Segmed Blogs

As the healthcare and artificial intelligence (AI) worlds converge ever more deeply, the upcoming   Radiological Society of North America (RSNA) 2025 conference (Nov 30 — Dec 3) in Chicago   becomes a major milestone. Among the key participants is Segmed, attending with a sharp focus on   real-world imaging data   and   multimodal datasets   as the backbone for next-generation AI solutions. At Booth #5147 in the AI Showcase Hall at McCormick Place, Segmed will demonstrate how it transforms raw clinical imaging and de-identified data into structured, ready-to-use cohorts for R&D, regulatory submission, and real-world evidence. This offering is particularly timely: as AI models grow more sophisticated, the data they rely on must grow in scale, diversity and structure. Why this matters now The performance of AI models — especially large foundation models applied to healthcare — depends not just on algorithmic sophistication, but on the  quality, diver...

The field of cancer management is evolving in important ways with the incorporation of medical imaging and real-world evidence (RWE). In breast cancer, which is the most common cancer in women, real-world data (RWD) provides novel perspectives that are driving important advancements. Using RWD to develop RWE has become an essential method of supplementing more traditional clinical trials. Real-world imaging data encompasses various imaging modalities, including mammography, MRI, and digital breast tomosynthesis. When combined with clinical and pathology data, this radiology data enhances patient care by promoting informed decision-making and patient-centered care.  This is of utmost use in oncology, where tumor heterogeneity and patient differences are paramount. Real-world imaging data enable more granular subgroup analysis and tracking of outcomes in populations typically excluded or underrepresented in clinical trials. Real-world studies have used imaging data, for example, to ...

Artificial intelligence (AI) is rapidly reshaping the arena of oncology research. AI can process massive amounts of complex data and generate relevant insights and improve workflows. In recent years, AI applications have extended well beyond genomic-based applications and now involve real-world imaging data (RWiD) , which have the potential to redefine how tumors are detected, characterized, and monitored through treatment. Real-world imaging applications are more sophisticated than previous systems that were based on claims or electronic health records because they reveal the dynamic nature of tumor biology as it relates to spatial heterogeneity, changes post-treatment, and patterns of progression.‍ Recent advances in AI have led to the development of tools that have the capability to analyze millions of digital pathology slides and radiology images to recognize subtle features in tumors and novel biomarkers automatically. For instance, DeepHRD, an AI-enabled model has been demonstra...

Historically, clinicians detected breast cancer through physical examinations or self-examination by patients who felt a lump or other breast abnormalities, with breast cancer diagnosed after their visual progression. By the early twentieth century, imaging technology started to focus more on breast cancer, starting with X-ray imaging and advancing through decades of imaging research into mammography, ultrasound, and MR imaging. Using medical imaging as a screening method, allowed identification of breast tumors before symptom onset. Although these developments improved survival, there were still significant challenges, such as false-positive and false-negative results, radiologist variability between imaging studies, and limitations for dense breast tissue. In the past few years, artificial intelligence (AI) and real-world imaging data (RWiD) have created a paradigm shift. AI models can currently evaluate mammograms and other breast images for subtle cancer-related changes, while real...

On July 4, the One Big Beautiful Bill Act (OBBBA) was signed into law, ushering in Medicaid reforms that present health systems with looming financial challenges. A  new analysis  from Kodiak Solutions projects hospitals could lose up to  $25 billion annually  in net revenue. For every hospital already operating on razor-thin margins, this is a significant challenge. But in times of disruption, opportunities emerge. The Financial Reality Hospitals Face Kodiak Solutions’ analysis modeled the impact of Medicaid disenrollment scenarios: 5% disenrollment  → $1M+ in annual net revenue losses 10% disenrollment  → $2M+ drop 15% disenrollment  → $3M+ drop 20% disenrollment  → nearly $4M drop Traditional levers – cost reduction, payer negotiations, or Medicaid enrollment outreach – won’t be enough to fill the widening gap. Hospitals need to think differently. A New Path: Monetizing Medical Imaging Data What if hospitals could transform an existing, underut...