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Optimizing and Accelerating Medical Device Regulatory Approvals with AI and RWiD

Artificial intelligence (AI) and real-world imaging data (RWiD) are fundamentally reshaping and accelerating regulatory approval for medical devices. By delivering more effective, accurate, and transparent regulatory efficiency to a historically manual, slow-motion approach, AI and RWiD are fundamentally changing the regulatory landscape. AI gives the ability to automate functions, reliability, predict, and approve compliance in near real-time, ultimately shortening approval time and error rate. RWiD integrates all of this rich, real-world data into the regulatory review process, amplifying confidence in the safety and effectiveness of devices, while allowing for expedited access to the marketplace. Ultimately, the administrative functions of device development, or regulatory submissions, are transformed by the convergence of AI and RWiD. Market access, innovation, and patient outcomes are all measurably enhanced through monitoring of device performance, clinical utility, and automatic...
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Data Preparation for Medical Imaging AI Model Development

The introduction of AI (Artificial Intelligence) has invaded every industry, enhancing outcomes particularly in healthcare. AI in healthcare provides numerous benefits, the major one being precise diagnosis, which leads to better decision making. AI models in healthcare lead to better diagnosis and treatment plans, ultimately leading to better overall healthcare. One aspect of healthcare that contributes to a vast amount of data generation is medical imaging. And owing to huge volumes, it has encouraged professionals to embrace technologies like AI for the management of this data. AI also helps access the rich detail hidden within imaging data. Medical imaging data, when used to develop healthcare AI models, supports important steps like diagnosis and treatment planning. For these reasons, AI is coming forward as a leading solution, one that could transform medical research. For instance, AI diagnostics in breast cancer through mammography scans have led to earlier diagnosis. AI models...
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Location: United States
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Our Brand-New White Paper: Healthcare Innovation Depends on Data Sharing

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Healthcare innovation is at an inflection point. Across life sciences, digital health, and clinical care, innovation is accelerating. However, it is also increasingly constrained by one fundamental issue: access to high-quality, responsibly shared data. Today, Segmed is proud to launch our latest white paper, Healthcare Innovation Depends on Data Sharing , which explores why trusted data exchange is no longer optional, and what it will take to make it work at scale. Why This White Paper, and Why Now? Healthcare has moved from paper charts and fax machines to cloud platforms, interoperable EHRs, and AI-driven analytics . Yet despite this progress, much of the world’s most valuable clinical data (particularly rich, unstructured data like medical imaging) remains locked in silos. The white paper outlines how this fragmentation slows research, delays diagnosis, and limits the potential of AI, precision medicine, and population health. It argues that fast, secure, and meaningful data sharin...
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Location: United States
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Segmed at RSNA 2025 — Real-World Imaging Data for AI Innovation

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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...
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Location: United States
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