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Healthcare & Life Sciences

Accelerated tools for genomics, drug discovery, medical imaging and medical devices: Parabricks, BioNeMo, the community MONAI framework, Holoscan for real-time device data and Nemotron models for digital health.

Technology profiles for this category are in research.

Overview

Healthcare and life sciences combine very different workloads: sequencing pipelines, protein and molecule models, imaging AI and real-time device software, each with its own rules on data and approval.

BioNeMo is NVIDIA's platform of models, libraries, datasets and NIM microservices for biology and drug discovery; its framework code is being consolidated into BioNeMo Recipes. Parabricks runs GPU versions of genomics tools such as BWA-MEM alignment and DeepVariant and is offered as a free public container on NGC. MONAI is an open-source, PyTorch-based imaging framework under Apache 2.0, maintained by a community that includes NVIDIA, NIH and King's College London; it is not an NVIDIA product. Holoscan SDK processes device sensor data in real time. Readers looking for Clara should note that NVIDIA's Clara web address now opens its healthcare and life sciences page.

None of these tools makes a product clinically approved. Validation, regulatory clearance and data governance remain the developer's job. Small labs may find hosted genomics services simpler than running Parabricks themselves.12345

Problems it addresses

  • Slow genome analysis1

    Parabricks runs GPU versions of alignment and variant-calling tools for whole-genome and exome data.

  • Large biological design spaces1

    BioNeMo provides models, libraries and datasets for biology and drug discovery.

  • Labeling medical images4

    MONAI Label provides server-side annotation with active learning.

  • Moving imaging models into clinical systems4

    MONAI Deploy packages trained models into clinical pipelines with DICOM and FHIR input and output.

  • Real-time device data1

    Holoscan processes raw sensor data for medical devices.

A typical workflow

  1. Set data rules

    Confirm consent, de-identification and governance for the images you will use.

  2. Label images4

    Annotate studies with MONAI Label and its active learning loop.

  3. Train the model4

    Train and evaluate with MONAI Core on GPUs.

  4. Package for clinical systems4

    Wrap the model with MONAI Deploy for DICOM and FHIR integration.

  5. Run on a device if needed6

    For device-side inference, process the sensor stream with Holoscan on IGX.

NVIDIA Solution Architect

Describe your project and get an explainable architecture.

Open the lab

Next steps

  1. Confirm data governance and consent for the datasets you plan to use.

  2. Run the Parabricks container on a sample genome and compare runtime with your current pipeline.

  3. Work through the MONAI tutorials if your project centers on imaging.

  4. Use the NVIDIA Solution Architect to outline a stack for your organization type.

Sources

  1. NVIDIA Healthcare and Life Sciences (opens in a new tab)NVIDIA · Vendor-reported
  2. NVIDIA BioNeMo Framework documentation (opens in a new tab)NVIDIA · Vendor-reported
  3. NVIDIA Parabricks documentation (opens in a new tab)NVIDIA · Vendor-reported
  4. Project MONAI (opens in a new tab)Project MONAI · Third-party reporting
  5. London AI Centre and NVIDIA launch MONAI, a new AI framework for healthcare (opens in a new tab)King's College London · Customer-reported
  6. NVIDIA IGX (opens in a new tab)NVIDIA · Vendor-reported

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