AIMC Topic: Jordan

Clear Filters Showing 1 to 10 of 19 articles

Understanding how medical students learn in the era of artificial intelligence: a mixed methods study.

BMC medical education
BACKGROUND: As medical education evolves, current teaching practices often remain misaligned with how today's digitally native students prefer to learn. While the use of digital tools is widespread, there is limited clarity on students' learning beha...

Integrating pollution indices, spatial interpolation, and machine learning for soil contamination analysis along the Zarqa River, Jordan.

Environmental monitoring and assessment
This study assesses soil contamination along the Zarqa River (ZR) in Jordan by integrating pollution indices, geostatistical interpolation, and machine learning models. We collected 34 soil samples from agricultural lands within the study area. Sampl...

Assessing ChatGPT adoption in Jordanian medical education: a UTAUT model approach.

BMC medical education
BACKGROUND: ChatGPT has shown significant promise in transforming medical education by streamlining research and improving teaching methods. However, its adoption in Middle Eastern medical education has remained underexplored. This study investigated...

Unveiling Disparities in Patient Rights Awareness and Practice: Insights From Artificial Neural Networks.

Journal of patient safety
BACKGROUND: High-quality universal health care coverage for all patients is the common theme in patient rights. However, pertinent investigations on this topic within the context of Jordanian health care are absent. This systematic review, coupled wi...

Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.

Parasites & vectors
BACKGROUND: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples. Howeve...

Awareness and Attitude Toward Artificial Intelligence Among Medical Students and Pathology Trainees: Survey Study.

JMIR medical education
BACKGROUND: Artificial intelligence (AI) is set to shape the future of medical practice. The perspective and understanding of medical students are critical for guiding the development of educational curricula and training.

Predicting hypoglycemia in ICU patients: a machine learning approach.

Expert review of endocrinology & metabolism
BACKGROUND: The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast the risk of hypoglycemia among ICU patients in Jordan.

Machine Learning-Based Mortality Prediction in Chronic Kidney Disease among Heart Failure Patients: Insights and Outcomes from the Jordanian Heart Failure Registry.

Medicina (Kaunas, Lithuania)
Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypert...

Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan.

BMC medical informatics and decision making
INTRODUCTION: The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, p...