AI创新与医疗器械行业企业绩效
AI Innovation and Firm Performance in the Medical Device Industry
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中文总结 AI 辅助
本研究利用FDA批准记录等数据，通过三阶段递归模型发现外部AI合作显著促进医疗器械企业引入AI设备并提升劳动生产率，且对小企业作用更大。
中文摘要 AI 辅助
人工智能是否能为将其嵌入产品的企业带来回报，这一问题难以确定，因为AI创新本身难以观察。医疗技术行业是一个罕见的例外：一款具备AI功能的设备在到达患者手中之前，必须获得国家卫生主管部门的批准，从而留下带有日期、可归属于企业的AI创新产出记录，该记录可以直接观察而非通过代理指标衡量。我们利用这一情境，采用一个三阶段递归模型，基于一个新颖的企业层面数据集进行估计，该数据集将FDA上市前批准、USPTO专利、Scopus出版物和Orbis财务数据关联起来，追踪从外部合作到AI设备引入再到企业绩效的完整创新链条。我们发现，外部AI研究合作是推动各规模企业和各估计方法下AI设备引入的稳健驱动因素，且对小企业的影响更大，这与外部知识联系替代有限内部研发能力的观点一致。按合作方类型分解后，该效应在产业界和临床合作中最大，在学术合作中最小，这与前者更接近监管和商业化过程相符。将AI设备推向市场的企业表现出更高的劳动生产率，这一效应对小企业和全样本均稳健，在序贯估计和联合极大似然估计下均成立，并随设备连续引入而累积。对利润率的影响存在但较弱，且并非在所有设定下都显著，这一模式与竞争性进入在AI设备在行业内扩散时侵蚀定价能力的观点一致。
英文摘要
Whether artificial intelligence pays off for the firms that build it into their products is hard to establish, because AI innovation is itself hard to observe. The medical technology sector is a rare exception: an AI-enabled device must obtain clearance from a national health authority before it can reach a patient, leaving a dated, firm-attributable record of AI innovation output that can be observed directly rather than proxied. We exploit this setting with a three-stage recursive model estimated on a novel firm-level dataset linking FDA premarket clearances, USPTO patents, Scopus publications, and Orbis financials, tracing the full innovation chain from external collaboration through AI device introduction to firm performance. We find that external AI research collaboration is a robust driver of AI device introduction across firm sizes and estimators, with a larger effect for small firms, consistent with external knowledge ties substituting for limited internal R&D capacity. Decomposing by partner type, the effect is largest for industry and clinical collaborations and smallest for academic ties, consistent with the former being closer to the regulatory and commercialisation process. Firms that bring AI devices to market display higher labour productivity, an effect robust for small firms and the full sample that holds under both sequential and joint maximum-likelihood estimation and accumulates across successive device introductions. Effects on profit margins are present but weaker and do not survive all specifications, a pattern consistent with competitive entry eroding pricing power as AI devices diffuse through the sector.
发表机构
- Université Sorbonne Paris Nord（索邦巴黎北大学）
- Université de Strasbourg（斯特拉斯堡大学）
- Université Paris 1 Panthéon-Sorbonne（巴黎第一先贤祠-索邦大学）
- Department of Cultures, Politics and Society, University of Turin（都灵大学）
- Collegio Carlo Alberto（卡洛阿尔贝托学院）
机构由 AI 辅助整理，请以论文原文为准。
