Segmed Blogs

  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...

  In the current era, the pharmaceutical industry has largely embraced the “Multimodal Dataset”. R&D teams are successfully integrating genomics, transcriptomics, and Electronic Health Records (EHR) to build longitudinal patient views. This shift toward integrated evidence is driven by the realization that biology is too complex for unimodal analysis. This has made multimodal datasets not just desirable, but essential. What makes multimodal pipelines the new gold standard is not volume alone, it is coherence. When data sources are systematically linked, time aligned, and governed under consistent quality and compliance frameworks, they enable answering research questions that were previously impossible to answer. Questions about patient heterogeneity, real world treatment effectiveness, and disease evolution across care pathways are now becoming accessible. However, a significant gap remains: medical imaging is the most data-rich phenotypic record and is still being treated as ...