Editorial: AI and robotics for the smart hospitals of the future

Healthcare systems worldwide are facing unprecedented challenges, especially after the pandemic that uncovered strong vulnerabilities. Workforce shortages, fragmented care including data and information silos, limited digital maturity, inefficient workflows and supply chain vulnerabilities are examples of problems that surfaced during and beyond the pandemic. In this context, digital transformation of healthcare is more urgent than ever before. Research breakthroughs in artificial intelligence and robotics are redefining how hospitals operate, enabling intelligent decision support, precision therapeutics, continuous patient monitoring and care at home, optimization of healthcare resources, automation of clinical workflows and documentation. It is high time now, that these innovative technologies have matured and proved to be robust enough, to be integrated and implemented in the real clinical context. This shift will bring us one step closer towards the realization of the “smart hospitals” vision – the next-generation, smart and connected healthcare systems of the future. Within this Special Collection we seek to situate digital health state-of-the-art, and especially AI and robotics technologies, within the clinical context, bringing together research that explores how these technologies can be responsibly designed, implemented, and scaled to transform hospital practice and outcomes.

This special issue brings together papers that address digital health from a variety of perspectives, ranging from foundational methods for structured knowledge and communication,^1,2 to platforms and infrastructures that support the evaluation and operational deployment of AI in clinical practice,^3,4 to AI-enabled care delivery models designed to alleviate workforce pressures and improve access to care^5,6 and finally to personalized, patient-centered digital health systems that leverage multimodal data for continuous monitoring, engagement, and proactive intervention.^7,8

Lo et al. ¹ address the challenge of heterogeneous and often opaque medical imaging reports by proposing the Medical Image Interpretation Template (MIIT). By grounding automated report generation in established image interpretation theory and leveraging standardized metadata (i.e., DICOM, RADS, CAD features), the work demonstrates how structured representations can enhance understanding across clinical and patient-facing contexts. Complementing this, Benis et al. ² focus on semantic interoperability at a broader scale through the Medical Informatics Multilingual Ontology (MIMO). Through real-world case studies in smart hospital innovation and digital health education, it illustrates how multilingual ontologies enable shared understanding, efficient collaboration, and improved access to high-quality resources in international and multidisciplinary environments.

Apostolidis et al. ³ introduce a web-based platform designed to support rigorous multi-reader multi-case clinical studies for AI systems in video capsule endoscopy. The platform gives emphasis on usability, remote access, and multicenter collaboration, and it responds directly to the methodological demands of validating AI in real clinical settings. Chavez et al., ⁴ in contrast, address a critical operational bottleneck in oncology by proposing an optimization algorithm for radiotherapy scheduling. By dynamically reallocating appointments to accommodate urgent cases and integrating patient feedback via a mobile application, this work demonstrates how mathematical optimization can improve efficiency, flexibility, and satisfaction in resource-constrained hospital environments.

Arioz et al. ⁵ present the development of a social robotic nurse through a Living Lab methodology, emphasizing participatory design, stakeholder engagement, and ethical considerations. The results highlight both technical feasibility and strong user acceptance, positioning social robotics as a viable response to workforce shortages and increasing care demands. Busch et al. ⁶ shift the focus to population health and primary care, reviewing the integration of large language models with telehealth for HIV management in Indonesia. While identifying gaps in the current evidence base, the study proposes a structured and ethical blueprint for LLM-enabled telehealth, arguing that its potential benefits in triage, patient education, and care efficiency outweigh current limitations.

Choi et al. ⁷ introduce IVORY, an AI-powered mobile platform for managing personal health records in children with developmental disorders. By combining OCR-based document digitization with AI-driven interpretation and recommendations, the platform supports scalable, data-driven personalization, with positive usability evaluations from both caregivers and professionals. Manias et al. ⁸ extend this patient-centered paradigm to oncology, presenting a personalized monitoring system for early risk mitigation in pancreatic cancer. By integrating clinical, behavioral, wearable, and self-reported data into Holistic Health Records and enabling dynamic intervention planning, the system demonstrates improved adherence and data quality, underscoring the value of bidirectional engagement between patients and healthcare professionals.

In this collection several common themes emerge that collectively demonstrate how artificial intelligence and robotics are becoming an integral part of the routine care and transforming the way healthcare is practiced in everyday life. Intelligent systems, including telehealth, remote patient monitoring, patient mhealth tools and socially assistive robotics enhance healthcare capabilities beyond current practices standards. A number of studies address technology enablers towards the adoption of such tools, such as interoperability and common clinical data standards and shared knowledge models, while others emphasize implementation enablers such as participatory design, usability and real-world deployment. Together, all these applied research define smart hospitals as human-centered, data-driven, interconnected ecosystems in which artificial intelligence and robotics augment capacities of healthcare teams, empower patients to become more active in their care, improve the quality of healthcare services and ultimate contributing to the resilience of healthcare systems worldwide.