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This paper investigates the role of local knowledge specializations from universities in the artificial intelligence (AI) startup creation process. The empirical analysis is grounded in the Knowledge Spillovers Theory of Entrepreneurship. It provides evidence of the interplay between the presence of university knowledge specializations and the creation of AI startups located in Italian NUTS3 regions. More precisely, we analyze the mix of different types of university knowledge specializations able to foster the creation of AI startups, demonstrating that competencies in technological domains, such as computer science, need to be complemented with domains of applications for the emergence of AI technology. The findings indicate that the creation of AI startups is closely tied to the local knowledge present in the region. The article also proposes an empirical approach to identify and classify innovative startups operating in AI domains. Our work offers important implications for regional innovation policies and the management of AI in entrepreneurship. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the "AI for Medical Physics" (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare. [ABSTRACT FROM AUTHOR]