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September 2, 2025

deepc unveils Complete Platform Offering for Clinical AI Research Organizations to Accelerate the Translation of AI Innovations from the Lab to the Bedside

The new offering within the deepcOS® AI infrastructure platform enables in-house AI management, deployment, validation, and scale across clinical workflows

  • deepc’s leading Enterprise AI infrastructure platform, deepcOS®, enables AI researchers to validate their solutions directly within real-world clinical workflows.
  • Shaped in collaboration with the AI Centre for Value-Based Healthcare, deepcOS®now enables health systems globally to deploy their in-house AI models for routine clinical use and improved patient care.
  • The platform offers researchers full access to deepcOS®, supporting flexible deployment, scalable hosting, and comprehensive lifecycle management from development to clinical integration.

02 September 2025; Munich, Germany: AI innovation in medical imaging is advancing rapidly (1), driven in part by the rise of foundation models that make development faster, more accessible, and cost-effective. Yet most research breakthroughs still never reach clinical practice. Many remain confined to academic environments, limited by infrastructure, high commercialization costs, complex integration requirements, and slow regulatory pathways (2,3). Meanwhile, commercial AI tools, though increasingly available, can underperform on local populations (4), may cover only a subset of clinical use cases, and can be costly to scale. To help health systems overcome these challenges, deepc has launched the deepcOS® Researcher Suite - a new offering within its enterprise AI infrastructure platform that enables leading hospitals and research institutions to manage, deploy, validate, and scale in-house AI applications within their clinical workflows.

End-to-End Infrastructure for In-House AI Teams

While thousands of AI models are developed each year in top institutions (5), most never move beyond research due to integration challenges. Even experienced teams struggle with infrastructure access, IT approvals, and manual data handling, which diverts time from development and validation.

Tailored to the needs of medical AI researchers, the deepcOS® Researcher Suite removes these barriers with a secure, flexible platform. It supports full AI lifecycle management with onboarding and packaging (including MONAI MAPs), data routing, validation, deployment, and integration, whether on local infrastructure or in the cloud. Models can be evaluated with structured clinical feedback, monitored through live dashboards, and integrated into existing systems like worklists, viewers, and reporting tools.

Designed for real-world use, the suite enables hospitals to assess AI not just by technical accuracy but by workflow fit and clinical value, while ensuring compliance through continuous monitoring at scale.

Co-Developed and Clinically Proven with NHS Partners

The Researcher Suite was developed in close collaboration with the AI Centre for Value-Based Care and clinical AI teams at globally leading health systems, including King’s College Hospital and Guy’s and St Thomas’ NHS Foundation Trust. Their insight helped shape a platform offering that matches the day-to-day needs of translational AI teams, supporting both iterative experimentation and full clinical rollout. This builds on deepc’s strategic alignment with the MONAI consortium, reinforcing its commitment to open-source collaboration and accelerating the path from research to real-world clinical impact.

Julia Moosbauer, CTO and co-founder of deepc, said:
“Our mission is to bring AI into the real world of clinical care, and that means supporting not just commercial vendors, but also the researchers developing high-impact, locally trained models. With the deepcOS® Researcher Suite, we're giving clinical AI teams access to the same AI infrastructure that already powers numerous enterprise deployments so they can move from prototype to patient impact more easily than ever before.”
Reflecting on their recent deployment, Anil Mistry, AI Safety Lead and Senior Clinical Scientist in Artificial Intelligence at Guy’s and St Thomas’ NHS Foundation Trust, said:
“Deploying our in-house AI models into clinical workflows has always been one of the most complex steps. With deepcOS, we were able to get up and running quickly using our own on-premise hardware GPU infrastructure. We’ve been impressed by deepc’s platform and their professional, hands-on support, which helped streamline deployment and testing. The flexibility of deepc’s AI infrastructure platform has been key in helping us move from development to clinical use with speed and confidence.”
Prof. Sebastien Ourselin, FREng FMedSci, Director of the AI Centre for Value-Based Care, and Assistant Principal (Innovation) at King’s College London, said: “The ability to translate research AI into clinical workflows without building custom infrastructure from scratch is a major step forward. The deepcOS Researcher Suite allows clinicians and researchers to focus on what matters most: evaluating and applying AI in ways that improve care for patients. This kind of platform-level support is essential if we want to see more evidence-based, locally developed AI solutions make it to the frontlines of radiology.”

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The deepcOS® Researcher Suite is available as part of deepcOS® Enterprise deployments. Interested institutions can contact deepc for integration and onboarding details.

References:

  1. Pesapane, F., Codari, M. & Sardanelli, F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2, 35 (2018).
  2. Tang X. The role of artificial intelligence in medical imaging research. BJR Open. 2019 Nov 28;2(1):20190031. doi: 10.1259/bjro.20190031. PMID: 33178962; PMCID: PMC7594889.
  3. Ravi K Samala et al., AI and machine learning in medical imaging: key points from development to translation, BJR|Artificial Intelligence, Volume 1, Issue 1, January 2024, ubae006.
  4. Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell. 2022 May 4;4(3):e210064. doi: 10.1148/ryai.210064. PMID: 35652114; PMCID: PMC9152694.
  5. Kocak, B., Baessler, B., Cuocolo, R. et al. Trends and statistics of artificial intelligence and radiomics research in Radiology, Nuclear Medicine, and Medical Imaging: bibliometric analysis. Eur Radiol 33, 7542–7555 (2023).

‍About deepc
deepc enables the infrastructure layer that powers safe, vendor-neutral AI in medical imaging. Our platform, deepcOS®, spans the full product lifecycle: discovery, clinical validation, deployment, monitoring, and governance, so hospitals can adopt, scale, and continuously improve the AI tools that matter most to their workflows. Through rigorous, large-scale testing on independent and local data sets, deepc certifies every integrated algorithm for performance, robustness, and regulatory compliance. Clinicians can then easily activate best-in-class solutions across more than 80 indications, confident that patient safety and data privacy are protected by design. deepcOS® installs quickly and interfaces seamlessly with existing PACS/RIS, cloud, or on-prem environments. By abstracting complexity and preserving choice, deepc empowers radiology departments to build and evolve an AI-driven practice—faster, safer, and on their own terms today and into the future.