Sara Abdelbagi Ballal Mahala
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sara Abdelbagi Ballal Mahala has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Dalia Mohammed Osman Khalfallah
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Northern Border University, Arar, Saudi Arabia
1 shared publication - Elryah I Ali
Department of Medical Laboratory Technology, College of Applied Medical Sciences, Northern Border University, Arar, Saudi Arabia
1 shared publication - Farhana Shaheen
Assistant Professor, Department of Nursing, Mohi-ud-Din Islamic Medical University, Azad Kashmir, Pakistan
1 shared publication - Manal Satti Awad Elseid
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Northern Border University, Arar, Saudi Arabia
1 shared publication - Mervat Mohammed Mazhar Abdelgadir
College of Science, Department of Mathematics, Northern Border University, Arar, Saudi Arabia
1 shared publication - Ranya Mohammed Elmagzoub
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Northern Border University,Arar, 91431, Saudi Arabia
1 shared publication
Latest Publications
- Open Access
- 5 January 2025
- Published by Institute for Advanced Studies in Cuestiones de Fisioterapia
- Vol. 54 (1) , 683-704
NANOTECHNOLOGY MEETS AI AND IOT: REDEFINING DIAGNOSTIC TECHNIQUES AND MEDICAL TREATMENT PROTOCOLS
Background: Combined with nanotechnology, AI, and IoT, the field of healthcare is experiencingsignificant changes in the approaches that are used to diagnose diseases and determine furthertreatment plans. Therefore, the purpose of this research will be to establish the effects of thesetechnologies in the delivery of healthcare.Objective:To evaluate how healthcare professionals are aware and using nanotechnology, AI, and IoT indiagnostics and treatment processes and the key difficulties and perceived advantages of thosetechnologies; and to figure out the present state of implementation of nanotechnology, AI, and IoT inhealthcare.Methods:From this study, a quantitative, cross-sectional survey design was used. A quantitative surveymethod that adopts a set of standard questions was employed for healthcare practitioners, scholars,technologists, and clients. Qualitative and both quantitative research methods were employed wheredescriptive statistics and inferential statistics were used in analyzing the data. The normality of thedata was tested using the Shapiro-Wilk test, survey reliability was tested with a Cronbach alpha testwhile, and the model accuracy was tested using the Random Forest classifier.
- 5 January 2025
- Published by Institute for Advanced Studies in Cuestiones de Fisioterapia
- Vol. 54 (1) , 683-704
Open AccessNANOTECHNOLOGY MEETS AI AND IOT: REDEFINING DIAGNOSTIC TECHNIQUES AND MEDICAL TREATMENT PROTOCOLS
Background: Combined with nanotechnology, AI, and IoT, the field of healthcare is experiencingsignificant changes in the approaches that are used to diagnose diseases and determine furthertreatment plans. Therefore, the purpose of this research will be to establish the effects of thesetechnologies in the delivery of healthcare.Objective:To evaluate how healthcare professionals are aware and using nanotechnology, AI, and IoT indiagnostics and treatment processes and the key difficulties and perceived advantages of thosetechnologies; and to figure out the present state of implementation of nanotechnology, AI, and IoT inhealthcare.Methods:From this study, a quantitative, cross-sectional survey design was used. A quantitative surveymethod that adopts a set of standard questions was employed for healthcare practitioners, scholars,technologists, and clients. Qualitative and both quantitative research methods were employed wheredescriptive statistics and inferential statistics were used in analyzing the data. The normality of thedata was tested using the Shapiro-Wilk test, survey reliability was tested with a Cronbach alpha testwhile, and the model accuracy was tested using the Random Forest classifier.