Imagine Health is at the forefront of medical technology innovation, combining the precision of AI, with the scalability and efficiency of robots, and using breakthroughs in multimodal diagnostics to deliver safe and autonomous precision in healthcare.
Our breakthrough system leverages computer vision to enable the robotic arm to deliver optimal imaging and multimodal data in a consistent, repeatable and safe manner without the need of experienced technicians.
Some medical tasks, like sonography, should be automated due to the high risk they pose to practitioners and the potential for human error. These roles involve repetitive tasks and physical strain, leading to musculoskeletal injuries. Manual processes can introduce variability and errors, which automation could significantly reduce, leading to more consistent and accurate outcomes. By automating these hazardous and error-prone tasks, we can shift medical expertise to other critical areas of care, allowing healthcare professionals to focus on complex, decision-driven aspects of patient treatment.
Unfilled Sonographer Roles
Occupational Injury Rate
Average Ultrasound Wait Time
Sonographers ready to quit
With over 150,000 publications on robotic ultrasound systems (RUSS) detailing advancements in teleoperated platforms, AI integration, and improved imaging techniques, why has this technology not yet become a practical, widespread reality in healthcare? Despite the considerable research and development, what are the remaining barriers—whether technical, economic, regulatory, or clinical—that continue to prevent RUSS from achieving large-scale adoption in hospitals and clinics, especially given its potential to revolutionize diagnostic imaging and improve access to care?
Achieving the delicate control required for ultrasound procedures is challenging. Robotic systems must replicate the nuanced, real-time adjustments that human sonographers make based on tactile feedback and visual cues. While machine learning has made strides, real-time interpretation of ultrasound images and decision-making during scans remains difficult to fully automate.
Ultrasound exams often involve dynamic and complex environments (e.g., moving organs, varying patient conditions). Adapting robotic systems to such variations requires advanced algorithms that can handle uncertainties in real time.
Ensuring that robots don’t cause discomfort or injury to patients while delivering consistent and high-quality images is a major challenge.