AIMC Topic: Health Personnel

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Perceptions of artificial intelligence in healthcare: a qualitative study among healthcare professionals in Jordan.

BMJ leader
PURPOSE: While there are studies on this topic, there may be a relative scarcity of research focusing on specific regions, such as Jordan. So, this study aims to gather insights from healthcare providers in Jordan concerning the advantages of integra...

Artificial intelligence and employee performance in Uganda's healthcare institutions: exploring the mediation effects of perceived ease of use and skills enhancement.

Journal of health organization and management
PURPOSE: The purpose of this study is to investigate the relationship between artificial intelligence (AI) and employee performance in Uganda's healthcare institutions, with a specific focus on exploring the mediating effects of perceived ease of use...

Factors Shaping Healthcare Professionals' Perceptions of AI in Saudi Arabia: A Cross-Sectional Study.

Studies in health technology and informatics
The successful adoption of artificial intelligence (AI) in healthcare relies on healthcare professionals' perceptions of its usefulness and their preparedness to integrate it into their practice. This study explores factors influencing these percepti...

CHRONIC CRITICAL ILLNESS IN BONE TRAUMA PATIENTS: AN AI-BASED APPROACH FOR INTENSIVE CARE UNIT HEALTHCARE PROVIDERS.

Shock (Augusta, Ga.)
Background: Chronic critical illness (CCI) is a serious condition characterized by a prolonged course of illness, resulting in elevated morbidity and mortality. CCI presents significant challenges for healthcare providers in intensive care units (ICU...

Assessing training needs and influencing factors among personnel at centers for disease control and prevention in northeast China: a cross-sectional study framed by SDT and TPB using machine learning techniques.

BMC public health
OBJECTIVES: Training public health personnel is crucial for enhancing the capacity of public health systems. However, existing research often falls short in providing a comprehensive theoretical framework and fails to account for the intricate interp...

Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.

Critical care explorations
IMPORTANCE: Sepsis is a major cause of morbidity and mortality, with early intervention shown to improve outcomes. Predictive modeling and artificial intelligence (AI) can aid in early sepsis recognition, but there remains a gap between algorithm dev...

Artificial intelligence (AI) use for personal protective equipment training, remediation, and education in health care.

American journal of infection control
BACKGROUND: Personal protective equipment (PPE) is a first-line transmission-based precaution for reducing the spread of nosocomial infections between health care workers (HCWs), patients, and staff. The COVID-19 pandemic highlighted a problematic sk...

Bridging the gap between scientists and clinicians: addressing collaboration challenges in clinical AI integration.

BMC anesthesiology
This article explores challenges for bridging the gap between scientists and healthcare professionals in artifical intelligence (AI) integration. It highlights barriers, the role of interdisciplinary research centers, and the importance of diversity,...

Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives.

Journal of medical ethics
BACKGROUND: There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race).