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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v9i5.12395</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>The Application of Artificial Intelligence Technology in Assisting R&amp;D Project Initiation</title><url>https://artdesignp.com/journal/JERA/9/5/10.26689/jera.v9i5.12395</url><author>LiuZhenhuan</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>9</volume><issue>5</issue><history><date date-type="pub"><published-time>2025-10-21</published-time></date></history><abstract>This paper reviews the latest advancements in artificial intelligence-assisted R&amp;amp;D project initiation, aiming to provide intelligent solutions for R&amp;amp;D management. It thoroughly examines the value of artificial intelligence technologies in four core areas: intelligent requirement analysis, technical feasibility assessment, market prospect forecasting, and automated risk identification. Furthermore, it proposes three forward-looking trends—enhanced intelligence, the establishment of industry standards, and deeper human-machine collaboration. These insights are expected to improve project approval success rates and shorten initiation timelines, driving a paradigm shift in R&amp;amp;D management from experience-based to data-driven decision-making. The review highlights how artificial intelligence, through machine learning, natural language processing, and data mining, effectively addresses chronic challenges in traditional initiation processes such as inefficiency, delayed decisions, and resource misallocation. It also identifies critical hurdles, including data quality, model interpretability, and organizational transformation, offering a vital reference framework for the future of intelligent R&amp;amp;D development.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Hamamoto R, 2021, Application of Artificial Intelligence for Medical Research. Biomolecules, 11(1): 90.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Mooghal M, Anjum S, Khan W, et al., 2024, Artificial Intelligence-Powered Optimization of KI-67 Assessment in Breast Cancer: Enhancing Precision and Workflow Efficiency. A Literature Review. J Pak Med Assoc, 74(4 (Supple-4)): S109–S116.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Ding H, Tian J, Yu W, et al., 2023, The Application of Artificial Intelligence and Big Data in the Food Industry. Foods, 12(24): 4511.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Claudino JG, Capanema DO, de Souza TV, et al., 2019, Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: A Systematic Review. Sports Med Open, 5(1): 28.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Yu H, Liu S, Qin H, et al., 2024, Artificial Intelligence-Based Approaches for Traditional Fermented Alcoholic Beverages’ Development: Review and Prospect. Crit Rev Food Sci Nutr, 64(10): 2879–2889.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Mu W, Kleter GA, Bouzembrak Y, et al., 2024, Making Food Systems More Resilient to Food Safety Risks by Including Artificial Intelligence, Big Data, and Internet of Things into Food Safety Early Warning and Emerging Risk Identification Tools. Compr Rev Food Sci Food Saf, 23(1): e13296.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
