Optimizing Data Management: A Cornerstone for AI | ||||
| Ain Shams Dental Journal | ||||
| Volume 37, Issue 1, March 2025, Page 1-7 PDF (1.15 MB) | ||||
| Document Type: Original articles | ||||
| DOI: 10.21608/asdj.2025.336854.1645 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohammad Nabawy Tawfik   1; Esraa Ahmed Eid 2; Amr Essam Elramah 2; Sahar Mohamed Sameer 3; Walaa Mohammed Hamed 4
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| 1Teaching Assistant of Oral and Maxillofacial Radiology, Faculty of Dentistry, Egyptian Russian University, Egypt | ||||
| 2Assistant Lecturer of Oral & Maxillofacial Radiology, Faculty of Dentistry, Ain Shams University | ||||
| 3Associate Professor of Oral & Maxillofacial Radiology, Faculty of Dentistry, Ain Shams University | ||||
| 4Professor 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. | ||||
| Keywords | ||||
| Data Management; Information Storage and Retrieval; Artificial Intelligence; Surveys and Questionnaires; Medical Records | ||||
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