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196 articles for “artificial intelligence in healthcare”
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Implementation of Artificial Intelligence Technology in Tele Medicine
Abstract: The practice of medicine across locations by the prudent application of information and communication technology is known as telemedicine. As a result of the COVID-19 epidemic, telemedicine has experienced exponential growth on a global scale. Artificial intelligence has the potential to enhance and broaden the scope of telemedicine, opening up countless opportunities for creating solutions for particular medical need. AI in telemedicine has the potential to significantly aid in the …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 15, Issue 3, 2025 · pp. 41–50 Read article
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AI-Enabled Recycling of Thermoplastic Polymer Waste in Hospitals: A Circular Economy Pathway Toward Green Hospital Certification
Abstract: This review research explores the latest role of AI in improving thermoplastic waste management for hospitals in terms of segregation accuracy, operational efficiency, and circular economy outcomes. Seventy-five relevant studies were analysed, and it was reported that AI-based systems, especially CNNs, YOLO models, and sensor-fusion approaches, achieved high accuracy in the identification and sorting of medical plastics, often above 90%. Early evidence also reveals improvements in the reduction of contaminants, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 170–182 Read article
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A Study to Assess the Knowledge and Attitude on Artificial Intelligence in Health Care Among Nursing Students at a Selected Nursing College in Kuppam
Abstract: Background: Artificial Intelligence (AI) is rapidly gaining prominence in healthcare, holding the potential to revolutionize patient care and streamline administrative processes. However, the preparedness of future nursing professionals for this technological shift, particularly their knowledge and attitudes towards AI in healthcare, remains a critical yet under-investigated area. This study aimed to assess the knowledge and attitudes of nursing students towards AI in healthcare at a selected nursing college in Kuppam, …
Published in Journal of Nursing Science & Practice · Vol. 16, Issue 1, 2026 · pp. 17–23 Read article
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AI Activity to Enhance Productivity, Creativity, and Sustainable Development of AI in Healthcare
Abstract: The application of artificial intelligence (AI) in healthcare is revolutionizing the industry by fostering both productivity and creative approaches to patient care and medical research. This study explores how artificial intelligence (AI) is reshaping healthcare by streamlining workflows, improving diagnostic precision, and supporting personalized treatment plans. AI-powered technologies are reshaping the way medical professionals approach patient care, providing tools that not only automate routine administrative and clinical tasks but also …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 21–29 Read article
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Symmetry Principles in Digital Twin Systems: Modeling, Integration, and Applications
Abstract: This systematic review comprehensively examines the burgeoning field of Digital Twin (DT) technology, analyzing its current state, diverse applications, and future trajectory. Through a rigorous methodology involving a systematic search of academic databases and relevant industry literature, this review synthesizes findings across a spectrum of disciplines. We identify the core components and underlying principles of DTs, distinguishing them from traditional simulation models by their dynamic, realtime data integration and bi-directional …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 06–24 Read article
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A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems
Abstract: Large amounts of diverse medical data have been produced because of the quick development of digital healthcare systems, offering substantial chances to use machine learning methods for clinical decision support and illness prediction. By identifying intricate patterns in clinical data, machine learning-based models have shown great promise in early disease detection, risk assessment, and personalised healthcare. However, issues with transparency, interpretability, and reliability have been brought up by the growing …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 20–28 Read article
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Transforming the Landscape of Advanced Dentistry: Incorporating Intelligence Augmentation (IA) and Artificial Intelligence (AI) to Propel Progress-A Narrative Review.
Abstract: Digitization is about to transform most sectors, and it is important to comprehend its role and socio-economic consequences. One of the infected sectors is dentistry and this article genericizes AI and IA in dentistry. It analyzes the advancement of AI and IA to supersede human capabilities using a comparative analysis of these technologies with current deployment. One of the challenges is to attain sufficient data input for AI and the …
Published in Research and Reviews: A Journal of Dentistry · Vol. 16, Issue 2, 2025 · pp. 23–29 Read article
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A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments
Abstract: The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial …
Published in Emerging Trends in Metabolites · Vol. 3, Issue 2, 2026 Read article
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Artificial intelligence’s role in mental health: Innovations, Challenges, and future prospects
Abstract: Among the many ways in which mental health services are gaining from the integration of artificial intelligence (AI) are improvements in diagnosis, tailored treatment programs, and round the- clock patient help. Two AI-driven solutions are virtual therapists and prediction algorithms, which could increase access to mental health therapy and enable early intervention. However, the application of artificial intelligence in this field raises ethical concerns about privacy, discrimination, and the potential …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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Securing Healthcare 5.0: A Review of Applications, Security Challenges, and Future Perspectives
Abstract: Healthcare 5.0 represents a revolutionary shift in the healthcare sector, focusing on individualized, patient-centric care through the integration of different cutting-edge technologies such as internet of things, artificial intelligence, big data analytics, blockchain, and cloud computing to enable healthcare services such as real-time health monitoring, personalized treatments, and access to expert advice regardless of geographic barriers. Potentially, Healthcare 5.0 supports more engaged and effective care and help through a focus …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 2, 2025 · pp. 22–36 Read article
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Interpretable Skin Cancer Detection via Optimized CNN Models for Smart Healthcare Solutions
Abstract: Skin cancer is a common and potentially life-threatening condition, highlighting the importance of reliable and efficient diagnostic techniques. Recently, convolutional neural networks (CNNs) have demonstrated significant potential in automating the classification of skin cancer using thermoscopic images. Despite these advancements, the lack of interpretability in these models poses a barrier to their widespread use in clinical settings. In this study, we propose an interpretable CNN architecture optimized for skin cancer …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 41–45 Read article
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A Review on Lung Cancer Prediction Using Machine Learning
Abstract: Lung cancer continues to be a major contributor to cancer-related mortality across the globe. Timely diagnosis and reliable prediction models play a crucial role in enhancing treatment outcomes and survival rates for patients. The present study focuses on the utilization of machine learning (ML) methods for the prediction of lung cancer. Using datasets that incorporate clinical records, imaging modalities, and genetic profiles, the research assesses the predictive capabilities of multiple …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–11 Read article
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Sustainable Advances in Oral Pathology: The Impact of Artificial Intelligence
Abstract: Oral cavity cancers constitute about 4% of all malignant neoplasms. More than 9,900 new cases are reported from the developed countries yearly, and the 5-year mortality rate is higher than 39%. In Europe, the highest rates occur in Hungary, Croatia, and other Central and Eastern European countries; high levels are also seen in India, Pakistan, and Bangladesh. The management of endosseous pathologies presents considerable challenges because they often exhibit no …
Published in Research and Reviews: A Journal of Dentistry · Vol. 16, Issue 1, 2025 · pp. 24–27 Read article
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Comparative Analysis and Future Research Directions in AI in Healthcare: Medical Imaging and Diagnostics
Abstract: Artificial intelligence (AI) is reshaping healthcare, particularly in the areas of medical imaging and diagnostic practice. By using advanced techniques like machine learning and deep learning, AI systems help improve the accuracy, speed, and effectiveness of identifying diseases and analyzing medical images. This paper provides a comprehensive overview of the application of artificial intelligence in medical imaging and highlights its growing importance in clinical diagnostics. It discusses how AI-based systems …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 8–13 Read article
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MediSense AI - Smart Health Analysis System
Abstract: MediSense AI is a revolutionary health analysis system that empowers users by transforming complex medical data into understandable insights. This platform utilizes advanced technologies, particularly natural language processing and machine learning, to make intricate medical terminologies accessible to individuals without a healthcare background. Leveraging Llama 3, a cutting-edge AI model developed by Meta AI, the system can analyze various forms of medical data, including the ability for users to upload …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 20–30 Read article
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A Review of Recent Advancements in Machine Learning and Deep Learning Approaches for Pet Diseases Prediction
Abstract: This systematic study assesses recent developments in Machine Learning (ML) and Deep Learning (DL) approaches to predict pet diseases. With the increasing role of Artificial Intelligence (AI) in pet healthcare, this study identifies recent research trends, limitations, and future directions. A comprehensive search was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines in selecting 20 relevant studies from over 300 articles published between 2020 and …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 14, Issue 3, 2025 · pp. 1–6 Read article
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Role of Artificial Intelligence in Health Care Decision Making: Balancing Innovation and Caution
Abstract: Healthcare is undergoing a transformation powered by artificial intelligence, which improves monitoring, diagnosis, and treatment capabilities. Among Artificial Intelligence (AI's) shortcomings is the dearth of an emotional relationship between individuals and medical personnel. Robotic surgery procedures pose the possibility of malfunctioning machinery and mistaken assumptions. So, the present systematic review focused on exploring the boon and bane of the role of AI in predicting various abnormalities in advance to improve …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 24–35 Read article
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article
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Forecasting Climate-Driven Healthcare Demand in Agricultural Regions: A Multi-Modal AI Approach
Abstract: The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 28–38 Read article
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Pharmacovigilance: The Silent Guardian of Patient Safety
Abstract: Pharmacovigilance is critical in protecting the populace health by monitoring, detecting and preventing adverse drug reaction (ADR) post drug approval. Although there is technological advancement and national initiatives such as the Pharmacovigilance Programme of India (PvPI), there is still a great problem of underreporting. Many benefits of effective pharmacovigilance include identification of risks early, better prescriptions and patient confidence. There are historical examples of drugs being withdrawn due to thalidomide, …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 1, 2026 · pp. 01–05 Read article