Segmed Blogs

  Revolutionizing Radiology with Tokenized and De-Identified Imaging Data Radiology, or diagnostic imaging, consists of procedures that take and process images of different parts of the body. Radiology, in its various processes, offers the means to visualize internal organs. Imaging modalities employed in healthcare include X-rays, MRIs, ultrasounds, CT scans, mammography, nuclear medicine, fluoroscopy, bone mineral densitometry, and PET scans. Radiology plays an important role in the management of diseases, as it provides a variety of methods for the detection, staging, treatment planning, and monitoring of diseases. Radiology helps in the early detection of cancer, neurodegenerative, and cardiovascular diseases. And early detection further gives way to planning for early intervention and better outcomes. Imaging is also critical for planning surgery, radiation therapy, and other treatments since it gives accurate images of the targeted area.Additionally, radiology enables the mon...

  Standardized Medical Imaging Beyond DICOM: The Key to Interoperability Medical imaging, or radiological imaging, is one of the most vital aspects of modern medicine. It is important at multiple stages, from screening to diagnosing, to post-treatment monitoring. Advanced imaging technologies have allowed the enhanced visualization of internal structures, organs, and tissues with great detail. And with this greater ability, more precise and timely diagnosis of various diseases has become possible. This has significantly enhanced treatment planning and optimization of the care strategy, facilitating accurate monitoring and assessment of treatment effects. The availability of medical imaging data has also opened avenues for further research and innovation in medicine. There is a huge amount of data that is generated during medical imaging procedures in clinical practice. And this data is the foundation for clinical decision-making. On the research side, advanced methods such as Radio...

Real World Imaging Data-Driven AI Models for Expanding the Capabilities of Surgical Robotics Introduction Robotic surgery has changed the future of minimally invasive procedures. Traditional robotic systems helped surgeons perform delicate tasks with improved control and visibility. But these systems mostly depend on the surgeon’s skills and are mostly passive tools. These traditional surgical robotic systems operated on pre-programmed instructions. All these factors impede the adaptability and effectiveness of surgical robotic systems during procedures. The integration of artificial intelligence (AI) into surgical robotics brings a new level of intelligence to robotic systems. This integration allows robots to do more than simply follow commands. Machine learning models process large amounts of surgical data, and through that process, these systems learn patterns and are able to guide surgeons in real-time. These systems can detect structures, anticipate complications, and give sugges...