Public articles linked to the same research event.
Research Square This paper proposes an evidence-informed conceptual framework that links multimodal clinical data (electronic health records, laboratory data, imaging, physiological monitoring, surgical video, device telemetry) through a modular AI architecture, domain adaptation, uncertainty estimation, and safety controls to a clinician-facing decision-support interface, explicitly separating the proposed architecture from evidence already reported in the literature and noting that clinical effectiveness of surgical AI remains to be established prospectively.
This paper proposes an evidence-informed conceptual framework that links multimodal clinical data (electronic health records, laboratory data, imaging, physiological monitoring, surgical video, device telemetry) through a modular AI architecture, domain adaptation, uncertainty estimation, and safety controls to a clinician-facing decision-support interface, explicitly separating the proposed architecture from evidence already reported in the literature and noting that clinical effectiveness of surgical AI remains to be established prospectively.
This paper proposes an evidence-informed conceptual framework that links multimodal clinical data (electronic health records, laboratory data, imaging, physiological monitoring, surgical video, device telemetry) through a modular AI architecture, domain adaptation, uncertainty estimation, and safety controls to a clinician-facing decision-support interface, explicitly separating the proposed architecture from evidence already reported in the literature and noting that clinical effectiveness of surgical AI remains to be established prospectively.
This paper proposes an evidence-informed conceptual framework that links multimodal clinical data (electronic health records, laboratory data, imaging, physiological monitoring, surgical video, device telemetry) through a modular AI architecture, domain adaptation, uncertainty estimation, and safety controls to a clinician-facing decision-support interface, explicitly separating the proposed architecture from evidence already reported in the literature and noting that clinical effectiveness of surgical AI remains to be established prospectively.