Search Results (491)

Echocardiography is a first-line, non-invasive imaging modality widely used to assess cardiac structure and function; however, its interpretation remains highly operator dependent and subject to variability. The integration of artificial intelligence (AI) into echocardiographic practice holds the potential to transform workflows, enhance efficiency, and improve the consistency of assessments across diverse clinical settings. Interest in the application of AI to echocardiography has grown significantly since the early 2000s with AI models that assist with image acquisition, disease detection, measurement automation, and prognostic stratification for various cardiac conditions. Despite this momentum, the safe and effective deployment of AI models relies on rigorous development and validation practices, yet these are infrequently described in the literature. This narrative review aims to provide a comprehensive overview of the essential steps in the development and validation of AI models for echocardiography. Additionally, it explores current challenges and outlines future directions for the integration of AI within echocardiography. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

Background/Objectives: Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Accurate surveillance and timely delivery are critical to improving outcomes. This narrative review examines the role of computerized cardiotocography (cCTG) and short-term variation (STV) interpretation in the monitoring of FGR and its integration with Doppler velocimetry and the biophysical profile (BPP). Methods: A comprehensive literature search of PubMed, Scopus, and Web of Science was performed for studies published up to 2021 using combinations of terms related to FGR, CTG, STV, and Doppler surveillance. Eligible sources included original studies, systematic reviews, and international guidelines. Case reports, intrapartum-only monitoring, and studies involving major anomalies were excluded. Results: Reduced STV consistently correlates with fetal compromise, abnormal Doppler findings, and adverse perinatal outcomes. In early-onset FGR (<32 weeks), ductus venosus abnormalities often coincide with or precede STV reduction; combined use supports optimal timing of delivery. In late-onset FGR (≥32 weeks), STV changes are less pronounced and require integration with cerebroplacental ratio, variability indices, and trend-based interpretation. Longitudinal evaluation offers greater prognostic value than isolated measurements. However, heterogeneity in thresholds, fragmented outcome data, and system-specific definitions limit standardization and comparability across studies. Conclusions: cCTG provides an objective and adjunct to Doppler and BPP in the surveillance of FGR, a tool for obstetrician needs. Its greatest utility lies in serial, integrated assessment, supported by gestational age-specific reference ranges. Future advances should include standardized STV thresholds, large outcome-linked databases, and artificial intelligence-driven tools to refine decision-making and optimize delivery timing. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

The rise of social media platforms has fundamentally reshaped the global information ecosystem, fostering the spread of disinformation. Beyond the circulation of false content, this article frames disinformation as an aesthetic crisis of public communication: an algorithmic reorganization of sensory experience that privileges performative virality over shared intelligibility, fragmenting public discourse and undermining democratic deliberation. Drawing on John Dewey’s philosophy of aesthetic experience and critical theory (Adorno, Benjamin, Fuchs, Han), we argue that journalism, understood as a form of public art rather than mere fact-transmission, can counteract this crisis by cultivating critical attention, narrative depth, and democratic engagement. We introduce the concept of aesthetic literacy as an extension of media literacy, equipping citizens to discern between seductive but superficial forms and genuinely transformative experiences. Empirical examples from Portugal (Expresso, Público, Mensagem de Lisboa) illustrate how multimodal journalism—through paced narratives, interactivity, and community dialogue—can reconstruct Deweyan “integrated experience” and resist algorithmic disinformation. We propose three axes of intervention: (1) public education oriented to aesthetic sensibility; (2) journalistic practices prioritizing ambiguity and depth; and (3) algorithmic transparency. Defending journalism as a public art of experience is thus crucial for democratic regeneration in the era of sensory capitalism, offering a framework to address the structural inequalities embedded in global information flows. Full article

Patient-specific technologies within the field of adult spinal deformity (ASD) aid surgeons in pre-surgical planning, accurately help identify anatomical landmarks, and can project optimal post-surgical sagittal alignment. This narrative review aims to discuss the current uses of patient-specific technologies in ASD and identify new innovations that may very soon be integrated into patient care. Pre-operatively, machine learning or artificial intelligence helps surgeons to simulate post-operative alignment and provide information for the 3D-printing of pre-contoured rods and patient-specific cages. Intraoperatively, robotic surgery and intraoperative guides allow for more accurate positioning of implants. Implant materials are being developed to allow for better osseointegration and patient outcome monitoring. Despite the significant promise of these technologies, work still needs to be performed to ensure their accuracy, safety, and cost efficacy. Full article

The rapid growth of AI-enabled medical wearables and home-based diagnostic devices has opened new pathways for preventive care, chronic disease management and user-driven health insights. Despite significant technological progress, many solutions face adoption hurdles, often due to usability challenges, episodic measurements and poor alignment with daily life. This review surveys the current landscape of at-home healthcare technologies, including wearable vital sign monitors, digital diagnostics and body composition assessment tools. We synthesize insights from the existing literature for this narrative review, highlighting strengths and limitations in sensing accuracy, user experience and integration into daily health routines. Special attention is given to the role of AI in enabling real-time insights, adaptive feedback and predictive monitoring across these devices. To examine persistent adoption challenges from a user-centered perspective, we reflect on the Pi-CON methodology, a conceptual framework previously introduced to stimulate discussion around passive, non-contact, and continuous data acquisition. While Pi-CON is highlighted as a representative methodology, recent external studies in multimodal sensing, RFID-based monitoring, and wearable–ambient integration confirm the broader feasibility of unobtrusive, passive, and continuous health monitoring in real-world environments. We conclude with strategic recommendations to guide the development of more accessible, intelligent and user-aligned smart healthcare solutions. Full article

Differentiated thyroid carcinoma (DTC) is the most common endocrine malignancy and typically has a favorable prognosis. However, a subset of patients experience aggressive disease, recurrence, or treatment resistance, underscoring the need for more precise diagnostic and therapeutic strategies. Advances in molecular profiling have improved the management of thyroid cancer by enabling risk-adapted treatment and targeted interventions. This narrative review offers a clinically focused synthesis of the current role of molecular diagnostics and personalized therapeutics in DTC. We examine key genetic alterations and their diagnostic, prognostic, and therapeutic implications, and discuss how molecular markers enhance traditional risk stratification systems, informing surgical decisions, radioactive iodine (RAI) use, and surveillance. The growing role of targeted therapies, such as tyrosine kinase inhibitors and agents against specific oncogenic drivers, is reviewed, particularly for RAI-refractory DTC. We also address real-world challenges in implementing precision medicine, including access, cost, and standardization. Future directions, such as liquid biopsy, artificial intelligence, and multi-omic integration, are explored as tools to achieve fully personalized care. This review aims to bridge the gap between molecular discovery and clinical application, offering practical insights for endocrinologists, surgeons, oncologists, and multidisciplinary teams managing DTC. Full article

Background: Over the years, surgical techniques have evolved, resulting in an abundance of available procedures in the armamentarium of metabolic and bariatric surgeons, and the technology has also advanced in a similar way. Significant steps have been made in stapling technology especially, introducing artificial intelligence (AI) in optimizing this technology for better treatment outcomes. The introduction of AI in stapling technology showed a decrease in potential stapling complications not only in MBS, but also in other (surgical) specialties. Areas Covered: This review will cover the general principles of stapling in surgery, but with an emphasis on both the technical and anatomical considerations. We will also discuss the mechanisms of staplers and potential safety hazards. Finally, we will focus on how AI is integrated in stapling technology, potential pros and cons, and areas for future development of stapling technology and the integration of AI. Conclusions: In metabolic and bariatric surgery, stapling is a technical procedure that requires a comprehensive understanding of the anatomical and physiological characteristics of the target tissue. Variability in tissue thickness, vascularity, elasticity, and mechanical load, compounded by patient-specific factors and intraoperative dynamics, demands constant vigilance and adaptability from the surgeon. The integration of AI and digital technologies offers potential improvements in refining this process. By providing real-time feedback on tissue properties and supporting intraoperative decision-making, these tools can assist surgeons in optimizing staple-line integrity and minimizing complications. The ongoing combination of surgical expertise with intelligent technology may contribute to advancing precision stapling in metabolic and bariatric surgery. Full article

Human storytellers often orchestrate diverse narrative orders (chronological, flashback) for crafting compelling stories. To equip artificial intelligence systems with such capability, existing methods rely on implicitly learning narrative sequential knowledge, or explicitly modeling narrative order through pairwise event temporal order (e.g., take medicine <after> get ill). However, both suffer from imbalanced narrative order distribution bias and inadequate event temporal understanding, hindering generating high-quality events in the story that balance the logic and narrative order. In this paper, we propose a narrative-order-aware framework, TimeWeaver, which presents an event–order bidirectional pretrained model integrated with temporal mixture-of-experts to orchestrate diverse narrative orders. Specifically, to mitigate imbalanced distribution bias, the temporal mixture-of-experts is devised to route events with various narrative orders to corresponding experts, grasping distinct orders of narrative generation. Then, to enhance event temporal understanding, an event sequence narrative-order-aware model is pretrained with bidirectional reasoning between event and order, encoding the event temporal orders and event correlations. At the fine-tuning stage, reinforcement learning with multi-granular optimal transport reward is designed to boost the quality of generated events. Extensive experimental results on automatic and manual evaluations demonstrate the superiority of our framework in orchestrating diverse narrative orders during story generation. Full article

The digital transformation of agricultural product supply chains has emerged as a strategic direction that cannot be overlooked in the global modernization of agriculture. This paper adopts a narrative review framework based on the “Technology–Collaboration–Sustainability” perspective in the digital transformation of agricultural product supply chains, summarizing the drivers of digital transformation, the application of digital technologies, multi-stakeholder collaborative mechanisms, and pathways for sustainable development within these supply chains. The study finds that the core drivers promoting the digital transformation of agricultural product supply chains include external environmental factors (such as population growth, dietary shifts, and food waste) and internal demand drivers (such as industrial upgrading and increased corporate competition). The application of digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) has significantly improved the efficiency, transparency, and resilience of the supply chains. Furthermore, various models of multi-stakeholder collaborative mechanisms have optimized resource allocation and enhanced supply chain stability. Finally, the paper proposes a pathway for the sustainable development of agricultural product supply chains based on digital transformation, providing directions for future research and practice. Full article

Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies evaluating AI-based echocardiography, cardiac CT, or cardiac MRI in LMICs. Articles were screened according to PRISMA guidelines, and data on diagnostic outcomes, challenges, and enabling factors were extracted and narratively synthesized. Results: Twelve studies met the inclusion criteria. AI-driven methods frequently surpassed 90% accuracy in detecting coronary artery disease, rheumatic heart disease, and left ventricular hypertrophy, often enabling task shifting to non-expert operators. Challenges included limited dataset diversity, operator dependence, infrastructure constraints, and ethical considerations. Insights from high-income countries, such as automated segmentation and accelerated imaging, suggest potential for broader AI integration in cardiac MRI and CT. Conclusions: AI holds promise for enhancing cardiovascular care in LMICs by improving diagnostic accuracy and workforce efficiency. However, multi-center data sharing, targeted training, reliable infrastructure, and robust governance are essential for sustainable adoption. This review underscores AI’s capacity to bridge resource gaps in LMICs, offering practical pathways for future research, clinical practice, and policy development in global cardiovascular imaging. Full article

Despite rapid proliferation of AI applications in prostate MRI showing impressive technical performance, clinical adoption remains limited. We conducted a comprehensive narrative review of literature from January 2018 to December 2024, examining AI applications in prostate MRI with emphasis on real-world performance and implementation challenges. Among 200+ studies reviewed, AI systems achieve 87% sensitivity and 72% specificity for cancer detection in research settings. However, external validation reveals average performance drops of 12%, with some implementations showing degradation up to 31%. Only 31% of studies follow reporting guidelines, 11% share code, and 4% provide model weights. Seven real-world implementation studies demonstrate integration times of 3–14 months, with one major center terminating deployment due to unacceptable false positive rates. The translation gap between artificial and clinical intelligence remains substantial. Success requires shifting focus from accuracy metrics to patient outcomes, establishing transparent reporting standards, developing realistic economic models, and creating appropriate regulatory frameworks. The field must combine methodological rigor, clinical relevance, and implementation science to realize AI’s transformative potential in prostate cancer care. Full article

Non-player characters (NPCs) play a crucial role in creating engaging and immersive experiences in role playing games (RPGs). Traditional NPC interactions often rely on scripted dialogues, which can limit their ability to adapt dynamically to player input. This study presents a novel framework that enhances NPC interactions by integrating advanced conversational systems. Utilizing Open AI’s natural language processing capabilities, RPG Maker MZ as the game development platform, and JavaScript for customization, the framework introduces context-aware dialogues that respond intelligently to player queries and actions. By bridging the gap between game AI and conversational systems, this approach enables more lifelike and meaningful NPC behavior. Experimental results indicate that the proposed system significantly improves the narrative depth and overall player experience. These findings demonstrate the potential of combining AI-driven chatbots with game development tools to redefine the role of NPCs in modern gaming. Full article

The rapid advancements in computer processing, algorithmic development, and the availability of large-scale datasets have positioned Artificial Intelligence (AI) as a valuable tool across multiple domains, including Medicine. In the field of Nuclear Medicine neuroimaging, with Positron Emission Tomography (PET), AI has demonstrated significant potential in improving diagnostic accuracy for neurodegenerative cognitive disorders. This is especially relevant for the early diagnosis, preclinical detection, and prediction of disease progression in Alzheimer’s disease (AD), the most prevalent form of cognitive impairment in individuals over 65 years of age. This narrative review aims to synthesize current advances, explore future directions, and highlight outstanding challenges in the application of Artificial Intelligence to PET imaging for the clinical management of Alzheimer’s disease, with particular focus on three key modalities: ¹⁸F-FDG PET, Amyloid PET, and Tau PET. Full article