Frontiers in Artificial Intelligence | New and Recent Articles

Early and accurate detection of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans is crucial for clinical intervention. A novel S3Net, a Synthesis–Segmentation–Spiking Network, is proposed for this purpose. It integrates synthetic MRI generation, pathology-aware segmentation, and spike-based classification. The Synthesis Network uses a generative adversarial network framework. …

IntroductionTo assess and compare the accuracy, readability, and overall performance of large language models (LLMs) in answering questions about functional hypothalamic amenorrhea (FHA) for patients and healthcare professionals.MethodsA total of 11 patient-level and 15 clinician-level FHA-related questions were entered separately into four LLMs: ChatGPT 3.5 (free version), ChatGPT 4.0 (updated, …

IntroductionAlzheimer’s disease (AD) is responsible for many dementia-related deaths worldwide, and Mild Cognitive Impairment (MCI) is often its earliest clinical manifestation. Early detection of MCI is essential to initiate timely interventions that can slow progression and improve the patient’s quality of life. The emergence of diffusion tensor imaging (DTI) has made it a powerful neuroimaging…

The rapid evolution of artificial intelligence in financial technologies has fundamentally reshaped risk management within complex socio-economic systems. However understanding how users build transactional trust within high-density mobile economies remains a critical challenge. This study integrates technology fit and reassurance confirmation theories to investigate user security perceptions in …

Neural modeling and large language models (LLMs) has led to a significant improvement in the quality of machine translation (MT) output. While MT increasingly rivals human output, “translationese”—systematic linguistic fingerprints left by the translation process—has long been studied qualitatively, yet its quantitative boundaries remain unclear. We present an interpretable machine-learning frame…

BackgroundAutomated analysis of Pap-smear images plays an important role in cervical cancer screening, particularly in low-resource settings where manual cytology remains labour-intensive, subjective, and prone to inter-observer variability. On the other hand, accurate segmentation of the nucleus and cytoplasm is a fundamental step in computer-aided diagnosis systems because it enables quantitati…

Artificial intelligence (AI) has achieved extraordinary progress in recent years, yet this progress reveals a deep educational and epistemic imbalance. Neural architectures have mastered prediction but often obscure the grounds of their outputs. This Perspective argues that knowledge graphs (KGs) are more than a technical advance: they are an intellectual bridge between symbolic and neural paradi…

IntroductionTumor heterogeneity poses a significant challenge for predicting responses to cancer therapy, highlighting the need for the development of biomarkers to guide personalized treatment. Contrast-enhanced ultrasound (CEUS) imaging is an established method to assess tumor perfusion, which directly affects drug delivery and therapeutic efficacy, as poorly perfused tumors often limit the pen…

BackgroundArtificial intelligence (AI) has increasingly been proposed to strengthen infection surveillance, early risk stratification, antimicrobial decision support, and selected workflow functions in acute-care hospitals. However, the literature remains clinically heterogeneous, methodologically uneven, and conceptually fragmented, with technical performance often interprested too readily as ev…

Medical image analysis faces persistent challenges due to the distributed data, limited annotations, and variations in imaging modalities, acquisition protocols, and patient demographics. Centralized deep learning approaches compromise data privacy, while Federated Learning (FL) enables decentralized model training without sharing raw data. However, conventional FL frameworks struggle with non-II…

Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate this new paradigm. Our paper addresses this gap by providing a comprehensive revie…

IntroductionAutomated classification of skin lesion remains a challenging task due to high inter-class similarity, intra-class variance and extreme class imbalance in the dermoscopic datasets. To overcome these limitations, the proposed study presents a Hybrid Bi-Feature Network model as HB-Net, in which the convolutional neural networks are fine-tuned to extract features and subsequently machine…

The growing demand of smart surveillance systems necessitates the accurate and real-time detection of weapons and face recognition with robustness against occlusion, illumination changes, and complex backgrounds. Existing techniques based on standalone CNN or transformer architectures are less effective in capturing local fine-grained features as well as long-range dependencies. This paper presen…

IntroductionThe MADCOW annotation system enables group-based annotation, allowing users to direct annotations toward communities focused on specific domain topics. In smart city environments, such groups may include citizens, urban planners, and domain experts collaborating on urban services, infrastructure, mobility, environment, and public safety. Existing recommendation approaches mainly rely …

Logistic and logit functions play important roles in modern science, serving as foundational tools in various applications, such as artificial neural networks (ANN). While there are functions that could produce distinct logistic and logit curves, no single, unified framework has been developed to generate both logistic and logit curves. We introduce a Cannistraci–Muscoloni–Gu generalized logistic…

Obesity is a multifactorial chronic disease whose worldwide prevalence in adults has more than doubled since 1990, demanding a shift from reactive treatment towards early, personalised prevention. Artificial intelligence (AI) provides a methodological pathway for this shift by integrating heterogeneous, longitudinal evidence—genomic, metabolomic, electronic health record (EHR), wearable Internet-…

BackgroundPathway enrichment analyses are widely used to interpret transcriptomic datasets; however, their outputs typically consist of lists of statistically enriched pathways that require qualitative interpretation and are difficult to compare across biological contexts. Methods of semantic classification that transform enrichment results into quantitative, mechanistically interpretable measure…

Background and objectivesAccurate medical image segmentation remains a challenging task in computer-aided diagnosis because of the intricacies and the variability in the biomedical data in terms of the anatomical complexity, inter-patient diversity, class imbalance, and irregular morphological patterns.MethodsIn the present work, a Context Aware Adaptive Progressive Network (CA2PNet) is proposed.…

Workplace stress is a significant concern, as it negatively impacts employee wellbeing and organizational productivity and is a major contributor to burnout and job turnover. Detecting stress in real-world work environments remains challenging; however, recent advances in machine learning and deep learning techniques offer promising solutions. Furthermore, the growing availability of multimodal d…

Artificial intelligence plays a central role in modern cybersecurity, yet systems optimized for detection accuracy often lack mechanisms for accountability, transparency, and policy compliance. This study proposes the Agentic AI Framework (AAIF), a policy-aware intrusion detection architecture that integrates predictive modeling with executable governance. Guided by Design Science Research, the f…