Using AI quantitative metrics algorithms to assess the innovation potential of technology startups in specific sectors such as biotechnology or clean energy

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.

کلیدواژه ها:

Artificial Intelligence ، Startup Innovation ، Biotechnology ، Innovation Potential ، Quantitative Metrics