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A. Fuster-Matanzo

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Review Open access Jul 2026

Technological innovation in healthcare: challenges, opportunities and impact on sustainability

Healthcare technologies are increasingly reshaping how diseases are detected, monitored, treated, and managed across healthcare systems. Advances in artificial intelligence (AI), digital health, remote monitoring, advanced medical devices, and data-driven clinical infrastructures are creating important opportunities to improve prevention, diagnostic accuracy, personalisation of care, workflow efficiency, and long-term healthcare sustainability within the framework of predictive, preventive, personalized, and participatory medicine (4P Medicine). However, despite growing technological sophistication and investment, many innovations fail to achieve scalable and sustainable implementation in real-world clinical environments. This narrative review critically examines the systemic factors that condition the successful translation of healthcare technologies into routine clinical practice, with particular emphasis on the European and Spanish contexts. Rather than focusing exclusively on technological performance, the review analyses the broader regulatory, organisational, financial, ethical, and governance challenges that shape implementation. Key areas discussed include technology transfer, regulatory frameworks, health data governance, and the organisational challenges associated with implementing AI-driven healthcare technologies. The central argument of this review is that the real-world impact of healthcare innovation depends less on technological capability itself than on the capacity of healthcare systems to support validation, regulation, implementation, workforce adaptation, interoperability, and long-term governance. Consequently, the principal challenge for contemporary healthcare systems is no longer simply how to develop new technologies, but how to integrate them safely, equitably, and sustainably into routine clinical practice.

María Vallet-Regí, M. Doblaré, J. A. Garrido et al. · 1 citation
Review Jul 2026

Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation

Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous. We conducted a PRISMAScR scoping review of peer-reviewed studies published between January 2017 and March 2026 describing foundation models trained exclusively on radiological imaging data. Sixty-seven studies were included and mapped across three pillars: data scale and heterogeneity, architectural and pretraining scalability, and downstream transferability and generalization. Datasets primarily covered brain MRI, thoracoabdominal CT, and chest X-ray, ranging from fewer than 100,000 samples to multi-million-image cohorts. Transformer-based architectures and self-supervised pretraining predominated, particularly masked image modeling, contrastive learning and multi-stage approaches. Evaluation focused mainly on segmentation and classification, whereas cross-center, cross-scanner, anatomical and modality-shift validation was inconsistently reported. Alignment with FUTURE-AI principles was uneven. Overall, radiology-specific VFMs show promising transferability, but clinical translation remains constrained by limited data representativeness, heterogeneous benchmarks, incomplete reporting and insufficient deployment-oriented evaluation.

A. Vergara-Richart, Xavier Rafael-Palou, A. Fuster-Matanzo et al. · 0 citations