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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
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Article Submitted : 21686
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Total Authors : 22487
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| Paper Title: | Applying SDTM/CDISC Standards for Automated Regulatory Compliance in FDA Submissions |
| Authors Name: | Naresh Koribilli |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_204313
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| Published Paper Id: | IJRTI2505298 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i5.204313 |
| Abstract: | In the evolving landscape of clinical research and regulatory affairs, the application of artificial intelligence (AI) to automate Study Data Tabulation Model (SDTM) compliance, as mandated by the Clinical Data Interchange Standards Consortium (CDISC) and required by the U.S. Food and Drug Administration (FDA), is becoming increasingly critical. This review explores the methodologies, tools, and models employed to automate SDTM mapping and validation processes. By evaluating rule-based engines, machine learning (ML) algorithms, and deep learning techniques, the study provides a comprehensive analysis of their effectiveness, accuracy, and compliance with regulatory standards. The paper also presents a theoretical model, experimental results, and industry case studies. Challenges such as data heterogeneity, lack of transparency, and auditability are discussed alongside strategic solutions. Future directions highlight the importance of explainable AI, interoperability, and regulatory acceptance of AI-assisted data standardization. This work aims to guide researchers, developers, and regulatory professionals in optimizing AI applications for SDTM/CDISC compliance. |
| Keywords: | Study Data Tabulation Model (SDTM), Clinical Data Interchange Standards Consortium (CDISC), Artificial Intelligence (AI), Machine Learning (ML), Regulatory Compliance, FDA Submissions,Data Standardization, Explainable AI, Clinical Data Automation, Deep Learning |
| Cite Article: | "Applying SDTM/CDISC Standards for Automated Regulatory Compliance in FDA Submissions", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c844-c853, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505298.pdf |
| Downloads: | 000504 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2505298
Registration ID:204313
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i5.204313
Page No: c844-c853 Country: Hayward, California, United States Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505298 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505298 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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