Segmed Blogs

Synthetic patient data is revolutionizing healthcare, empowering all of us to innovate, safeguard privacy, and unlock new research possibilities. As we push the boundaries in this transformation, we at Segmed are proud to be recognized as a frontrunner. According to CB Insights' recent ESP (Execution, Strength, and Positioning) ranking , we've been classified as a Leader – a true testament to our commitment to elevating the synthetic patient data platform market. In this blog, we’ll share what makes us an industry leader, how our capabilities in medical imaging data aggregation and de-identification are moving healthcare forward, and why our recognition in the CB Insights ranking signals a shift toward widespread adoption of synthetic data. The role of synthetic patient data in healthcare The need for secure, accessible, and pioneering patient datasets keeps growing – and for good reasons. Traditional data collection processes run up against challenges like privacy laws, limit...

1,000 Reasons to Celebrate Every so often in research, a project lands at just the right moment, addressing a challenge many people were quietly struggling with. For us at Segmed, that project was our 2020 paper: Preparing Medical Imaging Data for Machine Learning , published in Radiology. We didn’t set out to write a ' landmark paper '. We were simply trying to make sense of the chaos we were seeing firsthand: the inconsistent, fragmented, and inaccessible world of medical imaging data for AI. Now, five years later, that paper has been cited more than 1,000 times! A milestone we’re proud of, not just because of the number, but because of what it represents for healthcare AI and for Segmed. Why This Paper Exists and Why Segmed Does Too At the time, AI in medical imaging was gaining momentum, many AI startups got founded, and larger companies started developing AI models as well. But almost nobody was talking about the elephant in the room: preparing imaging data for AI is hard....

Introduction to robotic surgery and data in robotic surgery Modern surgery has been transformed with the advent of robotic surgery technology. Robotic surgery has facilitated procedures to be easier and safer, which were once considered complicated. Robotics has enabled physicians to use precision techniques, thus achieving improved patient outcomes. When performing such advanced techniques, healthcare providers can treat complicated areas effectively and minimize human errors. And now the introduction of real-world data, artificial intelligence (AI) algorithms, and predictive analytics is changing how surgeries are conducted. For instance, wearable devices can monitor patients' vital signs during surgery and follow their recovery thereafter. In the background, AI analyzes data to forecast possible complications, such that risks may be detected proactively and treatment plans can be modified accordingly. All such advancements are possible due to the availability of clinical data. D...

Healthcare is shifting from population-based approaches to individualized care. Precision medicine is a novel strategy for disease treatment and prevention that takes into account variability in individual genes, environment, and lifestyle. This system disposes of the "one size fits all" principle of medicine and works to provide patients with what they specifically need. This makes it possible for healthcare workers and researchers to tailor treatment and prevention interventions to each individual patient. In cancer care, precision medicine aims to provide the right cancer treatment to the right patient at the right dose and time. Because progression of cancer is fueled by certain genetic mutations, addressing these mutations enables drugs to be more targeted and individualized. Using the approach of precision medicine in oncology, researchers are able to identify individuals who might be at increased risk for cancer. This thus helps to diagnose and perform risk stratificat...

Artificial intelligence (AI) is revolutionizing the face of healthcare in meaningful ways. It is being used to improve the accuracy of diagnoses, streamline patient care planning, and enhance patterns in ongoing monitoring processes. Furthermore, AI can process large medical datasets to find latent patterns and information that clinicians can utilize to make well-informed decisions. AI in healthcare provides sophisticated problem-solving techniques beyond conventional human abilities, permitting a more subtle method towards interpretation and diagnosis, and enabling pioneering, personalized healthcare approaches. One area in healthcare AI gaining increasing traction is using Real-World Data (RWD) . Using RWD for the development and AI model training and validation is a prudent tool as it helps capture clinical details in real-world settings, improving accuracy and relevance. And now with growing access to newer real-world data sources like Real-World Imaging Data (RWiD) , integration ...