The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South.

The design of healthcare facilities must comply with applicable regulatory requirements, which significantly influence functional, spatial, and performance aspects of healthcare buildings. However, compliance checking is still predominantly conducted through manual procedures, leading to time-consuming workflows and a high risk of errors. In this context, Computational Compliance Checking (CCC) offers the potential to enhance the efficiency and reliability of regulatory verification processes. The adoption of Building Information Modelling (BIM), supported by the open standard Industry Foundation Classes (IFC), enables the formalisation of Information Delivery Specifications (IDS) as machine-interpretable rules for model validation. Additionally, the buildingSMART Data Dictionary (bSDD) contributes to the semantic structuring and classification of information requirements. This study explores how the IDS standard can enhance CCC processes by supporting the automated verification of regulatory requirements. Complementary solutions based on Python are proposed to address cases where IDS alone proves insufficient. A methodology for the semi-automated compliance checking of healthcare regulatory requirements is developed and applied to a case study based on regional regulations in the Veneto Region (Italy). Tested on the inpatient ward of a hospital BIM model, the procedure processed all 35 accreditation requirements, 91.4% through IDS and 8.6% through complementary Python routines, revealing several design non-conformities.