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Precision health depends on access to high-quality, research-ready data — yet fragmented systems, regulatory complexity, and limited imaging availability continue to slow progress. For pharma and biotech teams developing diagnostics and therapeutics, the gap between having data and being able to act on it remains a fundamental barrier. This session focuses on the infrastructure and partnerships that close that gap. By exploring how Verily's AI-native platform and Segmed's imaging assets work together, Denise demonstrates how an integrated ecosystem can support governed, scalable workflows, from dataset discovery to secure analysis, enabling teams to move faster from insight to impact. Whether you're developing AI models, running clinical trials, or evaluating data partnerships, this session offers a practical roadmap for accelerating precision health with real-world imaging data.

AI innovation in healthcare is accelerating, yet many promising algorithms remain confined to academic publications and research environments. The real challenge lies not in developing AI models, but in deploying them in ways that meaningfully improve patient care. This session focuses on the critical bridge between innovation and implementation. By exploring how AI technologies can generate credible evidence, integrate into clinical pathways, and deliver measurable outcomes, Dr. Harston provides a practical framework for bringing AI from the lab to the bedside. Through the example of Brainomix's stroke imaging platform, viewers will gain a clearer understanding of how AI solutions can move beyond proof-of-concept to achieve sustained adoption and real clinical impact.

Now Streaming: Segmed on YouTube We’re sharing conversations, insights, and real-world perspectives on how AI is shaping the future of healthcare, from medical imaging to clinical research. On our YouTube channel, you’ll find: • Bytes of Innovation sessions: expert discussions unpacking the latest in healthcare AI • Interviews with healthcare and AI leaders • Practical insights on real-world medical data • Highlights and recordings from scientific conferences and panels around the world

A model that scores 0.95 AUC at the institution where it was trained may not hold that performance at the hospital down the road. The reason is structural: single-center datasets encode institutional fingerprints (scanner-specific noise, local acquisition protocols, narrow referral patterns) that algorithms learn alongside the pathology they're supposed to detect. When those signals disappear in a new environment, so does performance. Multi-center validation isn't a regulatory checkbox. It's the mechanism that separates optimization from generalizability. Our latest piece breaks down what changes when imaging AI development moves from controlled single-center conditions to real-world, multi-site data and what we've learned working with globally distributed datasets at scale.

Imaging AI models are commonly developed using retrospective datasets that are carefully curated to optimize image quality by minimizing noise and variability. While these datasets are essential for early algorithm development, they rarely reflect how imaging is acquired and interpreted in routine clinical practice. In the real world, imaging data are heterogeneous by design. Differences in scanner manufacturers, reconstruction algorithms, acquisition protocols, and patient populations introduce variability that curated datasets often exclude. When models are validated only under homogeneous conditions, they risk learning dataset-specific shortcuts rather than clinically meaningful signals.

Artificial intelligence (AI) has demonstrated impressive performance across a range of medical imaging tasks, from lesion detection and segmentation to prognostication and treatment response assessment. However, most imaging AI systems are still trained and validated using limited and highly curated datasets. Such datasets are often drawn from a small number of institutions, scanners, and patient populations. While these datasets are essential throughout the algorithm development process, they rarely reflect the complexity, heterogeneity, and unpredictability of real-world clinical practice.

For more information visit: https://www.segmed.ai/

Precision health depends on accessible, unified data. This session explores how integrating real-world imaging and multimodal datasets enables secure analysis, accelerates research, and bridges critical data gaps in healthcare and life sciences innovation.

Multimodal data pipelines are transforming pharmaceutical research by integrating medical imaging, clinical records, and omics data into unified AI-ready datasets. These pipelines enable more accurate models, faster drug discovery, and deeper insights, helping researchers leverage real-world data to improve outcomes and accelerate innovation in healthcare.

Empowering healthcare innovation through trusted data sharing, this blog highlights how secure access to real-world data and medical imaging data accelerates research, enhances AI model development, and drives data-driven decision-making across clinical, regulatory, and patient care ecosystems.

For more information visit: https://www.segmed.ai/resources/events

For more information visit: https://www.segmed.ai/resources/events

🚀 Segmed at RSNA 2025: Unlocking the Power of Real-World Imaging Data 🚀 We’re excited to share that Segmed is heading to RSNA 2025, where we’ll spotlight how Real-World Imaging Data (RWiD) is transforming every stage of the AI lifecycle — from Foundation Model development all the way to regulatory submission readiness. This year, we’re proud to be featured in a AI Theather Session with Microsoft, diving into how high-quality, tokenized, de-identified imaging data accelerates model performance, safety, and clinical reliability. 🔗 Event details: https://www.segmed.ai/events/segmed-rsna-2025 🌐 Why RWiD Matters More Than Ever Healthcare Foundation Models (HFMs) are reshaping medical AI — but real-world deployment requires more than generalized training. Clinical imaging varies dramatically by modality, equipment, demographic factors, and context. Without robust fine-tuning, even strong HFMs risk: ⚠️ Missing subtle diagnoses ⚠️ Failing to generalize across populations ⚠️ Stalling on the path to clinical validation and regulatory clearance At Segmed, we bridge that gap with the industry’s most diverse, global imaging dataset: 📊 100M imaging studies 🏥 2,000+ healthcare sites 🌎 Across 5 continents 🔐 Fully tokenized, de-identified, regulatory-ready Fine-tuning with Segmed’s RWiD enables: ✅ Higher diagnostic accuracy ✅ Diverse patient cohorts ✅ Stronger safety and performance evidence for regulatory submissions ✅ Real-world reliability that gets AI from the lab into the industry Dive deeper into how RWiD elevates HFM development in our latest blog post: 👉 https://www.segmed.ai/resources/blog/fine-tuning-healthcare-foundation-models-with-rwid If you’re building the next generation of medical AI, let’s connect at RSNA. Segmed is here to streamline your journey from Foundation Models to regulatory-grade evidence — powered by Real-World Imaging Data.

Join Segmed at RSNA 2025 in Chicago from Nov 30–Dec 3 to explore how real-world imaging and multimodal data are accelerating AI innovation in life sciences, devices and healthcare. Visit Booth #5147 and attend our AI Theatre session in collaboration with Microsoft to learn best practices for preparing medical data for foundational models.

Segmed is heading to South Korea to attend the 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)! Jie W. Co-founder & CDO/CRO of Segmed, will be attending this year’s conference, joining the global community of researchers and innovators advancing the frontiers of medical imaging, AI, and computer-assisted technologies. At Segmed, we’re making it easier for teams to move from research to real-world impact. With access to over 100 million de-identified imaging studies, our fit-for-purpose Real-World Imaging Datasets (RWiD) and multimodal longitudinal datasets are fueling breakthroughs in algorithm development, validation, and clinical translation. If you’ll be at MICCAI 2025, Jie will be happy to connect. Reach out to schedule a meeting: https://www.segmed.ai/events/miccai2025 Let’s explore how high-quality imaging data can accelerate your work in medical image computing and beyond.

Join Segmed at the MICCAI 2025 Annual Meeting in Daejeon, South Korea (Sep 23-27) to explore Real-World Imaging Data (RWiD) that fuels AI innovation. We offer diverse, de-identified multimodal imaging datasets (radiology, reports, longitudinal data) backed by 100M+ studies and support for regulatory-grade evidence generation, biomarker discovery, generalization & trial optimization. Meet Dr. Jie Wu onsite & see how we can accelerate your medical imaging research.