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Background: The pharmaceutical industry operates within a complex regulatory environment, requiring strict compliance with global guidelines. Regulatory affairs (RA) departments are pivotal in ensuring drug approvals and compliance. However, the increasing complexity and volume of regulatory requirements have put a strain on traditional processes, driving the adoption of automation tools to streamline these operations.
Objective: This review aims to explore the key automation tools used in regulatory affairs, focusing on their role in streamlining submissions, ensuring compliance, centralizing data, and reducing human error. It also aims to examine the emerging technologies in the field and their potential for enhancing automation.
Methods: A comprehensive review of current automation tools in regulatory affairs was conducted. The key tools explored include Submission Management Systems (SMS), Regulatory Information Management (RIM) systems, Electronic Document Management Systems (EDMS), and Regulatory Intelligence Tools. Additionally, the role of emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) in automating regulatory processes was evaluated.
Results: Automation tools such as SMS, RIM, EDMS, and Regulatory Intelligence Tools have been found to significantly improve the efficiency of regulatory affairs operations. These tools streamline submissions, centralize data, and ensure compliance. AI and ML technologies further enhance automation by enabling predictive analytics and automating risk assessments. Despite the advantages, challenges remain, including high implementation costs, data security concerns, and the need to adapt to varying global regulations. However, overcoming the challenges and limitations associated with these technologies in adopting regulatory automation is crucial.
Discussion: This study highlights that automation tools are important for modernizing regulatory affairs by improving efficiency, accuracy, and compliance. The integration of Artificial Intelligence (AI) and Machine Learning (ML) adds predictive and adaptive capabilities, transforming static processes into dynamic systems. These technologies hold immense potential to reshape regulatory operations globally.
Conclusion: Automation tools are becoming essential in the pharmaceutical industry to maintain regulatory compliance, reduce time-to-market, and manage the increasing complexity of drug development in a globalized industry. As emerging technologies like AI, ML, and blockchain continue to evolve, they promise to further revolutionize regulatory affairs processes.
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http://dx.doi.org/10.2174/0115748871366461250802092217 | DOI Listing |
J Craniofac Surg
September 2025
Department of Breast Plastic Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shijingshan, Beijing, China.
Background: With the development of artificial intelligence, obtaining patient-centered medical information through large language models (LLMs) is crucial for patient education. However, existing digital resources in online health care have heterogeneous quality, and the reliability and readability of content generated by various AI models need to be evaluated to meet the needs of patients with different levels of cultural literacy.
Objective: This study aims to compare the accuracy and readability of different LLMs in providing medical information related to gynecomastia, and explore the most promising science education tools in practical clinical applications.
Biopreserv Biobank
September 2025
National Cancer Institute, NIH, Bethesda, Maryland, USA.
Biobankers rely on their experience, supplemented with a variety of tools, to help establish and sustain their operations. These tools support operations, cost determination, quality management, and governance. Costing tools have often been used to determine the economic value of a single specimen or an entire collection, with the purpose of allowing researchers to recover costs when providing access to those resources.
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August 2025
Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, LE3 9QP Leicester, UK.
Adult congenital heart disease (ACHD) constitutes a heterogeneous and expanding patient cohort with distinctive diagnostic and management challenges. Conventional detection methods are ineffective at reflecting lesion heterogeneity and the variability in risk profiles. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models, has revolutionized the potential for improving diagnosis, risk stratification, and personalized care across the ACHD spectrum.
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July 2025
L3S Research Center, Leibniz University Hannover, Hannover, Germany.
OpenML is an open-source platform that democratizes machine-learning evaluation by enabling anyone to share datasets in uniform standards, define precise machine-learning tasks, and automatically share detailed workflows and model evaluations. More than just a platform, OpenML fosters a collaborative ecosystem where scientists create new tools, launch initiatives, and establish standards to advance machine learning. Over the past decade, OpenML has inspired over 1,500 publications across diverse fields, from scientists releasing new datasets and benchmarking new models to educators teaching reproducible science.
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July 2025
Sandia National Laboratories, Albuquerque, NM, USA.
Pyomo is an open-source optimization modeling software that has undergone significant evolution since its inception in 2008. Pyomo has evolved to enhance flexibility, solver integration, and community engagement. Modern collaborative tools for open-source software have facilitated the development of new Pyomo functionality and improved our development process through automated testing and performance-tracking pipelines.
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