Segmed Blogs

Anonymized medical data sharing is necessary for the advancement of medicine, but it requires tremendous trust from all stakeholders. Patients need to trust that their healthcare providers are utilizing their data to advance innovations that will help them in the future. Healthcare providers such as health systems and hospitals must trust external stakeholders to use that data for actual good. Transparency allows for accountability, with data sharers and data users providing checks and balances. Radical transparency is at the heart of Segmed’s identity, which is why we formed an ethics committee. The committee will be a quarterly gathering of our data partners, discussing issues pertinent to medical data sharing, sharing their thoughts, and providing guidance on the way in which their data will be used. Our first committee meeting was centered around patient opt-in or opt-out consent for data sharing, with Aline Lutz (Medical Director), Akemi Leung (Lead Designer), and Jie Wu (Chief Da...

  The intersection of   artificial intelligence (AI) ,   real-world imaging data , and   robot-assisted surgery   is shaping the next frontier of healthcare innovation. With surgical robotics gaining traction in operating rooms worldwide, the integration of   AI models trained on high-quality, de-identified imaging datasets   has the potential to expand their capabilities beyond precision cutting and stitching. Instead, these systems are evolving into intelligent surgical assistants that enhance decision-making, improve patient outcomes, and accelerate innovation in medical devices. The Evolution of Surgical Robotics Surgical robotics was once seen as futuristic technology, but today, robotic systems are commonly used in procedures ranging from minimally invasive surgery to complex cardiac and neurological operations. While these systems have revolutionized precision, their true potential lies in pairing robotics with  AI-driven insights from imag...

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the medical device industry. These technologies are developing new methods to aid in the process of diagnosis, clinical decision-making, and the formation of treatment plans. AI and ML can identify anomalies and patterns to forecast patient outcomes from intricate data trends. They are making medical treatments more accurate and faster. As such algorithms evolve, they are being applied more and more in medical devices, enabling the devices to learn, adapt, and improve while being used. Medical device companies are applying these technologies to create innovative products that will better aid healthcare professionals and enhance patient care. One of the most significant advantages of AI/ML is the fact that it can learn from practical usage and experience, and improve its performance. Evolving FDA guidance on AI in medical devices The FDA also encourages AI and machine learning-based software as a medical device (...

The emergence of real-world imaging data (RWiD) is causing a rapid change in the innovation landscape of medical devices. The focus is on how RWiD demonstrates device performance, patient safety, and regulatory compliance, rather than on why device manufacturers are a critical component of healthcare. The process has undergone genuine changes as a result of its integration. In the past, limited imaging evidence and controlled clinical trials were used to evaluate medical devices. Real-world data (RWD) can include datasets beyond electronic health records (EHR), such as results of pathology tests, diagnostic imaging, genomic, and/or other “omic” data. Regarding RWiD, a robust dataset can be made accessible for novel clinical studies, allowing for early-stage analytics, feasibility assessments, and exploratory analyses. Digital imaging, advanced data management systems, and integration of AI/ML have facilitated widespread use of large collections of high-quality images linked to clinica...

It often feels like there’s no corner of healthcare that artificial intelligence (AI) hasn’t touched. Medical devices are no exception. The use of AI in medical devices originated back in the 1990s with imaging applications that were dependent on locked algorithms operating statically. We are now dealing with adaptive and evolutionary AI algorithms. These capabilities provide revolutionary unlocks in technology from personalised care to faster diagnostics and better decision-making in health services. Then, in the early 2000s, AI started to live up to its earlier promise. Healthcare workers were using AI to screen for diseases such as diabetic retinopathy and skin cancer with great precision. The US Food and Drug Administration (FDA) appreciates that AI and devices enabled by machine learning (ML) will revolutionise healthcare. Since 2020, the FDA has approved nearly 730 "new" AI-enabled devices; now we are observing how the field has evolved. As of June 25, 2024, there are 9...