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Introduction A limitation to expanding laparoscopic simulation training programs is the scarcity of expert evaluators. In 2019, a new digital platform for remote and asynchronous laparoscopic simulation training was validated. Through this platform, 369 trainees have been trained in 14 institutions across Latin America, collecting 6729 videos of laparoscopic training exercises. The use of artificial intelligence (AI) has recently emerged in surgical simulation, showing usefulness in training assessment, virtual reality scenarios, and laparoscopic virtual reality simulation. An AI algorithm to assess basic laparoscopic simulation training exercises was developed. This study aimed to analyze the agreement between this AI algorithm and expert evaluators in assessing basic laparoscopic-simulated training exercises. Methods The AI algorithm was trained using 400-bean drop (BD) and 480-peg transfer (PT) videos and tested using 64-BD and 43-PT randomly selected videos, not previously used to train the algorithm. The agreement between AI and expert evaluators from the digital platform (EE) was then analyzed. The exercises being assessed involve using laparoscopic graspers to move objects across an acrylic board without dropping any objects in a determined time (BD < 24 s, PT < 55 s). The AI algorithm can detect object movement, identify if objects have fallen, track grasper clamps location, and measure exercise time. Cohen’s Kappa test was used to evaluate the agreement between AI assessments and those performed by EE, using a pass/fail nomenclature based on the time to complete the exercise. Results After the algorithm was trained, 79.69% and 93.02% agreement were observed in BD and PT, respectively. The Kappa coefficients test observed for BD and PT were 0.59 (moderate agreement) and 0.86 (almost perfect agreement), respectively. Conclusion This first approach of AI use in basic laparoscopic skills simulated training assessment shows promising results, providing a preliminary framework to expand the use of AI to other basic laparoscopic skills exercises. Supplementary Information The online version contains supplementary material available at 10.1007/s00464-022-09576-1.

BACKGROUND: The COVID-19 pandemic has had a negative effect on surgical education in Latin America, decreasing residents’ surgical training and supervised clinical practice. AIMS: This study aimed to identify strategies that have been proposed or implemented to adapt surgical training and supervised clinical practice to COVID-19-related limitations in Latin America. METHOD: A literature review was performed between April and May 2021, divided into two searches. The first one sought to identify adaptation strategies in Latin America for surgical training and supervised clinical practice. The second one was carried out as a complement to identify methodologies proposed in the rest of the world. RESULTS: In the first search, 16 of 715 articles were selected. In the second one, 41 of 1,637 articles were selected. Adaptive strategies proposed in Latin America focused on videoconferencing and simulation. In the rest of the world, remote critical analysis of recorded/live surgeries, intrasurgical tele-mentoring, and surgery recording with postoperative feedback were suggested. CONCLUSIONS: Multiple adaptation strategies for surgical education during the COVID-19 pandemic have been proposed in Latin America and the rest of the world. There is an opportunity to implement new strategies in the long term for surgical training and supervised clinical practice, although more prospective studies are required to generate evidence-based recommendations.