Journal of Medicine Surgery and Public Health, Год журнала: 2024, Номер unknown, С. 100141 - 100141
Опубликована: Окт. 1, 2024
Язык: Английский
Geriatric Nursing, Год журнала: 2024, Номер 61, С. 41 - 49
Опубликована: Ноя. 14, 2024
Язык: Английский
Процитировано
4
Data, Год журнала: 2025, Номер 10(1), С. 4 - 4
Опубликована: Янв. 2, 2025
Patient-level grouped data are prevalent in public health and medical fields, multiple instance learning (MIL) offers a framework to address the challenges associated with this type of structure. This study compares four aggregation methods designed tackle structure classification tasks: post-mean, post-max, post-min, pre-mean aggregation. We developed customized AI pipeline that incorporates twelve machine algorithms along detect Parkinson’s disease (PD) using voice recordings from individuals available UCI Machine Learning Repository, which includes 756 188 PD patients 64 healthy individuals. Seven performance metrics—accuracy, precision, sensitivity, specificity, F1 score, AUC, MCC—were utilized for model evaluation. Various techniques, such as Bag Over-Sampling (BOS), cross-validation, grid search, were implemented enhance performance. Among methods, post-mean combined XGBoost achieved highest accuracy (0.880), score (0.922), MCC (0.672). Furthermore, we identified potential trends selecting suitable imbalanced data, particularly based on their differences sensitivity specificity. These findings provide meaningful implications further exploration data.
Язык: Английский
Процитировано
0
Diseases, Год журнала: 2025, Номер 13(1), С. 24 - 24
Опубликована: Янв. 20, 2025
Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy radiation often result in significant side effects varied patient outcomes. Immunotherapy has emerged as promising alternative, harnessing the immune system to target cancer cells. However, complexity responses tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores role artificial intelligence [AI] enhancing efficacy immunotherapy, predicting responses, discovering novel therapeutic targets. Methods: A comprehensive literature was conducted, focusing on studies published between 2010 2024 that examined application AI immunotherapy. Databases such PubMed, Google Scholar, Web Science were utilized, articles selected based relevance topic. Results: significantly contributed identifying biomarkers predict immunotherapy by analyzing genomic, transcriptomic, proteomic data. It also optimizes combination therapies most effective treatment protocols. AI-driven predictive models help assess response guiding clinical decision-making minimizing effects. Additionally, facilitates discovery targets, neoantigens, enabling development personalized immunotherapies. Conclusions: holds immense potential transforming related data privacy, algorithm transparency, integration must be addressed. Overcoming these hurdles will likely make central component future offering more treatments.
Язык: Английский
Процитировано
0
Journal of Radiology Nursing, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
European Journal of Education, Год журнала: 2025, Номер 60(1)
Опубликована: Янв. 27, 2025
ABSTRACT The acceptance of artificial intelligence (AI) in academic settings, particularly the context research creativity, is a growing area interest. This study aimed to design and validate AI Acceptance Research Creativity Scale (AIA&RCS) among faculty members. exploratory mixed‐method was conducted 720 A literature review participant interviews were qualitative phase generate develop items. In quantitative phase, face validity, content construct convergent validity reliability (internal consistency stability) used. Exploratory factor analysis (EFA) indicated 4‐factor model scale with ‘perceived usefulness effectiveness creativity’, ‘ethical issues research’, ‘trusted capabilities’ ‘willingness use AI’ accounting for 51.6% variance. arrangement verified by confirmatory (CFA), fit indices that at suitable levels. Then, network took into account four‐factor structure AIA&RCS further. Similarly, graph (EGA) configuration AIA&RCS. 25‐item well‐suited measuring innovation because its psychometrics.
Язык: Английский
Процитировано
0
Frontiers in Pediatrics, Год журнала: 2025, Номер 13
Опубликована: Фев. 24, 2025
Artificial Intelligence (AI) has the potential to revolutionize Pediatric Intensive Care Units (PICUs) by enhancing diagnostic accuracy, improving patient outcomes, and streamlining routine tasks. However, integrating AI into PICU environments poses significant ethical data privacy challenges, necessitating effective governance robust regulatory frameworks ensure safe implementation. This study aimed explore valuable insights healthcare professionals' current perceptions readiness adopt in pediatric critical care, highlighting opportunities challenges ahead. A cross-sectional conducted an online survey among practitioners at King Abdulaziz University Hospital Jeddah, Saudi Arabia. The included questions about professional roles, experience, familiarity with AI, their opinions on AI's role, trust AI-driven decisions, concerns. Statistical analyses were performed using IBM SPSS. Results found varying professionals, many expressing limited knowledge of applications settings. Despite this, there was growing recognition applications. Trust decisions for management mixed, most partial trust. Opinions role accuracy outcomes varied. Ethical considerations, privacy, address highlighted as Healthcare preferred monitoring but had concerns its use diagnoses advanced healthcare. Concerns held regarding security breaches, confidentiality.
Язык: Английский
Процитировано
0
Medicina, Год журнала: 2025, Номер 61(3), С. 433 - 433
Опубликована: Фев. 28, 2025
The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview current applications future potential AI ophthalmology. algorithms, particularly those utilizing machine learning (ML) deep (DL), have demonstrated remarkable success diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, glaucoma with precision comparable to, or exceeding, human experts. Furthermore, being utilized develop personalized plans by analyzing large datasets predict individual responses therapies, thus optimizing patient outcomes reducing healthcare costs. In surgical applications, AI-driven tools are enhancing procedures like cataract surgery, contributing better recovery times reduced complications. Additionally, AI-powered teleophthalmology services expanding access eye care underserved remote areas, addressing global disparities availability. Despite these advancements, challenges remain, concerning data privacy, security, algorithmic bias. Ensuring robust governance ethical practices crucial for continued conclusion, research should focus on developing sophisticated models capable handling multimodal data, including genetic information histories, provide deeper insights into disease mechanisms responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), technology companies essential deploy solutions effectively, especially low-resource settings.
Язык: Английский
Процитировано
0
Nurse Education in Practice, Год журнала: 2025, Номер 84, С. 104330 - 104330
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Health care science, Год журнала: 2025, Номер 4(2), С. 154 - 157
Опубликована: Март 24, 2025
The integration of artificial intelligence (AI) into various sectors has undoubtedly brought about numerous benefits, from increased efficiency to innovative problem-solving. growing influence AI across several industries may help achieve the sustainable development goals (SDGs). However, due revolution happening in globe, older employees are often confronted with significant hurdles keeping pace these changes. threat job displacement looms large as automation driven by encroaches upon routine tasks previously performed human workers. Job insecurity, that is, worry losing one's encompasses anxiety, and uneasiness, affects mental health employees. To address challenges empower era open AI, it is imperative organizations implement targeted strategies tailored their unique needs circumstances. Employees use opportunities for continued education provided them company support prevent unwanted effects. can create an inclusive supportive environment where empowered embrace presented while leveraging experience expertise drive innovation success.
Язык: Английский
Процитировано
0
Journal of Health Organization and Management, Год журнала: 2025, Номер unknown
Опубликована: Март 24, 2025
Purpose This narrative review explores the integration of artificial intelligence (AI) and Internet Things (IoT) technologies in Tanzania’s primary healthcare system. It aims to identify barriers adoption, propose strategies for effective implementation align these insights with digital health transformation goals. Design/methodology/approach A methodology was employed, synthesising evidence from 21 peer-reviewed studies reports published between 2015 2024. The thematic analysis examined barriers, research gaps, focusing on technical, socio-cultural organisational factors specific context. Findings highlights several challenges, including infrastructural limitations, low literacy, resistance lack robust policy frameworks. Strategies such as participatory system design, capacity building investments resilient infrastructure emerged critical enablers. Insights also underscore importance addressing ethical considerations customising solutions unique socio-economic cultural realities. Originality/value study uniquely focuses Tanzanian context, providing actionable recommendations bridge gap AI-IoT technological potential practical low-resource settings. Integrating global local offers a comprehensive framework guide policymakers, practitioners stakeholders advancing innovations personalised needs systems.
Язык: Английский
Процитировано
0