Ardito Vittoria, Cappellaro Giulia, Compagni Amelia, Petracca Francesco, Preti Luigi M
Center for Research on Health and Social Care Management (CERGAS), Government, Health, and Not-for-Profit (GHNP) Area, SDA Bocconi School of Management, Milan, Italy.
Department of Social and Political Sciences, Bocconi University, Milan, Italy.
Digit Health. 2025 Jul 10;11:20552076251355680. doi: 10.1177/20552076251355680. eCollection 2025 Jan-Dec.
Artificial intelligence (AI) offers transformative potential in healthcare, yet its adoption is hindered by cultural, organizational, and technological barriers, and little is known about their actual use in clinical practice. The aim of this study was to explore current trends in the adoption of AI applications across healthcare organizations in Lombardy, Italy.
This is a survey study that targeted public and private healthcare organizations in Lombardy and conducted between December 2023 and February 2024, with follow-ups between May and June 2024. It included three sections with up to 22 questions: mapping of clinical AI applications, organizational governance of AI, and perceived adoption barriers.
Among the 46 responding organizations, 56 AI applications were identified. Most applications focused on analyzing images or structured health data, and supported diagnostic, prognostic, or treatment optimization activities. Routinely used applications were Conformité Européenne-marked, with radiology being the main clinical area of use. Three distinct approaches emerged. While most organizations (57%) have not yet adopted AI applications, among adopters, 13% are developing AI tools, while 30% exclusively purchase commercial solutions.
There is considerable variability in both the types and stages of AI applications adopted in clinical practice by healthcare organizations in Lombardy. In terms of functions, most implementations support diagnostic and prognostic tasks, with strong emphasis on imaging-based tools. Regarding innovation strategies, varying approaches, ranging from exclusively purchasing AI applications to hybrid models that include in-house development, were observed. These findings support broader ecosystem efforts to understand and guide AI implementation in healthcare.
人工智能（AI）在医疗保健领域具有变革潜力，但其应用受到文化、组织和技术障碍的阻碍，人们对其在临床实践中的实际使用情况知之甚少。本研究的目的是探索意大利伦巴第地区各医疗保健机构采用人工智能应用的当前趋势。
这是一项针对伦巴第地区公共和私立医疗保健机构的调查研究，于2023年12月至2024年2月进行，并在2024年5月至6月进行了随访。它包括三个部分，最多22个问题：临床人工智能应用的映射、人工智能的组织治理以及感知到的采用障碍。
在46个回应机构中，识别出了56种人工智能应用。大多数应用集中于分析图像或结构化健康数据，并支持诊断、预后或治疗优化活动。常规使用的应用具有欧洲合格认证标志，放射学是主要的临床使用领域。出现了三种不同的方法。虽然大多数机构（57%）尚未采用人工智能应用，但在采用者中，13%正在开发人工智能工具，而30%只购买商业解决方案。
伦巴第地区医疗保健机构在临床实践中采用的人工智能应用的类型和阶段存在很大差异。在功能方面，大多数应用支持诊断和预后任务，特别强调基于成像的工具。在创新策略方面，观察到了不同的方法，从只购买人工智能应用到包括内部开发的混合模式。这些发现支持了更广泛的生态系统努力，以理解和指导医疗保健中的人工智能实施。
