Methods in microbiology, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
Language: Английский
AI & Society, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 7, 2025
Language: Английский
Citations
1
Clinical Neurology and Neurosurgery, Journal Year: 2025, Volume and Issue: unknown, P. 108761 - 108761
Published: Jan. 1, 2025
Stroke remains a leading cause of death and disability worldwide, with African populations bearing disproportionately high burden due to limited healthcare infrastructure. Early prediction intervention are critical reducing stroke outcomes. This study developed evaluated system using Gated Recurrent Units (GRU), variant Neural Networks (RNN), leveraging the Afrocentric Investigative Research Education Network (SIREN) dataset. The utilized secondary data from SIREN dataset, comprising 4236 records 29 phenotypes. Feature selection reduced these 15 optimal phenotypes based on their significance occurrence. GRU model, designed 128 input neurons four hidden layers (64, 32, 16, 8 neurons), was trained 150 epochs, batch size 8, metrics such as accuracy, AUC, time. Comparisons were made traditional machine learning algorithms (Logistic Regression, SVM, KNN) Long Short-Term Memory (LSTM) networks. GRU-based achieved performance accuracy 77.48 %, an AUC 0.84, time 0.43 seconds, outperforming all other models. Logistic Regression 73.58 while LSTM reached 74.88 % but longer 2.23 seconds. significantly improved model's compared demonstrated superior in prediction, offering efficient scalable tool for healthcare. Future research should focus integrating unstructured data, validating model diverse populations, exploring hybrid architectures enhance predictive accuracy.
Language: Английский
Citations
1
Clinica Chimica Acta, Journal Year: 2025, Volume and Issue: 569, P. 120181 - 120181
Published: Feb. 3, 2025
The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing computer vision) advance evidence-based medicine (EBLM) by automating optimizing critical processes(formulating clinical questions, conducting literature searches, appraising evidence, developing guidelines). These reduce the time for systematic reviews, ensuring consistency in appraisal, enabling real-time updates to guidelines. supports analyzing large datasets, genetic information electronic health records (EHRs), tailor treatment plans profiles. Predictive analytics enhance outcomes leveraging historical data ongoing monitoring predict responses optimize care pathways. Despite transformative potential, there are challenges. transparency, explainability algorithms gaining trust ethical deployment. Integration existing workflows requires collaboration between developers users ensure seamless user-friendly adoption. Ethical considerations, such as privacy,data security, algorithmic bias, must also be addressed mitigate risks equitable healthcare delivery. Regulatory frameworks, eg. EU Regulation, emphasize governance, human oversight, particularly high-risk systems. economic benefits cost savings, improved precision, enhanced outcomes. Future trends (federated learning self-supervised learning), will scalability applicability EBLM, paving way a new era precision medicine. EBLM has potential transform delivery, improve outcomes, personalized/precision
Language: Английский
Citations
1
Chinese Science Bulletin (Chinese Version), Journal Year: 2025, Volume and Issue: 70(7), P. 789 - 798
Published: Feb. 11, 2025
Citations
1
International Journal of Medical Informatics, Journal Year: 2025, Volume and Issue: 199, P. 105909 - 105909
Published: April 6, 2025
Artificial Intelligence (AI) is increasingly being integrated into healthcare to improve diagnostics, treatment planning, and operational efficiency. However, its adoption raises significant concerns related data privacy, ethical integrity, regulatory compliance. While much of the existing literature focuses on clinical applications AI, limited attention has been given perspectives Information Governance (IG) professionals, who play a critical role in ensuring responsible compliant AI implementation within systems. This study aims explore perceptions IG professionals Kent, United Kingdom, use delivery research, with focus governance, considerations, implications. A qualitative exploratory design was employed. Six from NHS trusts Kent were purposively selected based their roles compliance, policy enforcement. Semi-structured interviews conducted thematically analysed using NVivo software, guided by Unified Theory Acceptance Use Technology (UTAUT). Thematic analysis revealed varying levels knowledge among professionals. participants acknowledged AI's potential efficiency, they raised about accuracy, algorithmic bias, cybersecurity risks, unclear frameworks. Participants also highlighted importance need for national oversight. offers promising opportunities healthcare, but must be underpinned robust governance structures. Enhancing literacy teams establishing clearer frameworks will key safe implementation.
Language: Английский
Citations
1
Health Affairs, Journal Year: 2024, Volume and Issue: 43(6), P. 776 - 782
Published: June 1, 2024
Public health practice appears poised to undergo a transformative shift as result of the latest advancements in artificial intelligence (AI). These changes will usher new era public health, charged with responding deficiencies identified during COVID-19 pandemic and managing investments required meet needs twenty-first century. In this Commentary, we explore how AI is being used describe advanced capabilities generative models capable producing synthetic content such images, videos, audio, text, other digital content. Viewing use from perspective departments United States, examine technology can support core functions focus on near-term opportunities improve communication, optimize organizational performance, generate novel insights drive decision making. Finally, review challenges risks associated these technologies, offering suggestions for officials harness tools accomplish goals.
Language: Английский
Citations
8
Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12
Published: July 3, 2024
Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale datasets and the tailored dissemination health-related information interventions. This article conducts a profound exploration into integration AI within health, accentuating scientific foundations, prospective progress, practical application scenarios. It underscores transformative potential in crafting individualized educational programs, developing sophisticated behavioral models, informing creation policies. The manuscript strives to thoroughly evaluate extant landscape applications scrutinizing critical challenges such as propensity data bias imperative safeguarding privacy. By dissecting these issues, contributes conversation on how can be harnessed responsibly effectively, ensuring that both ethically grounded equitable. paper's significance multifold: it aims provide blueprint policy formulation, offer actionable insights authorities, catalyze progression interventions toward increasingly precise approaches. Ultimately, this research anticipates fostering an environment where not only augments but also does so with steadfast commitment principles justice inclusivity, thereby elevating standard reach initiatives globally.
Language: Английский
Citations
8
Indian Journal of Animal Research, Journal Year: 2024, Volume and Issue: Of
Published: Feb. 13, 2024
Background: This work explores the expansivetab realm of Artificial Intelligence (AI) applications in dynamic landscape animal health and veterinary sciences. Addressing challenges conventional approaches, we delve into how AI is transforming diagnosis, treatment healthcare practices for diverse species. Methods: Through a rigorous literature review methodology, study navigates current state health, identifying gaps emphasizing need further research. Looking ahead, paper outlines future directions opportunities, contributing to discourse on technology’s intersection with care. By providing comprehensive overview, this research paves way innovative solutions, promising brighter healthier our companions. Result: In domain emerges as powerful tool early disease detection intervention, offering personalized plans proactive management through continuous monitoring surveillance. sciences, accelerates drug discovery, enhances genetic reshapes surgical procedures robotic assistance. However, ethical considerations challenges, including data privacy AI-driven decision-making critical examination should be addressed to.
Language: Английский
Citations
7
Public Health Nursing, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 4, 2024
ABSTRACT Background Artificial intelligence now encompasses technologies like machine learning, natural language processing, and robotics, allowing machines to undertake complex tasks traditionally done by humans. AI's application in healthcare has led advancements diagnostic tools, predictive analytics, surgical precision. Aim This comprehensive review aims explore the transformative impact of AI across diverse domains, highlighting its applications, advancements, challenges, contributions enhancing patient care. Methodology A literature search was conducted multiple databases, covering publications from 2014 2024. Keywords related applications were used gather data, focusing on studies exploring role medical specialties. Results demonstrated substantial benefits various fields medicine. In cardiology, it aids automated image interpretation, risk prediction, management cardiovascular diseases. oncology, enhances cancer detection, treatment planning, personalized drug selection. Radiology improved analysis accuracy, while critical care sees triage resource optimization. integration into pediatrics, surgery, public health, neurology, pathology, mental health similarly shown significant improvements precision, treatment, overall The implementation low‐resource settings been particularly impactful, access advanced tools treatments. Conclusion is rapidly changing industry greatly increasing accuracy diagnoses, streamlining plans, improving outcomes a variety specializations. underscores potential, early disease detection ability augment delivery, resource‐limited settings.
Language: Английский
Citations
7
Infectious Diseases, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 26
Published: Nov. 14, 2024
Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as transformative force in healthcare, offering promising solutions to address this challenge. This review article provides comprehensive overview of the pivotal role AI can play treatment infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, image recognition systems, enhance accuracy efficiency disease detection surveillance. Furthermore, it delves into potential predict outbreaks, optimise strategies, personalise interventions based on individual patient data be used gear up drug discovery development (D3) process.The ethical considerations, challenges, limitations associated with integration management are also examined. By harnessing capabilities AI, healthcare systems significantly improve preparedness, responsiveness, outcomes battle against
Language: Английский
Citations
5