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on Artificial Intelligence |
| By: | Carla Irissarry (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Thierry Burger-Helmchen (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | While artificial intelligence has successful and innovative applications in common medicine, could its application facilitate research on rare diseases? This study explores the application of artificial intelligence (AI) in orphan drug research, focusing on how AI can address three major barriers: high financial risk, development complexity, and low trialability. This paper begins with an overview of orphan drug development and AI applications, defining key concepts and providing a background on the regulatory framework of and AI's role in medical research. Next, it examines how AI can lower financial risks by streamlining drug discovery and development processes, analyzing complex data, and predicting outcomes to improve our understanding of rare diseases. This study then explores how AI can enhance clinical trials through simulations and virtual trials, compensating for the limited patient populations available for rare disease research. Finally, it discusses the broader implications of integrating AI in orphan drug development, emphasizing the potential for AI to accelerate drug discovery and improve treatment success rates, and highlights the need for ongoing innovation and regulatory support to maximize the benefits of AI-driven research in healthcare. Based on those results, we discuss the implications for traditional and AI-powered business in the drug industry. |
| Keywords: | AI, Research, Orphan drug, Rare disease, Drug discovery and industry, Business, Financial risk, Clinical and virtual trials, Health economics, Development, Treatment success |
| Date: | 2024–09–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:pseptp:hal-04990197 |
| By: | Kimihiko Izawa (Bank of Japan); Ikuo Kamei (Bank of Japan); Nao Shibata (Bank of Japan); Yusuke Takahashi (Bank of Japan); Shunichi Yoneyama (Bank of Japan) |
| Abstract: | This paper examines whether textual data analysis using Large Language Models (LLMs) can be applied to assessing economic activity and prices in light of the rapid development of LLMs in recent years. LLMs have advantages in that there are a wide range of models available for use without large initial costs and that these models, which have already acquired basic knowledge of language, can analyze any topic or text and are beginning to be used in economic analysis more widely, including those of central banks. This paper, as an example, attempts to use LLMs to analyze recent wage and price developments in Japan using comments from the Cabinet Office's Economy Watchers Survey. The results suggest that the cause of increasing selling prices is gradually shifting from raw material costs to labor costs. |
| Date: | 2025–03–24 |
| URL: | https://d.repec.org/n?u=RePEc:boj:bojrev:rev25e05 |
| By: | Wu, Qiang; Zhou, Peng (Cardiff Business School, Cardiff University) |
| Abstract: | Artificial intelligence (AI) is crucial in achieving the carbon peak and neutrality goals and mitigating climate change. Although previous studies have explored cross-sectional differences in corporate carbon emissions, temporal heterogeneities in firm lifecycles have been overlooked. Therefore, this study investigates the effect of AI adoption on carbon emission intensity over firm lifecycles and the micro-level mechanisms of this effect. This study examines panel data from Chinese listed companies (2010–2021) using a two-way fixed-effects model and the difference-in-differences method. The empirical results demonstrate that AI significantly reduces enterprises’ carbon emission intensity. However, this effect is mainly observed in growth-stage enterprises and not in decline-stage enterprises. The mechanism analysis reveals that AI primarily reduces enterprises’ carbon emission intensity by improving productivity and promoting innovation. The effect on productivity is particularly evident in growth-stage enterprises, whereas the effect on innovation is dominant in decline-stage enterprises. Heterogeneity tests indicate that the effect on state-owned enterprises, medium-sized enterprises, the manufacturing sector, heavily polluting industries, non-high-tech industries, and capital-intensive industries is more pronounced than that on other enterprises. These findings suggest that enterprises should actively adopt AI, and differentiated AI adoption strategies should be formulated based on the needs of enterprises at different lifecycle stages. |
| Keywords: | artificial intelligence; carbon emission intensity; firm lifecycle; productivity |
| JEL: | O31 O32 O33 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2025/9 |
| By: | Yijia Chen; Kangmin Wu; |
| Abstract: | Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Despite growing interest in the geography of AI, our understanding of how AI integrates into regional contexts remains limited. In response, we examine the integration of AI into regional technological domains in China and the United States using patent data. Theoretically, we develop a framework by introducing the concepts of intra- and extra-regional AI relatedness. Our findings reveal that the integration of AI into regional technological domains is positively associated with both intra-regional and extra-regional AI relatedness. Additionally, extra-regional AI relatedness can moderate the lack of intra-regional AI relatedness. |
| Keywords: | integration of artificial intelligence, intra-regional AI relatedness, extra-regional AI relatedness, regional technological domains, China, the United States |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2507 |