Optimizing Data Management: A Cornerstone for AI

Document Type : Original articles

Authors

1 Teaching Assistant of Oral and Maxillofacial Radiology, Faculty of Dentistry, Egyptian Russian University, Egypt

2 Assistant Lecturer of Oral & Maxillofacial Radiology, Faculty of Dentistry, Ain Shams University

3 Associate Professor of Oral & Maxillofacial Radiology, Faculty of Dentistry, Ain Shams University

4 Professor of Oral & Maxillofacial Radiology & Vice Dean for Community Service and Environmental Development Affairs, Faculty of Dentistry, Ain Shams University

Abstract

Aim: This study aimed to explore the challenges faced by postgraduate researchers ‎at Ain Shams University in accessing and managing patient data from the Oral and ‎Maxillofacial Radiology (OMFR) department, and to assess the potential impact of ‎implementing a specialized data management software. This can be generalized to ‎all OMFR workers in different locations.‎
Materials and Methods:‎ A descriptive survey was conducted among 235 postgraduate researchers ‎using Google Forms from August 5 to August 28, 2024. The survey comprised four ‎sections addressing participants' specialties, experiences with OMFR data, and ‎challenges in data collection. Descriptive statistics, including frequencies and ‎percentages, were used to analyze the data.‎
Results:‎ The majority of participants (83.3%) used CBCT imaging in their research, ‎yet over 65% reported moderate-to-high difficulty in collecting data, with key ‎challenges including tracking patient history (44.4%) and incomplete patient ‎information (33.3%). Concerns over data availability and accessibility deterred ‎‎41.3% of respondents from conducting retrospective studies.‎
Conclusions:‎ The findings highlighted the need for a dedicated data management system to ‎streamline research processes. Respondents valued easy access to patient data and ‎comprehensive datasets, including clinical and radiographic records, which are ‎essential for AI applications in dental diagnostics. Implementing such a system ‎would improve data standardization, enhance research efficiency, and support the ‎effective use of AI models in OMFR.‎
 

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