Clinical Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 10
Published: Jan. 14, 2025
Introduction Delayed neurological sequelae is a common complication following carbon monoxide poisoning, which significantly affects the quality of life patients with condition. We aimed to develop machine learning-based prediction model predict frequency delayed in poisoning.
Language: Английский
Computer Methods and Programs in Biomedicine Update, Journal Year: 2024, Volume and Issue: 5, P. 100148 - 100148
Published: Jan. 1, 2024
Clinical prediction is integral to modern healthcare, leveraging current and historical medical data forecast health outcomes. The integration of Artificial Intelligence (AI) in this field significantly enhances diagnostic accuracy, treatment planning, disease prevention, personalised care leading better patient outcomes healthcare efficiency. This systematic review implemented a structured four-step methodology, including an extensive literature search academic databases (PubMed, Embase, Google Scholar), applying specific inclusion exclusion criteria, extraction focusing on AI techniques their applications clinical prediction, thorough analysis the collected information understand AI's roles enhancing prediction. Through 74 experimental studies, eight key domains, where were identified: 1) Diagnosis early detection disease; 2) Prognosis course outcomes; 3) Risk assessment future 4) Treatment response for medicine; 5) Disease progression; 6) Readmission risks; 7) Complication 8) Mortality Oncology radiology come top specialties benefiting from highlights transformative impact across various its role revolutionising diagnostics, improving prognosis aiding medicine, safety. AI-driven tools contribute efficiency effectiveness delivery. marks substantial advancement healthcare. Recommendations include quality accessibility, promoting interdisciplinary collaboration, ethical practices, investing education, expanding trials, developing regulatory oversight, involving patients process, continuous monitoring improvement systems.
Language: Английский
Citations
46
Cureus, Journal Year: 2024, Volume and Issue: unknown
Published: June 15, 2024
Artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing health care by offering unprecedented opportunities to enhance patient care, optimize clinical workflows, advance medical research. However, the integration of AI ML into healthcare systems raises significant ethical considerations that must be carefully addressed ensure responsible equitable deployment. This comprehensive review explored multifaceted surrounding use in including privacy data security, algorithmic bias, transparency, validation, professional responsibility. By critically examining these dimensions, stakeholders can navigate complexities while safeguarding welfare upholding principles. embracing best practices fostering collaboration across interdisciplinary teams, community harness full potential usher a new era personalized data-driven prioritizes well-being equity.
Language: Английский
Citations
44
Computer Methods and Programs in Biomedicine Update, Journal Year: 2024, Volume and Issue: 5, P. 100142 - 100142
Published: Jan. 1, 2024
Language: Английский
Citations
29
Computer Methods and Programs in Biomedicine Update, Journal Year: 2024, Volume and Issue: 5, P. 100141 - 100141
Published: Jan. 1, 2024
Diabetes, a major cause of premature mortality, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role Artificial Intelligence (AI) in enhancing prevention, diagnosis, management diabetes, highlighting potential for personalised proactive healthcare. A structured four-step method was used, including extensive literature searches, specific inclusion exclusion criteria, data extraction from selected studies focusing on AI's thorough analysis identify domains functions where AI contributes significantly. Through examining 43 experimental studies, has been identified as transformative force across eight key diabetes care: 1) Diabetes Management Treatment, 2) Diagnostic Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Interventions, 6) Lifestyle Dietary Management, 7) Enhancing Clinical Decision-Making, 8) Patient Engagement Self-Management. Each domain showcases revolutionise care, personalising treatment plans improving diagnostic accuracy patient engagement predictive integration into care offers personalised, efficient, solutions. It enhances accuracy, empowers patients, provides better understanding management. However, successful implementation requires continued research, security, interdisciplinary collaboration, focus patient-centred Education healthcare professionals regulatory frameworks are also crucial address challenges like algorithmic bias ethics. promises improved health outcomes quality life through Future efforts should investment, ensuring fostering prioritising Regular monitoring evaluation essential adjust strategies understand long-term impacts, ethical effective
Language: Английский
Citations
20
Ageing Research Reviews, Journal Year: 2024, Volume and Issue: unknown, P. 102497 - 102497
Published: Sept. 1, 2024
Language: Английский
Citations
20
Journal of Materials Science, Journal Year: 2024, Volume and Issue: 59(31), P. 14095 - 14140
Published: July 30, 2024
Abstract Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, mechanical flexibility, alongside adjustable fiber diameter distribution modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun to meet specific requirements has proven be challenging endeavor. The electrospinning process is inherently complex influenced by numerous variables, applied voltage, polymer concentration, solution flow rate, molecular weight polymer, needle-to-collector distance. This complexity often results in variations nanofibers, making it difficult achieve desired characteristics consistently. Traditional trial-and-error approaches parameter optimization been time-consuming costly, they lack precision necessary address these challenges effectively. In recent years, convergence materials science machine learning (ML) offered transformative approach electrospinning. By harnessing power ML algorithms, scientists researchers can navigate intricate space more efficiently, bypassing need for extensive experimentation. holds potential significantly reduce time resources invested producing wide range applications. Herein, we provide an in-depth analysis current work that leverages obtain target nanofibers. examining work, explore intersection ML, shedding light on advancements, challenges, future directions. comprehensive not only highlights processes but also provides valuable insights into evolving landscape, paving way innovative precisely engineered various Graphical abstract
Language: Английский
Citations
18
Molecular Biomedicine, Journal Year: 2025, Volume and Issue: 6(1)
Published: Jan. 3, 2025
Abstract Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates multidimensional role AI in pandemic, which arises as a global health crisis, and its preparedness responses, ranging from enhanced epidemiological modelling to acceleration vaccine development. The confluence technologies guided us new era data-driven decision-making, revolutionizing our ability anticipate, mitigate, treat infectious illnesses. begins by discussing impact on emerging countries worldwide, elaborating critical significance modelling, bringing enabling forecasting, mitigation response pandemic. In epidemiology, AI-driven models like SIR (Susceptible-Infectious-Recovered) SIS (Susceptible-Infectious-Susceptible) are applied predict spread disease, preventing outbreaks optimising distribution. also demonstrates how Machine Learning (ML) algorithms predictive analytics improve knowledge disease propagation patterns. collaborative aspect discovery clinical trials various vaccines is emphasised, focusing constructing AI-powered surveillance networks. Conclusively, presents comprehensive assessment impacts builds AI-enabled dynamic collaborating ML Deep (DL) techniques, develops implements trials. focuses screening, contact tracing monitoring virus-causing It advocates for sustained research, real-world implications, ethical application strategic integration strengthen collective face alleviate effects issues.
Language: Английский
Citations
4
Clinics and Practice, Journal Year: 2023, Volume and Issue: 13(6), P. 1460 - 1487
Published: Nov. 20, 2023
The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large models (LLMs) for use healthcare. This study assesses the performance of two LLMs, GPT-3.5 GPT-4 models, passing MIR medical examination access specialist training Spain. Our objectives included gauging model's overall performance, analyzing discrepancies across different specialties, discerning between theoretical practical questions, estimating error proportions, assessing hypothetical severity errors committed by a physician.We studied 2022 Spanish results after excluding those questions requiring image evaluations or having acknowledged errors. remaining 182 were presented LLM English. Logistic regression analyzed relationships question length, sequence, performance. We also 23 with images, using GPT-4's new analysis capability.GPT-4 outperformed GPT-3.5, scoring 86.81% (p < 0.001). English translations had slightly enhanced scored 26.1% images worse when Spanish, 13.0%, although differences not statistically significant = 0.250). Among achieved 100% correct response rate several areas, Pharmacology, Critical Care, Infectious Diseases specialties showed lower revealed that while 13.2% existed, gravest categories, such as "error intervention sustain life" resulting death", 0% rate.GPT-4 performs robustly on examination, varying capabilities discriminate knowledge specialties. While high success is commendable, understanding critical, especially considering AI's potential role real-world practice its implications patient safety.
Language: Английский
Citations
40
Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7
Published: Jan. 11, 2024
Rare and complex diseases pose significant challenges to both patients healthcare providers. These conditions often present with atypical symptoms, making diagnosis treatment a formidable task. In recent years, artificial intelligence natural language processing technologies have shown great promise in assisting medical professionals diagnosing managing such conditions. This paper explores the role of ChatGPT, an advanced model, improving rare diseases. By analyzing its potential applications, limitations, ethical considerations, we demonstrate how ChatGPT can contribute better patient outcomes enhance system's overall effectiveness.
Language: Английский
Citations
10
Tropical Medicine and Infectious Disease, Journal Year: 2024, Volume and Issue: 9(10), P. 228 - 228
Published: Sept. 30, 2024
The integration of artificial intelligence (AI) in clinical medicine marks a revolutionary shift, enhancing diagnostic accuracy, therapeutic efficacy, and overall healthcare delivery. This review explores the current uses, benefits, limitations, future applications AI infectious diseases, highlighting its specific diagnostics, decision making, personalized medicine. transformative potential diseases is emphasized, addressing gaps rapid accurate disease diagnosis, surveillance, outbreak detection management, treatment optimization. Despite these advancements, significant limitations challenges exist, including data privacy concerns, biases, ethical dilemmas. article underscores need for stringent regulatory frameworks inclusive databases to ensure equitable, ethical, effective utilization field laboratory diseases.
Language: Английский
Citations
9