Exploring nurses’ awareness and attitudes toward artificial intelligence: Implications for nursing practice

Digital Health, Год журнала: 2024, Номер 10

Опубликована: Янв. 1, 2024

Introduction Worldwide, healthcare systems aim to achieve the best possible quality of care at an affordable cost while ensuring broad access for all populations. The use artificial intelligence (AI) in holds promise address these challenges through integration real-world data-driven insights into patient processes. This study aims assess nurses’ awareness and attitudes toward AI-integrated tools used clinical practice. Methods A descriptive cross-sectional design captured responses three governmental hospitals Saudi Arabia by using online questionnaire administered over 4 months. involved 220 registered nurses with a minimum one year experience, selected convenience sampling method. survey consisted sections: demographic information, assessment AI knowledge, general scale. Results Nurses displayed “moderate” levels technology, 70.9% having basic information about only 58.2% (128 nurses) were considered “aware” as they dealt its applications. expressed openness ( M = 3.51) on side, but also had some concerns AI. conservative AI, significant differences observed based gender (χ² 4.67, p < 0.05). Female exhibited higher proportion negative compared male nurses. Significant found age 9.31, 0.05), younger demonstrating more positive their older counterparts. Educational background yields 6.70, holding undergraduate degrees exhibiting highest attitudes. However, years nursing experience did not reveal variations Conclusion Healthcare administrators need work increasing applications emphasize importance integrating such technology use. Moreover, addressing AI's control discomfort is crucial, especially considering generational differences, often technology. Change management strategies may help overcome any hindrances.

Язык: Английский

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The Role of Artificial Intelligence and Machine Learning in Predicting and Combating Antimicrobial Resistance

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер 27, С. 423 - 439

Опубликована: Янв. 1, 2025

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised deep reinforcement natural language processing are some main tools used this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, epidemiological for predicting AMR outbreaks. Although relatively new fields, numerous case studies offer substantial evidence their successful application outbreaks with greater accuracy. These provide insights into discovery novel antimicrobials, repurposing existing drugs, combination therapy through analysis molecular structures. In addition, AI-based decision support systems real-time guide healthcare professionals improve prescribing antibiotics. also outlines how AI surveillance, analyze trends, enable early outbreak identification. Challenges, ethical considerations, privacy, model biases exist, however, continuous development methodologies enables play significant combating

Язык: Английский

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Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(2), С. 399 - 399

Опубликована: Янв. 10, 2025

Background/Objectives: The aim of this study was to analyze whether the implementation artificial intelligence (AI), specifically Natural Language Processing (NLP) branch developed by OpenAI, could help a thoracic multidisciplinary tumor board (MTB) make decisions if provided with all patient data presented committee and supported accepted clinical practice guidelines. Methods: This is retrospective comparative study. inclusion criteria were defined as patients who at MTB suspicious or first diagnosis non-small-cell lung cancer between January 2023 June 2023. Intervention: GPT 3.5 turbo chat used, providing case summary in proceedings latest SEPAR treatment application asked issue one following recommendations: follow-up, surgery, chemotherapy, radiotherapy, chemoradiotherapy. Statistical analysis: A concordance analysis performed measuring Kappa coefficient evaluate consistency results AI committee's decision. Results: Fifty-two included had an overall 76%, index 0.59 replicability 92.3% for whom it recommended surgery (after repeating cases four times). Conclusions: interesting tool which decision making MTBs.

Язык: Английский

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Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression?

Medical Sciences, Год журнала: 2025, Номер 13(1), С. 8 - 8

Опубликована: Янв. 11, 2025

Depression poses significant challenges to global healthcare systems and impacts the quality of life individuals their family members. Recent advancements in artificial intelligence (AI) have had a transformative impact on diagnosis treatment depression. These innovations potential significantly enhance clinical decision-making processes improve patient outcomes settings. AI-powered tools can analyze extensive data—including medical records, genetic information, behavioral patterns—to identify early warning signs depression, thereby enhancing diagnostic accuracy. By recognizing subtle indicators that traditional assessments may overlook, these enable providers make timely precise decisions are crucial preventing onset or escalation depressive episodes. In terms treatment, AI algorithms assist personalizing therapeutic interventions by predicting effectiveness various approaches for individual patients based unique characteristics history. This includes recommending tailored plans consider patient’s specific symptoms. Such personalized strategies aim optimize overall efficiency healthcare. theoretical review uniquely synthesizes current evidence applications primary care depression management, offering comprehensive analysis both personalization capabilities. Alongside advancements, we also address conflicting findings field presence biases necessitate important limitations.

Язык: Английский

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Transforming Healthcare in Low‐Resource Settings With Artificial Intelligence: Recent Developments and Outcomes

Public Health Nursing, Год журнала: 2024, Номер unknown

Опубликована: Дек. 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.

Язык: Английский

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Validation of an acute kidney injury prediction model as a clinical decision support system

Kidney Research and Clinical Practice, Год журнала: 2025, Номер unknown

Опубликована: Янв. 9, 2025

Background Acute kidney injury (AKI) is a critical clinical condition that requires immediate intervention. We developed an artificial intelligence (AI) model called PRIME Solution to predict AKI and evaluated its ability enhance clinicians' predictions. Methods The was using convolutional neural networks with residual blocks on 183,221 inpatient admissions from tertiary hospital (2013−2017) externally validated 4,501 at another (2020−2021). To assess application, we conducted prospective evaluation retrospectively collected data 100 patients the latter hospital, including 15 cases. prediction performance compared among specialists, physicians, medical students, both without AI assistance. Results Without assistance, specialists demonstrated highest accuracy (0.797), followed by students (0.619) (0.568). assistance improved overall recall (61.0% 74.0%) F1 scores (38.7% 42.0%), while reducing average review time (73.8 65.4 seconds, p < 0.001). However, impact varied across expertise levels. Specialists showed greatest improvement (recall, 32.1% 64.3%; F1, 36.4% 48.6%), whereas students' but aligned more closely model. Additionally, effect of outcome, showing greater in for cases predicted as AKI, better precision, score, reduction (73.4 62.1 0.001) non-AKI. Conclusion predictions were enhanced improvements according user. Keywords: injury, Artificial intelligence, Evaluation study, Machine learning

Язык: Английский

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Transforming Healthcare Delivery: AI-Powered Clinical Decision Support Systems

International Journal of Scientific Research in Computer Science Engineering and Information Technology, Год журнала: 2025, Номер 11(1), С. 339 - 347

Опубликована: Янв. 8, 2025

Integrating Artificial Intelligence in Clinical Decision Support Systems (CDSS) has fundamentally transformed healthcare delivery by enhancing diagnostic accuracy, improving treatment outcomes, and streamlining clinical workflows. This comprehensive article explores the key features, benefits, implementation challenges, future innovations AI-powered CDSS. Through examination of real-world implementations across multiple institutions, this demonstrates how advanced algorithms, multimodal data integration, automated analysis capabilities are revolutionizing decision-making. The highlights significant improvements reduced medical errors, enhanced patient outcomes while addressing critical challenges quality, workflow regulatory compliance, clinician acceptance. Furthermore, emerging technologies, including federated learning, ambient intelligence, extended reality providing insights into evolution decision support systems.

Язык: Английский

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Improving clinical decision making by creating surrogate models from health technology assessment models: A case study on Type 1 Diabetes Melitus

Computer Methods and Programs in Biomedicine, Год журнала: 2025, Номер 262, С. 108646 - 108646

Опубликована: Фев. 10, 2025

Язык: Английский

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Readability of Hospital Online Patient Education Materials Across Otolaryngology Specialties

Laryngoscope Investigative Otolaryngology, Год журнала: 2025, Номер 10(1)

Опубликована: Фев. 1, 2025

ABSTRACT Introduction This study evaluates the readability of online patient education materials (OPEMs) across otolaryngology subspecialties, hospital characteristics, and national organizations, while assessing AI alternatives. Methods Hospitals from US News Best ENT list were queried for OPEMs describing a chosen surgery per subspecialty; American Academy Otolaryngology—Head Neck Surgery (AAO), Laryngological Association (ALA), Ear, Nose, Throat United Kingdom (ENTUK), Canadian Society (CSOHNS) similarly queried. Google was top 10 links hospitals procedure. Ownership (private/public), presence respective fellowships, region, median household income (zip code) collected. Readability assessed using seven indices averaged: Automated Index (ARI), Flesch Reading Ease Score (FRES), Flesch–Kincaid Grade Level (FKGL), Gunning Fog (GFR), Simple Measure Gobbledygook (SMOG), Coleman–Liau (CLRI), Linsear Write Formula (LWRF). AI‐generated ChatGPT compared readability, accuracy, content, tone. Analyses conducted between against NIH standard, demographic variables. Results Across 144 hospitals, exceeded standards, averaging at an 8th–12th grade level subspecialties. In rhinology, facial plastics, sleep medicine, had higher scores than ENTUK's (11.4 vs. 9.1, 10.4 7.2, 11.5 9.2, respectively; all p < 0.05), but lower AAO ( = 0.005). ChatGPT‐generated averaged 6.8‐grade level, demonstrating improved especially with specialized prompting, to organization OPEMs. Conclusion sources exceed standard. ENTUK serves as benchmark accessible language, demonstrates feasibility producing more readable content. Otolaryngologists might consider generate patient‐friendly materials, caution, advocate national‐level improvements in readability.

Язык: Английский

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A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis

Computer Methods and Programs in Biomedicine Update, Год журнала: 2025, Номер unknown, С. 100184 - 100184

Опубликована: Фев. 1, 2025

Язык: Английский

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