Co-evolution of data acquisition systems, datasets, and artificial intelligence technologies in surgery

Surgical Data Science (SDS) is a discipline aimed at improving the quality of surgical care through the acquisition, organization, analysis, and modeling of surgical data. Progress in SDS is driven by the co-evolution of three components: intraoperative data collection systems, the datasets used to train AI algorithms, and the algorithms themselves. This study proposes an original conceptual timeline of this co-evolution, spanning from the mid-2010s - the time when SDS became institutionalized as a discipline - to the present day. It identifies three stages: the era of proprietary integrated operating rooms (approx. mid-2010s–2019), characterized by closed data collection systems; the era of overcoming the limitations of proprietary systems (approx. 2019–2024); and the era of interoperability and ambient AI (approx. 2024–present). A parallel evolution is observed in the datasets generated at each of these stages—transitioning from monomodal and multichannel to large-scale multimodal datasets. It is shown that each stage was initially driven by data acquisition systems, which set the limits for synchronization quality, modality composition, and the datasets that could be generated, whereas the requirements of AI algorithms guided the architectural evolution of these systems. The literature search was conducted across PubMed, IEEE Xplore, arXiv, the proceedings of the MICCAI and CVPR conferences, manufacturers' official websites, and technical documentation. Out of 127 initially identified publications, 68 were included in the study following screening and full-text analysis, supplemented by 29 sources comprising technical documentation and manufacturers' official websites (totaling 97 sources). Key factors for future progress will include the adoption and implementation of medical device interoperability standards; the development of multimodal datasets, foundational AI models, and multi-agent architectures; and their integration into the operating room environment.

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