European Hospital Accelerate AI Medical Innovations with KAYTUS MotusAI Empowering Breakthroughs in Healthcare Computing Challenges

A Case Study on Scalable, Secure Clinical AI with KAYTUS MotusAI

As AI adoption in healthcare grows, hospitals must develop intelligent platforms that are efficient, secure, and collaborative. A leading general hospital in Europe partnered with KAYTUS to establish a high-performance AI platform to power diagnostic imaging, smart assistants, and LLM-driven clinical applications. With KAYTUS MotusAI, the hospital accelerated model development, improved GPU utilization, and maintained full compliance with stringent healthcare data privacy regulations.

Customer Overview

This leading European general hospital is at the forefront of AI-driven digital transformation. By integrating AI tools for medical imaging, triage, and patient interaction, the hospital aims to streamline clinical workflows and enhance patient outcomes as part of its strategy to become a next-generation smart hospital.

Project Background

With AI technologies advancing rapidly across healthcare, the hospital collaborated with AI vendors and HIS providers to co-develop applications tailored to real-world clinical needs. Use cases include large language models (LLMs) for discharge summaries, AI-assisted diagnostics in imaging and pathology, and LLM voice-assisted navigation for patients. Due to strict data privacy mandates, all development must remain on-premise—posing compute and storage challenges, especially for data-intensive workloads like imaging. Multiple stakeholders —from clinicians to AI developers—require a unified, secure, and efficient platform for coordinated role-based access.

Business Challenges

High Compute Demands, Limited Resources

• Numerous models with long training cycles overwhelm GPU resources

• Absence of centralized scheduling results in low efficiency and resource conflicts

Complex Multi-Party Collaboration

• Competing priorities from clinicians, vendors, and IT staff teams make resource allocation difficult

• Lack of dynamic allocation reduces development agility

Heavy Imaging Data Workload

• Slow data transfers hinder iteration speed

• Repeated reloading delays model training

Strict Data Privacy and Compliance

• All data must stay within hospital-controlled infrastructure

• Requires end-to-end encryption and role-based access control

KAYTUS Solution

To address these challenges, KAYTUS deployed MotusAI, a secure, AI-ready compute platform tailored for the healthcare sector.

Smart Resource Scheduling

• Unified pool for development and training zones

• On-demand elastic GPU allocation ensures highly efficient operations

• Checkpoint recovery of interrupted training sessions achieved over 90% effective GPU utilization

Efficient Data Handling

• Integrated with imaging systems and fine-grained access control

• Local caching and high-speed pipelines reduce data load times and shorten training cycles

End-to-End Secure AI Development

• Fully encrypted data transmission pipelines

• Role-based API access to ensure secure, compliant collaboration

• Seamless integration with HIS and hospital IT

Outcomes & Business Impact

• Faster AI Model Development: Training reduced from weeks to days

• Improved GPU Utilization: Resource efficiency reached 80%+

• Successful Deployments: AI triage, diagnostics, and doctor assistant tools now live

• Full Compliance: On-premises, encrypted infrastructure aligned with EU standards

• Smart Hospital Foundation: A scalable, secure platform for future healthcare AI