Randomized controlled trials (RCT) were impacted by the COVID-19 pandemic, but no systematic analysis has evaluated the overall impact of COVID-19 on non-COVID-19-related RCTs. The ClinicalTrials.gov database was queried in February 2020. Eligible studies included all randomized trials with a start date after 1 January 2010 and were active during the period from 1 January 2015 to 31 December 2020. The effect of the pandemic period on non-COVID-19 trials was determined by piece-wise regression models using 11 March 2020 as the start of the pandemic and by time series analysis (models fitted using 2015–2018 data and forecasted for 2019–2020). The study endpoints were early trial stoppage, normal trial completion, and trial activation. There were 161,377 non-COVID-19 trials analyzed. The number of active trials increased annually through 2019 but decreased in 2020. According to the piece-wise regression models, trial completion was not affected by the pandemic (p = 0.56) whereas trial stoppage increased (p = 0.001). There was a pronounced decrease in trial activation early during the pandemic (p < 0.001) which then recovered. The findings from the time series models were consistent comparing forecasted and observed results (trial completion p = 0.22; trial stoppage p < 0.01; trial activation, p = 0.01). During the pandemic, there was an increase in non-COVID-19 RCTs stoppage without changes in RCT completion. There was a sharp decline in new RCTs at the beginning of the pandemic, which later recovered.

Background Intraoperative artificial intelligence (AI) decision support systems hold promise for improving surgical outcomes, yet significant barriers impede their translation from development to clinical deployment. A systematic implementation framework informed by stakeholder perspectives is needed to guide responsible adoption in operating room (OR) settings. Methods We conducted a qualitative study using the Consolidated Framework for Implementation Research at a single North American academic institution. Phase I involved semi-structured interviews with OR personnel (surgeons, trainees, nurses, biomedical engineers) recruited through purposive maximum variation and snowball sampling until thematic saturation. Phase II comprised focus groups with patients recruited via convenience sampling. Interview and focus group transcripts underwent iterative thematic analysis using both deductive and inductive coding approaches. Results Twenty-two stakeholder interviews and two patient focus groups (n = 8) identified unique barriers and facilitators that coalesced into five major themes defining implementation requirements for intraoperative AI decision support: intuitive design, adequate training, maximizing adaptability, ongoing support, and fostering buy-in. These themes were contextualized across the surgical timeline: pre-implementation, implementation, and post-implementation phases, to create a comprehensive SMART-OR framework. Key barriers included overreliance concerns, automation bias risks, workflow disruption, team coordination challenges, and medico-legal ambiguity. Facilitators included perceived accuracy improvements, real-time guidance utility, and enhanced educational opportunities. Conclusions This study provides the first comprehensive implementation framework for intraoperative AI decision support, offering practical guidance across the technology lifecycle. The framework addresses critical gaps between AI development and clinical deployment by integrating diverse stakeholder perspectives into actionable recommendations. Future implementation efforts should prioritize transparent validation, coordinated training, and clear governance structures to ensure responsible adoption.