Assessment of Knowledge, Attitudes, and Practices in Artificial Intelligence Among Healthcare Professionals in Mogadishu, Somalia DOI Creative Commons
Mohamed Mustaf Ahmed, Najib Isse Dirie, Abdirahman Khalif Mohamud

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Май 14, 2024

Abstract Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly healthcare. Developed countries have invested significantly AI research and applications healthcare, while low-income such as Somalia lag due to challenges. This study aimed assess the knowledge, attitudes, practices (KAP) among healthcare professionals explore their familiarity with technologies practices. Methods: A cross-sectional was conducted from January 1, 2024, March 15, 441 Somalia, using an online questionnaire. questionnaire assessed participants' sociodemographic information, knowledge attitudes towards capabilities, practical experience Results: Most participants demonstrated good (67.6%) a positive attitude its potential (80.5%). However, significant gap observed application AI, 79.1% respondents reporting poor practice. also found that factors age, gender, income level did not influence or but affect use. Professionals certain fields midwifery public health are more likely use work. Knowledge scores were predictors practice scores. Conclusion: Healthcare demonstrate understanding encounter challenges application. emphasizes necessity enhanced infrastructure, technical expertise, data access fully utilize AI's It highlights significance addressing ethical considerations implementing regulations ensure responsible Efforts needed translate awareness receptiveness into effective practice, which could result better system.

Язык: Английский

Artificial Intelligence and Radiologist Burnout DOI Creative Commons
Hui Liu, Ning Ding, Minghao Yin

и другие.

JAMA Network Open, Год журнала: 2024, Номер 7(11), С. e2448714 - e2448714

Опубликована: Ноя. 22, 2024

IMPORTANCE Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI. OBJECTIVE To estimate AI use in radiology radiologist burnout. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study conducted questionnaire survey May October 2023, using national quality control system China. Participants included radiologists from 1143 hospitals. Radiologists reporting regular or consistent were categorized as group. Statistical analysis was performed 2023 to 2024. EXPOSURE practice. MAIN OUTCOMES MEASURES Burnout defined by emotional exhaustion (EE) depersonalization according Maslach Inventory. Workload assessed based on working hours, number image interpretations, hospital level, device type, role workflow. acceptance determined via latent class considering AI-related knowledge, attitude, confidence, intention. Propensity score–based mixed-effect generalized linear logistic regression used associations its components. Interactions use, workload, additive multiplicative scales. RESULTS Among 6726 this study, 2376 (35.3%) female 4350 (64.7%) male; median (IQR) age 41 (34-48) years; 3017 group (1134 [37.6%] female; [IQR] age, 40 [33-47] years) 3709 non-AI (1242 [33.5%] 42 [34-49] years). The weighted prevalence significantly higher compared (40.9% vs 38.6%; P < .001). After adjusting covariates, associated increased odds (odds ratio [OR], 1.20; 95% CI, 1.10-1.30), primarily driven EE (OR, 1.21; 1.10-1.34). A dose-response observed frequency ( trend more pronounced among high workload lower acceptance. significant negative interaction noted use. CONCLUSIONS RELEVANCE In burnout, frequent an risk particularly those Further longitudinal studies are needed provide evidence.

Язык: Английский

Процитировано

19
Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study DOI Creative Commons

Xiaoyan Wang,

Fangqin Fei, Jiawen Wei

и другие.

Frontiers in Public Health, Год журнала: 2024, Номер 12

Опубликована: Июль 2, 2024

The application of artificial intelligence (AI) in healthcare is an important public health issue. However, few studies have investigated the perceptions and attitudes professionals toward its applications nursing. This study aimed to explore knowledge, attitudes, concerns professionals, AI-related others China AI

Язык: Английский

Процитировано

14
Physician Preferences for an Electronic Lung Cancer Screening Decision Aid DOI Open Access

Orly Morgan,

Julie B. Schnur, Michael A. Diefenbach

и другие.

The American Journal of Managed Care, Год журнала: 2024, Номер 30(Spec. No. 6), С. SP445 - SP451

Опубликована: Май 30, 2024

To present primary care physician (PCP) suggestions for design and implementation of a decision aid (DA) tool to support patient-provider shared decision-making on lung cancer screening (LCS).

Язык: Английский

Процитировано

10
Nursing Students’ Personality Traits and Their Attitude toward Artificial Intelligence: A Multicenter Cross‐Sectional Study DOI Creative Commons
Gihan Mohamed Mohamed Salem, Heba Emad El‐Gazar,

Abeer Yahia Mahdy

и другие.

Journal of Nursing Management, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Background . Despite the importance of studying factors contributing to nursing students’ attitudes toward artificial intelligence, yet according our knowledge, no study has addressed relationship between personality traits and attitude students intelligence. Aim This aimed unveil whether are related their AI. Methods multicenter cross‐sectional included 218 from three governmental universities across various regions Kingdom Saudi Arabia. Data were gathered online, utilizing Big Five Inventory, General Attitudes Artificial Intelligence Scale, a demographic questionnaire. Descriptive statistics, Pearson’s correlation, regression analysis employed. The research complied with STROBE checklist. Results Findings indicated that high score in openness trait displayed positive Conversely, those who scored neuroticism agreeableness exhibited fewer intelligence more negative Additionally, ranked conscientiousness showed Conclusion Except for extraversion, appear predict Implications Nursing Management current provides foundation understanding how generative AI can be integrated into education practice manner is both effective considerate diverse psychological profiles students.

Язык: Английский

Процитировано

9
Machine Learning Operations in Health Care: A Scoping Review DOI Creative Commons

Anjali Rajagopal,

Shant Ayanian, Alexander J. Ryu

и другие.

Mayo Clinic Proceedings Digital Health, Год журнала: 2024, Номер 2(3), С. 421 - 437

Опубликована: Июль 14, 2024

Язык: Английский

Процитировано

6
The use of artificial intelligence in mental health services in Turkey: What do mental health professionals think? DOI Creative Commons
Mücahit GÜLTEKİN, Meryem ŞAHİN

Cyberpsychology Journal of Psychosocial Research on Cyberspace, Год журнала: 2024, Номер 18(1)

Опубликована: Фев. 1, 2024

Artificial intelligence (AI) supported applications have become increasingly prevalent in health care practice, with mental services being no exception. AI can be employed at various stages of and different roles. This study aims to understand the potential advantages disadvantages using services, explore its future roles, outcomes through opinions professionals engaged AI. Thus, we conducted a qualitative semi-structured interviews 13 who expertise AI, content analysis interview transcripts. We concluded that use revealed for clients, profession itself, experts. Our emphasized four findings. Firstly, participants were likely positive about services. Increased satisfaction, widespread availability reduced expert-driven problems, workload among primary advantages. Secondly, stated could not replace clinician but serve functional role as an assistant. However, thirdly, they skeptical notion would radically transform Lastly, expressed limited views on ethical legal issues surrounding data ownership, ‘black box’ problem, algorithmic bias, discrimination. Although our research has limitations, expect will play important

Язык: Английский

Процитировано

5
Navigating Privacy and Security in Telemedicine for Primary Care DOI Open Access

Katerina Andreadis,

Kimberly Muellers, Jenny J. Lin

и другие.

The American Journal of Managed Care, Год журнала: 2024, Номер 30(Spec. No. 6), С. SP459 - SP463

Опубликована: Май 30, 2024

Objective: To examine patient and provider perspectives on privacy security considerations in telemedicine during the COVID-19 pandemic. Study Design: Qualitative study with patients providers from primary care practices 3 National Patient-Centered Clinical Research Network sites New York, York; North Carolina; Florida. Methods: Semistructured interviews were conducted, audio recorded, transcribed verbatim, coded using an inductive process. Data related to information analyzed. Results: Sixty-five 21 participated. Patients faced technology-related concerns as well difficulties ensuring transformed shared space of telemedicine. expressed increased comfort doing home but often did not like their offer virtual visits outside office setting. Providers initially struggled find secure Health Insurance Portability Accountability Act–compliant platforms devices host software. Whereas some preferred familiar such FaceTime, others recognized potential concerns. Audio-only encounters sometimes raised that they would be able confirm identity provider. Conclusions: Telemedicine led novel about because at or public spaces, software hardware security. In addition technological safeguards, our emphasizes critical role physical infrastructure As continues evolve, it is important address mitigate around ensure high-quality safe delivery remote settings.

Язык: Английский

Процитировано

5
Assessment of the Knowledge, Attitude, and Practice of Artificial Intelligence (AI) Among Radiologists, Physicians, and Surgeons in Saudi Arabia DOI Open Access

Kholoud Sandougah,

Nasser Alsywina,

Hatan K Alghamdi

и другие.

Cureus, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Introduction Artificial intelligence (AI) is a broad term that refers to the idea and creation of computer systems capable carrying out tasks typically need human intelligence. Radiology, in particular, becoming increasingly interested quick development AI. There has been much discussion about possible impacts AI-based technologies on radiology. Aim This study aimed assess medical doctors' knowledge, attitudes, practice regarding applications AI radiology other fields Saudi Arabia. Subject methods cross-sectional was conducted among doctors A self-administered questionnaire distributed targeted through Commission Health Specialties an online survey using Google Forms. The comprised socio-demographic characteristics (i.e., age, gender, professional level, department unit) questionnaires attitude, toward field. Results Of 382 physicians, 56.8% were males 71.7% between 20 30 years old. Twenty-nine point six percent (29.6%) knew while only 12.3% able overall attitude physicians positive 26.2%. Increasing age non-resident associated with better knowledge attitudes application Lack awareness (41.6%) lack proper training recognized as most common reasons for reduced Conclusion Despite optimistic physicians' field deficient. Younger residents more likely exhibit unfavorable working Internal Medicine unit poor practices. innovating various ways. Hence, further, larger studies are required establish

Язык: Английский

Процитировано

0
Impacts of generative artificial intelligence on the future of labor market: a systematic review DOI Creative Commons
Nader Salari,

Mahan Beiromvand,

Amin Hosseinian‐Far

и другие.

Computers in Human Behavior Reports, Год журнала: 2025, Номер unknown, С. 100652 - 100652

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review DOI Creative Commons
Jade Basem, Racheed Mani,

Scott Sun

и другие.

Frontiers in Cardiovascular Medicine, Год журнала: 2025, Номер 12

Опубликована: Апрель 3, 2025

Neurocardiology is an evolving field focusing on the interplay between nervous system and cardiovascular that can be used to describe understand many pathologies. Acute ischemic stroke understood through this framework of interconnected, reciprocal relationship such occurs secondary cardiac pathology (the Heart-Brain axis), injury various neurological disease processes Brain-Heart axis). The timely assessment, diagnosis, subsequent management cerebrovascular diseases essential part bettering patient outcomes progression medicine. Artificial intelligence (AI) machine learning (ML) are robust areas research aid diagnostic accuracy clinical decision making better manage neurocardiology. In review, we identify some widely utilized upcoming AI/ML algorithms for most common sources stroke, strokes undetermined etiology, stroke. We found numerous highly accurate efficient products that, when integrated, provided improved efficacy prediction, identification, prognosis, within sphere focus cryptogenic strokes, there promising elucidating likely underlying causes thus, treatment options prevention. While still require a larger knowledge base or manual algorithmic training, in neurocardiology has potential provide more comprehensive healthcare treatment, increase access equitable healthcare, improve outcomes. Our review shows evident interest exciting new frontier with artificial learning.

Язык: Английский

Процитировано

0