113 attendees, who represented diverse career stages and research backgrounds in developmental biology and genetics, further strengthening the SCGDB community.

Artificial intelligence (AI) is increasingly applied in pediatric dentistry for caries detection, risk prediction, anomaly identification, and treatment planning. However, the quality, consistency, and overlap of evidence from existing systematic reviews have not been comprehensively evaluated. To synthesize evidence from systematic reviews on AI in pediatric dentistry, focusing on diagnostic performance, methodological quality, and overlap of primary studies. PubMed/MEDLINE and the Cochrane Database of Systematic Reviews were searched up to 31 August 2025. Methodological quality was assessed using AMSTAR-2, and overlap was quantified using the corrected covered area (CCA) with the GROOVE tool. Seven systematic reviews (109 primary studies) were included-four on early childhood caries, one each on dental anomalies, cleft lip and palate, and caries risk prediction. AI, particularly convolutional neural networks for imaging, achieved pooled sensitivities and specificities of 80%-83% and Area Under the Curve (AUCs)of 0.87-0.91. Most reviews were of low or critically low quality due to lack of protocol registration and limited bias assessment. GROOVE analysis indicated moderate overlap (CCA = 8.27%). AI demonstrates promising diagnostic performance in pediatric dentistry, particularly for image-based tasks, but current evidence remains preliminary and should be interpreted with caution. PROSPERO registration number: CRD420251142904.