Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems DOI Creative Commons
Arturs Kempelis, Inese Poļaka, Andrejs Romānovs

и другие.

Future Internet, Год журнала: 2024, Номер 16(2), С. 44 - 44

Опубликована: Янв. 28, 2024

Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important food production. This paper explores application convolutional neural networks (CNNs) to forecast key sensor measurements from thermal images within this context. research focuses on using relative air humidity, soil moisture, and light intensity, are integral plant health productivity urban farming environments. The results indicate a higher accuracy forecasting humidity moisture levels, with Mean Absolute Percentage Errors (MAPEs) range 10–12%. These findings correlate strong dependency these parameters patterns, effectively extracted by CNNs. In contrast, intensity proved be more challenging, yielding lower accuracy. reduced performance likely due complex variable factors that affect insights gained predictive for may inform targeted interventions practices, while highlights need further into integration additional data sources or hybrid modeling approaches. conclusion suggests technologies can significantly enhance maintenance health, leading sustainable efficient practices. However, study also acknowledges challenges implementing agricultural models.

Язык: Английский

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A REVIEW OF ETHICAL DILEMMAS AND PRACTICAL APPLICATIONS DOI Creative Commons

Evangel Chinyere Anyanwu,

Chiamaka Chinaemelum Okongwu,

Tolulope O Olorunsogo

и другие.

International Medical Science Research Journal, Год журнала: 2024, Номер 4(2), С. 126 - 140

Опубликована: Фев. 2, 2024

The fusion of Artificial Intelligence (AI) and healthcare heralds a new era innovation transformation, yet it is not without its ethical quandaries. This comprehensive review traverses the intricate landscape where AI meets healthcare, delving into dilemmas that arise alongside practical applications. considerations span spectrum, encompassing issues patient privacy, transparency, accountability, inadvertent perpetuation biases within algorithms. Privacy concerns emerge as central dilemma providers leverage to process vast amounts data. Striking delicate balance between harnessing power for diagnostic predictive purposes safeguarding sensitive medical information critical challenge. Moreover, scrutinizes implications algorithms their potential perpetuate biases, inadvertently exacerbating health disparities. A nuanced examination bias mitigation strategies becomes imperative ensure technologies contribute equitable outcomes. In tandem with considerations, illuminates applications reshaping landscape. AI-driven diagnostics, modeling, personalized treatment plans transformative tools, enhancing clinical decision-making efficient allocation resources, streamlined workflows, acceleration drug discovery processes showcase tangible benefits integration. aspires guide practitioners, policymakers, technologists in navigating crossroads healthcare. By fostering an awareness pitfalls emphasizing responsible development, stakeholders can collaboratively shape future augments delivery, upholds standards, ultimately improves quality care. Keywords: AI, Healthcare, Ethics, Review, Application.

Язык: Английский

Процитировано

28
Edge AI: A Taxonomy, Systematic Review and Future Directions DOI
Sukhpal Singh Gill, Muhammed Golec,

Jianmin Hu

и другие.

Cluster Computing, Год журнала: 2024, Номер 28(1)

Опубликована: Окт. 18, 2024

Язык: Английский

Процитировано

19
Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency DOI Creative Commons
Soumitra S. Bhuyan,

Vidyoth Sateesh,

Naya Mukul

и другие.

Journal of Medical Systems, Год журнала: 2025, Номер 49(1)

Опубликована: Янв. 16, 2025

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train professionals, and advance medical research. This paper examines various clinical non-clinical applications of Gen AI. In settings, AI supports the creation customized plans, generation synthetic data, analysis images, nursing workflow management, risk prediction, pandemic preparedness, population health management. By automating administrative tasks such as documentations, reduce clinician burnout, freeing more time for direct care. Furthermore, application may surgical outcomes by providing real-time feedback automation certain operating rooms. The data opens new avenues model training diseases simulation, enhancing research capabilities improving predictive accuracy. contexts, improves education, public relations, revenue cycle marketing etc. Its capacity continuous learning adaptation enables it drive ongoing improvements operational efficiencies, making delivery proactive, predictive, precise.

Язык: Английский

Процитировано

7
Comparative review of big data analytics and GIS in healthcare decision-making DOI Creative Commons
Odunayo Josephine Akindote,

Abimbola Oluwatoyin Adegbite,

Samuel Onimisi Dawodu

и другие.

World Journal of Advanced Research and Reviews, Год журнала: 2023, Номер 20(3), С. 1293 - 1302

Опубликована: Дек. 22, 2023

This research explores the confluence of big data analytics and Geographic information systems (GIS) in healthcare decision-making. The comparative review delineates unique strengths each technology, showcasing potential synergies. Big harnesses advanced for predictive modeling clinical decision support, while GIS introduces a spatial context health analysis. Future trends suggest integrations with artificial intelligence, real-time analytics, wearable technology. However, challenges encompass privacy, biases, interdisciplinary collaboration. Ethical considerations emphasize transparency, informed consent, responsible use patient data. As these technologies evolve, their seamless integration holds promise precision health, community-oriented interventions, proactive pandemic response, reshaping landscape

Язык: Английский

Процитировано

44
Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach DOI Creative Commons
Vito Santamato, Caterina Tricase, Nicola Faccilongo

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(22), С. 10144 - 10144

Опубликована: Ноя. 6, 2024

The integration of artificial intelligence (AI) in healthcare management marks a significant advance technological innovation, promising transformative effects on processes, patient care, and the efficacy emergency responses. scientific novelty study lies its integrated approach, combining systematic review predictive algorithms to provide comprehensive understanding AI’s role improving across different contexts. Covering period between 2019 2023, which includes global challenges posed by COVID-19 pandemic, this research investigates operational, strategic, response implications AI adoption sector. It further examines how impact varies temporal geographical addresses two main objectives: explore influences domains, identify variations based Utilizing an we compared various prediction algorithms, including logistic regression, interpreted results through SHAP (SHapley Additive exPlanations) analysis. findings reveal five key thematic areas: enhancing quality assurance, resource management, security, pandemic. highlights positive influence operational efficiency strategic decision making, while also identifying related data privacy, ethical considerations, need for ongoing integration. These insights opportunities targeted interventions optimize current future landscapes. In conclusion, work contributes deeper provides policymakers, professionals, researchers, offering roadmap addressing both

Язык: Английский

Процитировано

17
Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research DOI Creative Commons
Chieh-Chen Wu, Md. Mohaimenul Islam, Tahmina Nasrin Poly

и другие.

Diagnostics, Год журнала: 2024, Номер 14(4), С. 397 - 397

Опубликована: Фев. 12, 2024

Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number research articles evaluating its applications domain kidney disease. To comprehend evolving landscape AI disease, bibliometric analysis is essential. The purposes this study are to systematically analyze and quantify scientific output, trends, collaborative networks application This collected AI-related published between 2012 20 November 2023 from Web Science. Descriptive analyses trends disease were used determine growth rate publications by authors, journals, institutions, countries. Visualization network maps country collaborations author-provided keyword co-occurrences generated show hotspots on initial search yielded 673 articles, which 631 included analyses. Our findings reveal noteworthy exponential trend annual

Язык: Английский

Процитировано

11
Harnessing technology for infectious disease response in conflict zones: Challenges, innovations, and policy implications DOI Creative Commons
Okechukwu Paul-Chima Ugwu, Esther Ugo Alum,

Jovita Nnenna Ugwu

и другие.

Medicine, Год журнала: 2024, Номер 103(28), С. e38834 - e38834

Опубликована: Июль 12, 2024

Epidemic outbreaks of infectious diseases in conflict zones are complex threats to public health and humanitarian activities that require creativity approaches reducing their damage. This narrative review focuses on the technology intersection with disease response zones, complexity healthcare infrastructure, population displacement, security risks. explores how conflict-related destruction is harmful towards systems impediments surveillance activities. In this regards, also considered contributions technological innovations, such as improvement epidemiological surveillance, mobile (mHealth) technologies, genomic sequencing, strengthening management settings. Ethical issues related data privacy, fairness covered. By advisement policy investment systems, diagnostic capacity, capacity building, collaboration, even ethical governance, stakeholders can leverage enhance settings and, thus, protect global security. full information for researchers, policymakers, practitioners who dealing conflicts worn areas.

Язык: Английский

Процитировано

10
Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts DOI Creative Commons
Luís Ramalhete,

Paula Almeida,

Raquel Ferreira

и другие.

BioMedInformatics, Год журнала: 2024, Номер 4(1), С. 673 - 689

Опубликована: Март 1, 2024

This review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against backdrop a significant donor organ shortage evolution ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI ML can enhance process, from selection postoperative patient care. Our methodology involved comprehensive current research, focusing on application in various stages KT. included an analysis donor–recipient matching, predictive modeling, improvement The results indicated that significantly improve efficiency success rates They aid better reduce rejection, monitoring Predictive based extensive data analysis, has been particularly effective identifying suitable matches anticipating complications. In conclusion, this discusses transformative impact KT, offering more precise, personalized, healthcare solutions. Their field addresses critical issues like shortages post-transplant However, successful these technologies requires careful consideration their ethical, privacy, training aspects settings.

Язык: Английский

Процитировано

8
Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration DOI Open Access
Shabbar Abbas, Zeeshan Abbas,

Arifa Zahir

и другие.

Healthcare, Год журнала: 2024, Номер 12(24), С. 2587 - 2587

Опубликована: Дек. 22, 2024

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, remote monitoring, which empower real-time, decentralized data processing for predictive analytics personalized care. It addresses key challenges, including security risks like adversarial attacks, poisoning, model inversion. Additionally, it covers issues related to heterogeneity, scalability, system interoperability. Alongside these, the highlights emerging privacy-preserving solutions, such as differential secure multiparty computation, critical overcoming limitations. Successfully addressing these hurdles essential enhancing efficiency, accuracy, broader adoption in healthcare. Ultimately, FL offers transformative potential secure, data-driven promising improved outcomes, operational sovereignty ecosystem.

Язык: Английский

Процитировано

8
Deep Learning-Assisted Smartphone-Based Electrochemiluminescence Visual Monitoring Biosensor: A Fully Integrated Portable Platform DOI Creative Commons
Manish Bhaiyya, Prakash Rewatkar, Amit Pimpalkar

и другие.

Micromachines, Год журнала: 2024, Номер 15(8), С. 1059 - 1059

Опубликована: Авг. 22, 2024

A novel, portable deep learning-assisted smartphone-based electrochemiluminescence (ECL) cost-effective (~10$) sensing platform was developed and used for selective detection of lactate. Low-cost, fast prototyping screen printing wax methods with paper-based substrate were to fabricate miniaturized single-pair electrode ECL platforms. The lab-made 3D-printed black box served as a reaction chamber. This integrated smartphone buck-boost converter, eliminating the need expensive CCD cameras, photomultiplier tubes, bulky power supplies. advancement makes this ideal point-of-care testing applications. Foremost, integration learning approach enhance not just accuracy sensors, but also expedite diagnostic procedure. models trained (3600 images) tested (900 using images obtained from experimentation. Herein, user convenience, an Android application graphical interface developed. app performs several tasks, which include capturing real-time images, cropping them, predicting concentration required bioanalytes through learning. device’s capability work in real environment by performing lactate sensing. fabricated device shows good liner range (from 50 µM 2000 µM) acceptable limit value 5.14 µM. Finally, various rigorous analyses, including stability, reproducibility, unknown sample analysis, conducted check durability stability. Therefore, becomes versatile applicable across domains harnessing cutting-edge technology integrating it smartphone.

Язык: Английский

Процитировано

7