1. [Industries](/industries/overview) 2. Healthcare and Life Sciences # Developer Resources for Healthcare and Life Sciences A suite of computing platforms, software, and services that powers AI solutions for healthcare and life sciences, from imaging and instruments to genomics and drug discovery. Segments: - Digital Biology - Digital Health - Digital Devices Agent file: https://developer.nvidia.com/w/meta/healthcare/llms.txt ## NVIDIA AI Infrastructure The rows below are the raw result records for this component — one row per record. Column names are the source index fields; the full data is also downloadable in the links that follow. | title | ai_infra | featured | use_case | x_formats | document_url | technologies | document_date | short_summary | document_title | learning_level | x_content_types | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | FQ2BAM_Meth (BWA-Meth) | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_fq2bam_meth.html | Parabricks | 2026-03-10T04:00:00.000Z | Accurate alignment of bisulfite-treated DNA sequencing reads (BS-Seq) often used in methylation. | FQ2BAM_Meth (BWA-Meth) | Developer | Library & tool, Documentation | | HaplotypeCaller | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_haplotypecaller.html | Parabricks | 2026-03-10T04:00:00.000Z | Tool from Genome Analysis Toolkit (GATK) to detect germline/genetic variants. | HaplotypeCaller | Developer | Library & tool, Documentation | | Minimap2 | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_minimap2.html | Parabricks | 2026-03-10T04:00:00.000Z | Sequence alignment tool to align DNA or RNA sequencing reads to a reference genome. Often used for long-read technology from ONT or PacBio. | Minimap2 | Developer | Library & tool, Documentation | | MSA Search NIM | Deployment | false | Protein Structure Prediction | Hosted NIM/API, Documentation | https://build.nvidia.com/colabfold/msa-search | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for multiple sequence alignment generation. | MSA Search NIM | Developer | Deployment, Nim, Hosted nim/api | | OpenFold3 NIM | Deployment | false | Protein Structure Prediction | Hosted NIM/API, Documentation | https://build.nvidia.com/openfold/openfold3 | BioNeMo | 2026-01-20T00:00:00.000Z | Hosted build.nvidia NIM/API for protein/DNA/RNA/ligand structure prediction. | OpenFold3 NIM | Developer | Deployment, Nim, Hosted nim/api | | Giraffe | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_giraffe.html | Parabricks | 2026-03-10T04:00:00.000Z | Built by UCSC, a short-read mapping tool for pangenome graph alignment. | Giraffe | Developer | Library & tool, Documentation | | CodonFM | AI Models | true | Digital Biology | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/nv_codonfm_encodon | BioNeMo | 2026-02-20T00:00:00.000Z | Model weights/code for codon-level RNA modeling; not hosted NIM inference. | CodonFM | Developer | Model, Model weights | | DiffDock NIM | Deployment | false | Virtual Screening | Hosted NIM/API, Documentation | https://build.nvidia.com/mit/diffdock | BioNeMo | 2025-11-15T00:00:00.000Z | Hosted build.nvidia NIM/API for protein-ligand blind docking; use NIM docs. | DiffDock NIM | Developer | Deployment, Nim, Hosted nim/api | | Boltz-2 NIM | Deployment | false | Virtual Screening | Hosted NIM/API, Documentation | https://build.nvidia.com/mit/boltz2 | BioNeMo | 2026-02-01T00:00:00.000Z | Hosted build.nvidia NIM/API for biomolecular structure and affinity prediction. | Boltz-2 NIM | Developer | Deployment, Nim, Hosted nim/api | | maxToki | AI Models | false | Single-Cell Analysis | Model Weights, Code | https://huggingface.co/theodoris-lab/MaxToki | BioNeMo | 2026-06-05T00:00:00.000Z | Model weights/code for single-cell language modeling; not hosted NIM inference. | maxToki | Developer | Model, Understand, Model weights | | warpKAN | Libraries | false | Digital Biology | Code | https://github.com/NVIDIA-Digital-Bio/warpKAN | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for GPU-accelerated Kolmogorov-Arnold Networks with NVIDIA Warp. | warpKAN | Developer | Library & tool, Code | | AlphaFold2-Multimer NIM | Deployment | false | Protein Structure Prediction | Hosted NIM/API, Documentation | https://build.nvidia.com/deepmind/alphafold2-multimer | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for protein complex structure; container separate. | AlphaFold2-Multimer NIM | Developer | Deployment, Nim, Hosted nim/api | | La-Proteina | AI Models | true | Generative Design | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/laproteina_weights_data/artifacts | BioNeMo | 2026-01-28T00:00:00.000Z | Model weights/code for all-atom protein generation; not hosted NIM inference. | La-Proteina | Developer | Model, Model weights | | MolMIM NIM | Deployment | false | Molecular Design | Hosted NIM/API, Documentation | https://build.nvidia.com/nvidia/molmim-generate | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for molecule generation; docs define schema. | MolMIM NIM | Developer | Deployment, Nim, Hosted nim/api | | Parabricks | Libraries | false | Genomic Analysis | Documentation, Code | https://docs.nvidia.com/clara/parabricks/latest/ | BioNeMo | 2025-08-01T00:00:00.000Z | GPU genomics analysis docs; adjacent to BioNeMo, not a BioNeMo NIM. | Parabricks | Developer | Library & tool, Documentation | | cuik-molmaker | Libraries | false | Molecular Design | Code | https://github.com/NVIDIA-Digital-Bio/cuik-molmaker | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for molecular featurization for Chemprop-style GNN workflows. | cuik-molmaker | Developer | Library & tool, Code | | cuEquivariance | Libraries | false | AI Model Training | Code | https://github.com/NVIDIA/cuEquivariance | BioNeMo | 2025-11-10T00:00:00.000Z | Code route for optimized equivariant kernels in structure and molecular models. | cuEquivariance | Developer | Library & tool, Code | | nvMolKit | Libraries | false | Molecular Design | Code | https://github.com/NVIDIA-Digital-Bio/nvMolKit | BioNeMo | 2025-12-15T00:00:00.000Z | Code route for GPU cheminformatics: fingerprints, clustering, conformers, search. | nvMolKit | Developer | Library & tool, Code | | JEPA-DNA | AI Models | false | Genomic Analysis | Code | https://github.com/NVIDIA-Digital-Bio/JEPA-DNA | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for genomic foundation modeling with joint-embedding predictive objectives. | JEPA-DNA | Developer | Model, Understand, Code | | f-RAG | AI Models | false | Molecular Design | Code | https://github.com/NVlabs/f-RAG | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for fragment retrieval-augmented molecule generation and optimization. | f-RAG | Developer | Model, Design, Code | | BioNeMo Recipes Documentation | Training Tools | true | AI Model Training | | https://docs.nvidia.com/bionemo-recipes | BioNeMo | 2025-09-30T00:00:00.000Z | Model implementations, training recipes, and inference workflows for biological foundation models. | BioNeMo Recipes Documentation | | | | Generative Protein Binder Design Pipeline | Deployment | true | Protein Design | Blueprint, Code | https://build.nvidia.com/nvidia/protein-binder-design-for-drug-discovery | Blueprint | 2026-06-05T00:00:00.000Z | Blueprint workflow for de novo protein binder design; not a standalone NIM/API. | Generative Protein Binder Design Pipeline | Developer | Deployment, Blueprint | | Evo2 Protein Design Blueprint | Deployment | false | Protein Design | Blueprint | https://build.nvidia.com/nvidia/evo2-protein-design | Blueprint | 2026-06-05T00:00:00.000Z | Blueprint workflow around Evo2; use Evo2-40B NIM for hosted inference. | Evo2 Protein Design Blueprint | Developer | Deployment, Blueprint | | OpenFold2 NIM | Deployment | false | Protein Structure Prediction | Hosted NIM/API, Documentation | https://build.nvidia.com/openfold/openfold2 | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for protein structure from sequence, MSA, template. | OpenFold2 NIM | Developer | Deployment, Nim, Hosted nim/api | | Digital Biology Examples | Training Tools | false | AI Model Training | Code | https://github.com/NVIDIA/digital-biology-examples | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for Digital Biology examples that compose BioNeMo workflows. | Digital Biology Examples | Developer | Training, Code | | ProteinMPNN NIM | Deployment | false | Protein Design | Hosted NIM/API, Documentation | https://build.nvidia.com/ipd/proteinmpnn | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for protein sequence design; docs define schema. | ProteinMPNN NIM | Developer | Deployment, Nim, Hosted nim/api | | RNAPro | AI Models | false | Digital Biology | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/resources/rnapro | BioNeMo | 2025-12-01T00:00:00.000Z | Model weights/code for RNA 3D structure prediction with RNA foundation models. | RNAPro | Developer | Model, Understand, Model weights | | Evo2-40B NIM | Deployment | false | Digital Biology | Hosted NIM/API, Documentation | https://build.nvidia.com/arc/evo2-40b | BioNeMo | 2025-12-05T00:00:00.000Z | Hosted build.nvidia Evo2-40B NIM/API; use docs. Framework/Blueprint separate. | Evo2-40B NIM | Developer | Deployment, Nim, Hosted nim/api | | GenMol | Deployment | false | Molecular Design | Hosted NIM/API, Code | https://build.nvidia.com/nvidia/genmol-generate | BioNeMo | 2025-11-20T00:00:00.000Z | Molecule generation model with hosted NIM/API plus code and weights. | GenMol | Developer | Model, Design, Hosted nim/api | | GFMBench-API | Libraries | false | Genomic Analysis | Code | https://github.com/NVIDIA/GFMBench-api | BioNeMo | 2026-04-28T00:00:00.000Z | Code route for benchmarking genomic foundation models with standard tasks and metrics. | GFMBench-API | Developer | Library & tool, Code | | BioNeMo Recipes | Training Tools | false | AI Model Training | | https://github.com/NVIDIA-BioNeMo/bionemo-recipes/tree/main | BioNeMo | 2026-03-09T00:00:00.000Z | BioNeMo Recipes is a collection of reference implementations and step-by-step guides that demonstrate how to efficiently scale and optimize the training of biological foundation models using NVIDIA Transformer Engine and popular open-source frameworks. | BioNeMo Recipes | | | | Single Cell Analysis Blueprint | Deployment | false | Single-Cell Analysis | Blueprint, Code | https://build.nvidia.com/nvidia/single-cell-analysis | Blueprint | 2026-06-05T00:00:00.000Z | Blueprint workflow for GPU single-cell analysis; not a hosted NIM/API. | Single Cell Analysis Blueprint | Developer | Deployment, Blueprint, Code | | AlphaFold Database of Protein Complexes | Datasets | false | Protein Structure Prediction | Dataset | https://alphafold.ebi.ac.uk/ | BioNeMo | 2026-03-01T00:00:00.000Z | Dataset of predicted protein complexes for interaction biology and drug discovery. | AlphaFold Database of Protein Complexes | Developer | Data, Dataset | | Proteina-Complexa | AI Models | true | Protein Design | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/proteina_complexa | BioNeMo | 2026-03-15T00:00:00.000Z | Model weights/code route for protein binder design against protein or small-molecule targets. | Proteina-Complexa | Developer | Model, Model weights | | Genomics Analysis Blueprint | Deployment | false | Genomic Analysis | Blueprint, Code | https://build.nvidia.com/nvidia/genomics-analysis | Blueprint | 2026-06-05T00:00:00.000Z | Blueprint workflow for germline genomics analysis with Parabricks. | Genomics Analysis Blueprint | Developer | Deployment, Blueprint, Code | | Proteina | AI Models | false | Generative Design | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/proteina_weights_data | BioNeMo | 2025-12-10T00:00:00.000Z | Model weights/code for flow-based protein backbone generation with fold-class conditioning. | Proteina | Developer | Model, Design, Model weights | | ProtComposer | AI Models | false | Protein Structure Prediction | Model Weights, Code | https://github.com/NVlabs/protcomposer/tree/main/model_weights | BioNeMo | 2026-02-05T00:00:00.000Z | Model weights/code route for spatially conditioned protein structure generation. | ProtComposer | Developer | Model, Design, Model weights | | Megalodon | AI Models | false | Generative Design | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/megalodon | BioNeMo | 2025-10-15T00:00:00.000Z | Model weights/code route for 3D molecule generation with equivariant graph transformers. | Megalodon | Developer | Model, Design, Model weights | | AlphaFold3 NIM | Deployment | false | Protein Structure Prediction | Hosted NIM/API, Model card | https://build.nvidia.com/deepmind/alphafold3 | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for proteins, nucleic acids, ligands, and ions. | AlphaFold3 NIM | Developer | Deployment, Nim, Hosted nim/api | | VariantWorks | Libraries | false | Genomic Analysis | Code | https://github.com/NVIDIA/VariantWorks | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for deep-learning-based genomic variant calling. | VariantWorks | Developer | Library & tool, Code | | nvQSP | Libraries | false | Molecular Design | Code | https://github.com/NVIDIA-Digital-Bio/nvQSP | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for GPU QSP/PBPK ODE solvers for virtual patient simulations. | nvQSP | Developer | Library & tool, Code | | Consistency Distilled Synthetic Protein Database | Datasets | false | AI Model Training | Dataset | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/resources/proteina-atomistica_data?version=release | BioNeMo | 2025-12-20T00:00:00.000Z | Dataset of curated protein sequence-structure pairs for training and benchmarks. | Consistency Distilled Synthetic Protein Database | Developer | Data, Dataset | | ReaSyn | AI Models | false | Generative Design | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/resources/nv-reasyn-ar-166m-v1.ckpt?version=1.0 | BioNeMo | 2026-01-15T00:00:00.000Z | Model weights/code route for synthesis pathway prediction from building blocks to products. | ReaSyn | Developer | Model, Optimize, Model weights | | Context Parallelism (boltz-cp) | Training Tools | false | AI Model Training | Code | https://github.com/NVIDIA-Digital-Bio/boltz-cp | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for long-sequence parallelism in protein structure prediction models. | Context Parallelism (boltz-cp) | Developer | Training, Code | | DualBind | AI Models | false | Virtual Screening | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/resources/dualbind_v1_toxbench | BioNeMo | 2025-10-20T00:00:00.000Z | Model weights/code for protein-ligand affinity; scoped to HER-alpha. | DualBind | Developer | Model, Optimize, Model weights | | AlphaFold2 NIM | Deployment | false | Protein Structure Prediction | Hosted NIM/API, Documentation | https://build.nvidia.com/deepmind/alphafold2 | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for monomer protein structure; container separate. | AlphaFold2 NIM | Developer | Deployment, Nim, Hosted nim/api | | RFDiffusion NIM | Deployment | false | Protein Design | Hosted NIM/API, Documentation | https://build.nvidia.com/ipd/rfdiffusion | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for protein backbone/binder design; container separate. | RFDiffusion NIM | Developer | Deployment, Nim, Hosted nim/api | | AvgFlow | AI Models | false | Molecular Design | Model Weights, Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/avgflow | BioNeMo | 2026-01-10T00:00:00.000Z | Model weights/code for molecular 3D conformers; not hosted NIM inference. | AvgFlow | Developer | Model, Design, Model weights | | ESMFold NIM | Deployment | false | Protein Structure Prediction | Hosted NIM/API, Model card | https://build.nvidia.com/meta/esmfold | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for fast single-sequence protein structure. | ESMFold NIM | Developer | Deployment, Nim, Hosted nim/api | | ESM2-650M NIM | Deployment | false | Digital Biology | Hosted NIM/API, Model card | https://build.nvidia.com/meta/esm2-650m | BioNeMo | 2026-06-05T00:00:00.000Z | Hosted build.nvidia NIM/API for protein language-model embeddings. | ESM2-650M NIM | Developer | Deployment, Nim, Hosted nim/api | | KERMT | AI Models | false | Binding Energy | Code | https://github.com/NVIDIA-Digital-Bio/KERMT | BioNeMo | 2025-11-01T00:00:00.000Z | Code route for ADMET property prediction; fine-tune before inference. | KERMT | Developer | Model, Optimize, Code | | Pangenome Aware DeepVariant | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_pangenome_aware_deepvariant.html | Parabricks | 2026-03-10T04:00:00.000Z | Pangenome-aware DeepVariant is an enhanced version of Google's DeepVariant that leverages pangenome reference graphs (GBZ files) to improve variant calling accuracy, particularly in complex and highly variable genomic regions. | Pangenome Aware DeepVariant | Developer | Library & tool, Documentation | | Generative Virtual Screening Pipeline | Deployment | true | Virtual Screening | Blueprint, Code | https://build.nvidia.com/nvidia/generative-virtual-screening-for-drug-discovery | Blueprint | 2026-06-05T00:00:00.000Z | Blueprint workflow for molecule generation, docking, and optimization. | Generative Virtual Screening Pipeline | Developer | Deployment, Blueprint | | nvFLARE | Libraries | false | AI Model Training | Code, Documentation | https://github.com/NVIDIA/NVFlare | FLARE | 2026-03-09T04:00:00.000Z | NVFlare (NVIDIA Federated Learning Application Runtime Environment) is an open-source, domain-agnostic Python SDK that provides a secure, extensible framework for collaborative computing, enabling researchers to adapt existing machine learning and data science workflows to a federated paradigm without centralizing sensitive data. | nvFLARE | Developer | Library & tool, Documentation, Code | | Biomedical AI-Q Research Agent Blueprint | Deployment | false | Reasoning | Blueprint, Code | https://build.nvidia.com/nvidia/biomedical-aiq-research-agent | Blueprint | 2026-06-05T00:00:00.000Z | Blueprint workflow for biomedical research agents; not a BioNeMo NIM/API. | Biomedical AI-Q Research Agent Blueprint | Developer | Deployment, Blueprint, Code | | FQ2BAM (BWA-MEM) | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_fq2bam.html | Parabricks | 2026-03-10T04:00:00.000Z | Part of Burrows-Wheeler Aligner (BWA) bioinformatics software package to provide fast, accurate algorithm for mapping DNA sequence reads to a reference genome. | FQ2BAM (BWA-MEM) | Developer | Library & tool, Documentation | | STAR (rna_fq2bam) | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_rna_fq2bam.html | Parabricks | 2026-03-10T04:00:00.000Z | Spliced Transcripts Alignment to a Reference (STAR). Software tool for aligning RNA sequencing (RNA-seq) reads to a reference genome. | STAR (rna_fq2bam) | Developer | Library & tool, Documentation | | DeepSAP | Deployment | false | Genomic Analysis | Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/containers/clara-parabricks-deepsap?version=v0.0.3 | Parabricks | 2026-03-10T04:00:00.000Z | Enhance RNA-seq alignment by combining it with Transformer based Splice Junction Scoring (TSJS). TSJS alone improves accuracy when applied with additional tools including STAR. | DeepSAP | Developer | Library & tool, Code | | fgbio | Deployment | false | Genomic Analysis | Code | https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/containers/clara-parabricks-umi-fgbio?version=1.4.0-1 | Parabricks | 2026-03-10T04:00:00.000Z | Developed by Fulcrum Genomics, utilize molecular tagging (UMIs) to distinguish actual biological variants from machine sequencing errors, ensuring high-fidelity genomic data analysis. | fgbio | Developer | Library & tool, Code | | Segment-CT | AI Models | false | Imaging Segmentation | Model Weights, Code | https://huggingface.co/nvidia/NV-Segment-CT | MONAI | 2026-03-10T04:00:00.000Z | This 3D foundation model provides out-of-the-box automatic and interactive point-click segmentation for 132 anatomical classes in computed tomography (CT) scans. | Segment-CT | Developer | Model, Understand, Model weights | | RetMol | AI Models | false | Molecular Design | Code | https://github.com/NVlabs/RetMol | BioNeMo | 2026-06-05T00:00:00.000Z | Code route for retrieval-guided molecule generation and property-conditioned design. | RetMol | Developer | Model, Design, Code | | parakeet-1.1b-rnnt-multilingual-asr | AI Models | false | Speech | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/parakeet-1_1b-rnnt-multilingual-asr | Riva | 2026-03-10T04:00:00.000Z | The model transcribes 25 languages in upper case and lower case alphabets along with punctuations, spaces, and apostrophes. | parakeet-1.1b-rnnt-multilingual-asr | Developer | Deployment, Nim, Hosted nim/api | | llama-3.1-nemotron-ultra-253b | AI Models | false | Reasoning | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/llama-3_1-nemotron-ultra-253b-v1 | Nemotron | 2026-03-10T04:00:00.000Z | Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) which is a derivative of Meta Llama-3.1-405B-Instruct | llama-3.1-nemotron-ultra-253b | Developer | Deployment, Nim, Hosted nim/api | | DeepVariant | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_deepvariant.html | Parabricks | 2026-03-10T04:00:00.000Z | Google's deep-learning based variant caller for germline/genetic variants (i.e., inherited diseases like sickle cell disease). | DeepVariant | Developer | Library & tool, Documentation | | Mutectcaller (Mutect2) | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_mutectcaller.html | Parabricks | 2026-03-10T04:00:00.000Z | Tool from Genome Analysis Toolkit (GATK) for somatic variants. | Mutectcaller (Mutect2) | Developer | Library & tool, Documentation | | DeepSomatic | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_deepsomatic.html | Parabricks | 2026-03-10T04:00:00.000Z | Google's deep-learning based variant caller for somatic variants (i.e., non-inherited diseases like cancer). | DeepSomatic | Developer | Library & tool, Documentation | | NVIDIA NeMo | Libraries | false | Language With NVIDIA NeMo | Documentation, Code | https://www.nvidia.com/en-us/ai-data-science/products/nemo/ | NeMo | 2026-03-10T04:00:00.000Z | NeMo is a modular, interoperable, enterprise-ready software suite for managing the AI agent lifecycle, and can be used to develop digital health solutions. | NVIDIA NeMo | Developer | Library & tool, Documentation, Code | | STAR-Fusion | Deployment | false | Genomic Analysis | Documentation | https://docs.nvidia.com/clara/parabricks/latest/documentation/tooldocs/man_starfusion.html | Parabricks | 2026-03-10T04:00:00.000Z | Tool designed to detect gene fusion transcripts using RNA-Seq data-particularly from Illumina sequencing platforms. | STAR-Fusion | Developer | Library & tool, Documentation | | LLAMA3-VILA-M3-13B | AI Models | false | Reasoning | Model Weights, Code | https://huggingface.co/MONAI/Llama3-VILA-M3-13B | MONAI | 2026-03-10T04:00:00.000Z | An advanced multimodal model that analyzes MRI scans to provide structured clinical reasoning and detailed explanations for complex neurological or musculoskeletal findings. | LLAMA3-VILA-M3-13B | Developer | Model, Understand, Model weights | | Segment-CTMR | AI Models | false | Imaging Segmentation | Model Weights, Code | https://huggingface.co/nvidia/NV-Segment-CTMR | MONAI | 2026-03-10T04:00:00.000Z | Expanding on its predecessor, this model automates the detailed segmentation of over 345 anatomical and pathological classes across both CT and magnetic resonance imaging (MRI) modalities. | Segment-CTMR | Developer | Model, Understand, Model weights | | Generate-CT | AI Models | false | Synthetic Data Generation | Model Weights, Code | https://huggingface.co/nvidia/NV-Generate-CT | MONAI | 2026-03-10T04:00:00.000Z | This 3D latent diffusion model synthesizes high-resolution, realistic 3D CT volumes alongside paired segmentation masks to help augment limited medical training datasets. | Generate-CT | Developer | Model, Design, Model weights | | llama-3.3-nemotron-super-49b | AI Models | false | Reasoning | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5 | Nemotron | 2026-03-10T04:00:00.000Z | Llama-3.3-Nemotron-Super-49B-v1.5 is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. | llama-3.3-nemotron-super-49b | Developer | Deployment, Nim, Hosted nim/api | | Reason-CXR | AI Models | false | Reasoning | Model Weights, Code | https://huggingface.co/nvidia/NV-Reason-CXR-3B | MONAI | 2026-03-10T04:00:00.000Z | a specialized vision-language model designed for medical reasoning and interpretation of chest X-ray images, with detailed explanations. The model combines visual understanding with medical reasoning capabilities, enabling healthcare professionals to access comprehensive analyses and engage in follow-up discussions about radiological findings | Reason-CXR | Developer | Model, Understand, Model weights | | Generate-MR-Brain | AI Models | false | Synthetic Data Generation | Model Weights, Code | https://huggingface.co/nvidia/NV-Generate-MR-Brain | MONAI | 2026-03-12T04:00:00.000Z | This generative model synthesizes high-fidelity, 3D T1-weighted brain MRI volumes from noise, providing a powerful tool for creating diverse synthetic datasets to train other medical imaging algorithms. | Generate-MR-Brain | Developer | Model, Design, Model weights | | nemotron-3-nano-30b-a3b | AI Models | false | Reasoning | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/nemotron-3-nano-30b-a3b | Nemotron | 2026-03-10T04:00:00.000Z | Nemotron-3-Nano-30B-A3B is a large language model (LLM) trained from scratch and designed as a unified model for both reasoning and non-reasoning tasks | nemotron-3-nano-30b-a3b | Developer | Deployment, Nim, Hosted nim/api | | Generate-MR | AI Models | false | Synthetic Data Generation | Model Weights, Code | https://huggingface.co/nvidia/NV-Generate-MR | MONAI | 2026-03-10T04:00:00.000Z | Similar to the CT variant, this generative AI model creates diverse, high-quality synthetic 3D MRI scans across various contrast modalities to supplement research data. | Generate-MR | Developer | Model, Design, Model weights | | NVIDIA Riva | Libraries | false | Speech | Documentation | https://www.nvidia.com/en-us/ai-data-science/products/riva/ | Riva | 2026-03-10T04:00:00.000Z | NVIDIA Riva is a collection of GPU-accelerated microservices for building real-time, customizable speech AI functionality into your digital health applications. | NVIDIA Riva | Developer | Library & tool, Documentation | | parakeet-ctc-1.1b-asr | AI Models | false | Speech | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/parakeet-ctc-1_1b-asr | Riva | 2026-03-10T04:00:00.000Z | Parakeet transcribes audio into text, using spaces and apostrophes where needed | parakeet-ctc-1.1b-asr | Developer | Deployment, Nim, Hosted nim/api | | magpie-tts-multilingual | AI Models | false | Speech | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/magpie-tts-multilingual | Riva | 2026-03-10T04:00:00.000Z | The Magpie TTS Multilingual model converts text into audio (speech). | magpie-tts-multilingual | Developer | Deployment, Nim, Hosted nim/api | | parakeet-ctc-0.6b-asr | AI Models | false | Speech | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/parakeet-ctc-0_6b-asr | Riva | 2026-03-10T04:00:00.000Z | Parakeet transcribes audio into text, using spaces and apostrophes where needed | parakeet-ctc-0.6b-asr | Developer | Deployment, Nim, Hosted nim/api | | magpie-tts-zeroshot | AI Models | false | Speech | Hosted NIM/API, Model card | https://build.nvidia.com/nvidia/magpie-tts-zeroshot | Riva | 2026-03-10T04:00:00.000Z | The Magpie TTS Zeroshot model converts text into audio (speech). | magpie-tts-zeroshot | Developer | Deployment, Nim, Hosted nim/api | | Raw2Insights-US | AI Models | false | Image Reconstruction | Model Weights | https://huggingface.co/nvidia/NV-Raw2Insights-US | MONAI | 2026-03-12T04:00:00.000Z | Physics-informed AI model for real-time ultrasound autofocusing using raw channel data. | Raw2Insights-US | Developer | Model, Understand, Model weights | | Raw2Insights-MRI | AI Models | false | Image Reconstruction | Model Weights, Code | https://huggingface.co/nvidia/NV-Raw2insights-MRI | MONAI | 2026-03-11T04:00:00.000Z | Accelerated MR reconstruction model supporting a wide range of MRI protocols. | Raw2Insights-MRI | Developer | Model, Understand, Model weights | | GR00T-H | AI Models | false | AI Model Training | Code, Model Weights | https://github.com/NVIDIA-Medtech/GR00T-H | Isaac for Healthcare | 2026-06-01T05:28:10.000Z | GR00T-H is a vision-language-action foundation model with specialized tooling to unify and train across diverse surgical robotics datasets. | GR00T-H | Developer | Model, Robotics, Code | | Cosmos-H-Surgical | AI Models | false | Synthetic Data Generation | Code, Model Weights | https://github.com/NVIDIA-Medtech/Cosmos-H-Surgical | Isaac for Healthcare | 2026-06-10T18:20:08.000Z | Cosmos-H-Surgical delivers high-quality video prediction and transfer for surgical scenes, including future-state simulation and control-conditioned generation across modalities. | Cosmos-H-Surgical | Developer | Model, Design, Code | | Open-H | Datasets | false | AI Model Training | Code, Dataset | https://github.com/open-h/open-h-embodiment | Isaac for Healthcare | 2026-06-04T17:44:57.000Z | Open-H-Embodiment is a community-driven dataset initiative building the open, shared foundation needed to train and evaluate a generalist Vision-Language-Action (VLA) model for healthcare robotics | Open-H | Developer | Data, Dataset | | Rheo | Deployment | false | Synthetic Data Generation | Code, Blueprint | https://github.com/isaac-for-healthcare/i4h-workflows/tree/main/workflows/rheo | Isaac for Healthcare | 2026-06-05T10:11:24.000Z | Rheo is a blueprint for smart hospital automation, providing a simulation-first pipeline for healthcare robotics under NVIDIA Isaac for Healthcare. | Rheo | Developer | Deployment, Blueprint, Code | | Holoscan SDK | Deployment | false | Synthetic Data Generation | Documentation | https://nvidia-holoscan.github.io/ | NVIDIA Holoscan | 2026-05-31T10:45:55.000Z | Holoscan is an open-source library for building multi-modal, multi-rate processing pipelines with accelerated I/O and TensorRT-powered inference in Python or C++ | Holoscan SDK | Developer | Library & tool, Documentation | [Download the raw results data (JSON)](https://developer.nvidia.com/search-data/healthcare.json) * * * ### NVIDIA BioNeMo Agent Toolkit ### Changing any agent or AI platform into a life sciences agent. Built on more than a decade of NVIDIA life sciences libraries, tools, and models, NVIDIA BioNeMo™ Agent Toolkit brings domain-specific tools and skills to the new paradigm of agentic life sciences, where agents, scientists, and labs work together in closed loops to accelerate discovery. The toolkit provides the full breadth of life sciences computing as agent-callable skills: protein structure prediction, molecular docking, generative chemistry, genomics analysis, protein design, and biomarker discovery. These skills help agents gather evidence, reason over biology and chemistry, run digital experiments, and recommend what scientists should test next. [Visit GitHub ](https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit) ![](https://developer.download.nvidia.com/images/hc-bonemo-agent-toolkit-1920x1080.jpg) * * * ## Get Started With Healthcare and Life Sciences Use Cases Digital Biology Digital Health Digital Devices ### Generative Protein Binder Design Intelligently pre-optimize and screen protein binders, dramatically accelerating and improving the drug discovery process. - [Read the Tech Blog](https://developer.nvidia.com/blog/accelerate-protein-engineering-with-the-nvidia-bionemo-blueprint-for-generative-protein-binder-design/) - [Explore the NVIDIA AI Blueprint](https://build.nvidia.com/nvidia/protein-binder-design-for-drug-discovery?nvid=nv-int-txtad-232263) - [Take a Self-Paced Course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-HX-04+V1) ### Genomic Analysis Easily run essential genomics workflows to save time leveraging NVIDIA® Parabricks®. - [Explore the NVIDIA AI Blueprint](https://build.nvidia.com/nvidia/genomics-analysis) - [Try the Launchable](https://brev.nvidia.com/launchable/deploy/now?launchableID=env-2xn8JayrpXlBwGXkKASSomq6gXi) - [Get Started With NVIDIA Parabricks Documentation](https://docs.nvidia.com/clara/parabricks/latest/index.html) ### Single-Cell Analysis Accelerate data processing, clustering, dimensionality, reduction, and regression with NVIDIA RAPIDS-singlecell. - [Read the Tech Blog](https://developer.nvidia.com/blog/driving-toward-billion-cell-analysis-and-biological-breakthroughs-with-rapids-singlecell/) - [Explore the NVIDIA AI Blueprint](https://build.nvidia.com/nvidia/single-cell-analysis) - [Try the Launchable](https://brev.nvidia.com/launchable/deploy?launchableID=env-2xn8JayrpXlBwGXkKASSomq6gXi) - [Take a Self-Paced Course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-HX-05+V1) * * * #### NVIDIA-Verified Agent Skills for Digital Biology NVIDIA-verified agent skills are portable instruction sets that enable AI agents to use NVIDIA Open Models, CUDA-X™ libraries, AI Blueprints, and platform tools effectively. Verified skills are cataloged, scanned for software- and agent-native risks, signed, and documented with a skill card that describes ownership, dependencies, limitations, and verification status. Built on the agentskills.io open skills specification to work reliably across Claude Code, Codex, and Cursor, NVIDIA-verified agent skills help developers understand capabilities, discover where a skill originated, whether it was scanned for common risks, and whether it was modified after publication. [NVIDIA Agent Skills for Digital Biology](https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit) ### BioMedical AI-Q Research Agent Connect real-world data to reasoning models, enabling secure, privacy-preserving AI workflows that accelerate clinical development by improving research efficiency, insight generation, and decision-making across R&D, trials, and regulatory processes. - [Read the Tech Blog](https://developer.nvidia.com/blog/advancing-literature-review-amp-target-discovery-with-nvidia-biomedical-ai-q-research-agent-blueprint/) - [Explore the NVIDIA AI Blueprint](https://build.nvidia.com/nvidia/biomedical-aiq-research-agent) - [Try the Launchable](https://brev.nvidia.com/launchable/deploy/now?launchableID=env-2yHyPE6LQR0EJUgffHFsBRK1KD5) - [Take a Self-Paced Course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-HX-03+V1) ### Ambient Healthcare Agents Generative AI reasoning models enable provider and patient voice agents to deliver context-aware responses, automate clinical documentation, personalize patient interactions, and transform workflows for scalable, efficient care. - [Explore the Developer Example](https://build.nvidia.com/nvidia/ambient-healthcare-agents) - [Try the Launchable](https://brev.nvidia.com/launchable/deploy?launchableID=env-34f0WpRwoDMj851ced7kGEoovxH) * * * #### NVIDIA-Verified Agent Skills for Digital Health NVIDIA-verified agent skills are portable instruction sets that empower AI agents to leverage NVIDIA Open Models, CUDA-X libraries, AI Blueprints, and platform tools correctly. Verified skills are cataloged, scanned for software- and agent-native risks, signed, and documented with a skill card that describes ownership, dependencies, limitations, and verification status. Built on the agentskills.io open skills specification to work reliably across Claude Code, Codex, and Cursor, NVIDIA-verified agent skills help developers understand capabilities, discover where a skill originated, whether it was scanned for common risks, and whether it was modified after publication. [NVIDIA Agent Skills for Digital Health](https://github.com/NVIDIA/digital-health-skills) ### Training Healthcare Robotics Prototype and test advanced robotic systems, train and evaluate AI models with real and synthetic data, develop robotic policies for surgical dexterity, and seamlessly bridge simulation to real-world deployment. - [Read the Tech Blog](https://developer.nvidia.com/blog/introducing-nvidia-isaac-for-healthcare-an-ai-powered-medical-robotics-development-platform/) - [Get Started on GitHub](https://github.com/isaac-for-healthcare) - [Take a Self- Paced Course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-52+V1) ### AI-Assisted Annotation Accelerate and improve the process of labeling medical images - [Tutorial On-Demand](https://www.nvidia.com/en-us/on-demand/session/gtc24-dlit61433/) - [Get Started With MONAI Label Documentation](https://docs.monai.io/projects/label/en/latest/) - [Take a Self-Paced Course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-HX-02+V1) * * * #### NVIDIA-Verified Agent Skills for MedTech NVIDIA-verified agent skills are portable instruction sets that empower AI agents to leverage NVIDIA Open Models, CUDA-X libraries, AI Blueprints, and platform tools correctly. Verified skills are cataloged, scanned for software- and agent-native risks, signed, and documented with a skill card that describes ownership, dependencies, limitations, and verification status. Built on the agentskills.io open skills specification to work reliably across Claude Code, Codex, and Cursor, NVIDIA-verified agent skills help developers understand capabilities, discover where a skill originated, whether it was scanned for common risks, and whether it was modified after publication. [NVIDIA Agent Skills for Digital MedTech](https://github.com/NVIDIA-Medtech/medical-AI-skills) ## Explore Healthcare and Life Sciences Platforms ### BioNeMo ### AI Development Platform for Life Science Research NVIDIA BioNeMo™ is an open developer platform designed to accelerate AI-driven R&D. [Get Started With BioNeMo](https://github.com/NVIDIA-BioNeMo) ### Holoscan ### AI Sensor Processing Platform NVIDIA Holoscan is a versatile edge AI computing platform for real-time processing of streaming medical device data. [Get Started With Holoscan](https://nvidia-holoscan.github.io/) ### Isaac for Healthcare ### Simulation, Training, and Deployment of AI Robotics NVIDIA Isaac™ for Healthcare is an AI robotic development platform domain-specialized for healthcare. [Get Started With Isaac for Healthcare](https://isaac-for-healthcare.github.io/i4h-docs/workflows/) ### MONAI ### Open-Source AI Frameworks MONAI is an open-source, domain-specific framework for developing, training, and deploying deep learning models for medical imaging. [Get Started With MONAI](https://monai.io/) ### NVIDIA Parabricks ### Genomics Software Suite NVIDIA Parabricks is a scalable genomics software suite for secondary analysis that provides GPU-accelerated versions of trusted, open-source tools. [Get Started With NVIDIA Parabricks](https://docs.nvidia.com/clara/parabricks/latest/index.html) ## More Resources ![Decorative image representing Developer Community](https://developer.download.nvidia.com/icons/m48-developer-1.svg) ### Join the NVIDIA Developer Program ![Decorative image representing Training and Certification](https://developer.download.nvidia.com/icons/m48-certification-ribbon-2.svg) ### Get Training and Certification ![Decorative image representing Developer Newsletter](https://developer.download.nvidia.com/images/isaac/m48-ai-startup-256px-blk.png) ### Accelerate Your Startup ## Connect With Us ![](https://developer.download.nvidia.com/icons/m48-email-settings-ffffff.svg) Sign up for healthcare news—from exciting product releases to essential news updates. [Stay Informed](https://www.nvidia.com/en-us/industries/healthcare-life-sciences/healthcare-news-sign-up/) ![](https://developer.download.nvidia.com/icons/m48-question-support-ffffff.svg) Got questions? Our healthcare experts are here to assist you! [Contact Us](https://www.nvidia.com/en-us/industries/healthcare/contact-sales/)