چکیده مقاله
This systematic review investigates how artificial intelligence AI algorithms can quantify the innovation potential of technology startups in biotechnology and clean energy sectors, addressing the critical need for objective evaluation tools in these high stakes fields We searched over million academic papers from the Semantic Scholar corpus, identifying studies that utilize AI based quantitative metrics for startup assessment In biotechnology, key metrics include degree centrality in co authorship and citation networks, indicating collaboration strength, patent data reflecting research and development openness and specialization, and web media relevance scores with correlations of A, to external innovation rankings In clean energy, metrics encompass hybrid AI human scores for team quality, market potential, and technical innovation Spearman's rank correlation of V , alongside environmental factors like carbon footprint and emissions reduction potential The review highlights diverse AI methodologies, including machine learning, natural language processing, patent analysis, and hybrid AI human frameworks However, the absence of standardized validation methods limits cross study comparability These findings offer valuable insights for investors, policymakers, and researchers aiming to enhance startup evaluation and foster innovation in biotechnology and clean energy sectors
کلیدواژهها
نویسندگان
Mohammad Bahrami
Msc student of business administration MBA , Department of Management, science and Technology, Amirkabir University of Technology, Tehran, Iran
شیوه ارجاع
Bahrami، Mohammad،1404،Using AI quantitative metrics algorithms to assess the innovation potential of technology startups in specific sectors such as biotechnology or clean energy،دوازدهمین کنفرانس بین المللی مدیریت، حسابداری، بانکداری و اقتصاد ایران،مشهد
ارائهشده در
مجموعه مقالات دوازدهمین کنفرانس بین المللی مدیریت، حسابداری، بانکداری و اقتصاد ایران25 مرداد 1404 · مشهد