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[DOCS] Health - further review
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# Health-and-Life-Sciences AI Suite (Preview)
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# Health and Life Sciences AI Suite (Preview)
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The **Health-and-Life-Sciences AI Suite** is a preview collection of healthcare-focused AI applications, libraries, and benchmarking tools to help developers build intelligent patient monitoring solutions faster.
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The **Health and Life Sciences AI Suite** is a preview collection of healthcare-focused AI applications, libraries, and benchmarking tools to help developers build intelligent patient monitoring solutions faster.
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The suite provides **multi-modal AI pipelines** accelerated with the **OpenVINO™ toolkit**, enabling high-performance deployment on **Intel® Core™ Ultra platforms** using **CPU, integrated GPU (iGPU), and NPU**.
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This suite organizes workflows tailored for healthcare and life sciences, with initial support for a **Multi-Modal Patient Monitoring** application. The Multi-Modal Patient Monitoring application demonstrates how multiple AI workloads can run **simultaneously on a single platform**, enabling consolidated patient monitoring.
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This suite organizes workflows tailored for healthcare and life sciences, with initial support for a **Multi-Modal Patient Monitoring** application. The Multi-Modal Patient Monitoring application demonstrates how multiple AI workloads can run **simultaneously on a single platform** to enable consolidated patient monitoring.
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For full setup, usage, and contribution guide, see [**Read the Detailed Documentation**](multi_modal_patient_monitoring/README.md).
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For full setup, usage, and contribution guide, see [**Read the Detailed Documentation**](./multi_modal_patient_monitoring/README.md).
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## Disclaimer
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This software is provided for **development and evaluation purposes only** and is **not intended for clinical or diagnostic use**.
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health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/README.md

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# Initial Application: Multi-Modal Patient Monitoring
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# Multi-Modal Patient Monitoring
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The Multi-Modal Patient Monitoring application helps medical AI developers and systems engineers at medical OEMs/ODMs (GE Healthcare, Philips, Mindray) evaluate Intel® Core™ Ultra processors for AI‑enabled patient monitoring. It demonstrates that you can run **multiple AI workloads concurrently on a single Intel‑powered edge device** without a discrete GPU.
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- [Get Started](./docs/user-guide/get-started.md): Follow step-by-step instructions to set up the application.
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- [System Requirements](./docs/user-guide/get-started/system-requirements.md): Check the hardware and software requirements for deploying the application.
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- [Run the application](./docs/user-guide/run-multi-modal-app.md): Run Multi-Modal Patient Monitoring application.
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## How It Works
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At a high level, the system is composed of several microservices that work together to ingest patient signals and video, run AI models on Intel hardware (CPU, GPU, and NPU), aggregate results, and expose them to a UI for clinicians.
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![System design](./docs/user-guide/_assets/system-design.png)
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For details, see [How it Works](./docs/user-guide/how-it-works.md).
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## Learn More
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For detailed information about system requirements, architecture, and how the application works, see the

health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/docs/user-guide/index.md

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- [How It Works](./how-it-works.md) – High-level architecture, service responsibilities, and
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data/control flows.
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> This application is provided for development and evaluation purposes only and is _not_ intended for clinical or diagnostic use.
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> **Disclaimer:** This application is provided for development and evaluation purposes only and is _not_
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intended for clinical or diagnostic use.
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<!--hide_directive
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:::{toctree}

health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/docs/user-guide/release-notes.md

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# Release Notes - Multi-modal Patient Monitoring
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# Release Notes
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## Version 1.0.0
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The initial feature set of the application is now available:
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- monitoring heart and respiratory rates
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- Heart and respiratory rate monitoring
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- Integration with medical devices
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- Pose estimation with joint tracking
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- ECG analysis with 12-lead classification

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