Posts

Oligometastatic Disease in Real-World Radiology: The ECR 2026 Award-Winning Study That Reveals a Critical Gap

Image
A large-scale analysis of 33.7 million radiology reports uncovers how rarely radiologists independently document oligometastatic disease and why that matters for patient outcomes. At ECR 2026 , the European Congress of Radiology in Vienna, the world’s second-largest radiology congress, one of Segmed’s studies stood out . Among 11,376 submitted abstracts, an observational study through real-world data analysis of oligometastatic disease (OMD) documentation in routine U.S. radiology practice was recognized with the Best Research Presentation Abstract Award, on the topic of Oncologic Imaging. For a field still debating whether AI or structured reporting will close the gap between imaging interpretation and clinical decision-making, this study reframes the question entirely. The gap is not technological: it is linguistic, habitual, and measurable. And it is happening right now, in routine practice, across millions of reports. This blog breaks down what the study found, why it matters, and...
Post a Comment
Read more

Balancing Privacy and Progress When Sharing Real-World Imaging Data | Segmed Experts

Image
  Real‑World Imaging Data (RWiD) is increasingly being shared and used in research. It is valuable since it allows for new insights into patient care and outcome. But, it also comes with significant responsibilities. Its sensitivity necessitates strict security and privacy measures to safeguard patient information. When sharing multimodal RWiD, the main goal is to protect patient privacy, maintain data integrity, meet regulatory requirements, and promote the ethical use of health data to support care and advance medical research. The answer lies in striking the right balance, keeping privacy and compliance strong, while leaving room for innovation.‍ ‍Does Imaging Data Demand Additional Privacy Protection?‍ Imaging data includes standard protected health information (PHI) such as a patient’s name, date of birth, medical record number, and other identifiable details, but medical images also contain metadata which can include acquisition parameters, device identifiers, and embedded de...
Post a Comment
Location: United States
Read more

Real‑world validation still matters for imaging AI | Martin Willemink

Image
  Over the past few years, intracranial hemorrhage (ICH) detection has become one of the most mature and regulated use cases in clinical imaging AI. Multiple FDA‑cleared models are now clinically used in radiology workflows. However, recent evidence suggests that deployment maturity does not necessarily mean that models are clinically robust. Since ICH detection has been on the market for a while now, multiple studies on post-deployment evaluation are coming out. Two recent papers examining commercial ICH detection models illustrate both progress and persistent gaps. Together, they emphasize an important lesson for imaging AI research: performance claims are dependent on the data distributions on which models are trained and evaluated. Training an AI model on population A does not mean it will perform well in population B. Understanding where and why models fail is the foundation for the next generation of clinically meaningful AI. Real‑world performance of a commercial ICH model C...
Post a Comment
Location: United States
Read more