This paper presents an experimental study on the Segment Anything Model 2 (SAM2) for video and image analysis across diverse applications. We assess SAM2’s segmentation and object detection capabilities in medical imaging, road safety & traffic monitoring. Experimental results indicate SAM2’s high segmentation accuracy, computational efficiency, and robustness, especially in healthcare and road safety contexts. In healthcare, SAM2 initially demonstrated limited anatomical segmentation capabilities in heart-related surgical videos, requiring more user interactions than anticipated and often not meeting the precision standards of traditional models. However, after fine-tuning on a specialized medical dataset, it achieved its best performance to date. In road safety, SAM2 reliably identified key elements, such as emergency vehicles and potholes, under challenging traffic conditions. These findings underscore SAM2’s potential to streamline diverse applications, enhancing real-time analysis and operational simplicity. The paper concludes with insights into SAM2’s impact on video and image analysis workflows and directions for future research.