{
  "name": "MedMLX",
  "url": "https://medmlx.org/",
  "description": "On-device medical imaging AI for Apple Silicon. MedMLX reimplements published medical imaging models in MLX, and all inference runs on your Mac.",
  "requirements": {
    "platform": "macOS 14.0 or later on Apple Silicon",
    "python": "3.13",
    "installer": "uv",
    "installUv": "curl -LsSf https://astral.sh/uv/install.sh | sh"
  },
  "medmlx": {
    "version": "0.4.2",
    "install": "uv tool install --python 3.13 \"medmlx[all,mcp]\"",
    "run": "medmlx describe mlx-segment-ct\nmedmlx readiness mlx-segment-ct --arguments arguments.json --root /path/to/work\nmedmlx run mlx-segment-ct --arguments arguments.json --root /path/to/work",
    "downloadWeights": "medmlx setup --all --progress",
    "mcpServer": "medmlx serve --root /path/to/work",
    "mcpGuide": "https://medmlx.org/mcp/"
  },
  "article": {
    "title": "MedMLX: On-Device Medical Imaging AI for Apple Silicon",
    "authors": [
      "Joseph Sandoval",
      "Alfredo Sandoval Jr.",
      "Arsalan Saleem"
    ],
    "published": "2026-10-29",
    "doi": "10.64898/2026.10.28.26366762",
    "url": "https://doi.org/10.64898/2026.10.28.26366762",
    "citation": "Sandoval J, Sandoval A Jr, Saleem A. MedMLX: On-Device Medical Imaging AI for Apple Silicon. medRxiv. 2026.",
    "bibtex": "@article{sandoval2026medmlx,\n  title   = {MedMLX: On-Device Medical Imaging AI for Apple Silicon},\n  author  = {Sandoval, Joseph and Sandoval, Jr., Alfredo and Saleem, Arsalan},\n  journal = {medRxiv},\n  year    = {2026},\n  doi     = {10.64898/2026.10.28.26366762},\n  url     = {https://doi.org/10.64898/2026.10.28.26366762}\n}"
  },
  "maintainers": [
    {
      "name": "Joseph Sandoval",
      "url": "https://josephsandoval.org/",
      "profiles": [
        "https://orcid.org/0009-0008-9971-7103",
        "https://scholar.google.com/citations?user=DB6AKw8AAAAJ",
        "https://www.linkedin.com/in/joseph-s-182387319/",
        "https://github.com/sandovaljoseph"
      ]
    },
    {
      "name": "Alfredo Sandoval Jr.",
      "url": "https://alfredo-sandoval.com/",
      "profiles": [
        "https://orcid.org/0000-0002-7230-8325",
        "https://scholar.google.com/citations?user=vKKZksgAAAAJ",
        "https://www.ncbi.nlm.nih.gov/myncbi/alfredo.sandoval.3/bibliography/public/",
        "https://github.com/Alfredo-Sandoval",
        "https://x.com/AlfSandoval_"
      ]
    }
  ],
  "lab": {
    "name": "AI-IR",
    "url": "https://ai-ir.org/",
    "institution": "University of Texas Medical Branch"
  },
  "intendedUse": "Research software, not a medical device. Model outputs require human review.",
  "tasks": [
    "Segmentation",
    "Detection",
    "Generation",
    "Vision-language",
    "Quantitative imaging",
    "Feature extraction"
  ],
  "models": [
    {
      "name": "nnU-Net",
      "distribution": "nnunet-mlx",
      "version": "0.6.4",
      "task": "Segmentation",
      "description": "Segmentation of liver and pancreas CT, brain tumor MRI and PET/CT lesions.",
      "page": "https://medmlx.org/models/nnunet-mlx/",
      "pypi": "https://pypi.org/project/nnunet-mlx/",
      "source": "https://github.com/MedMLX/nnunet-mlx/tree/v0.6.4",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"nnunet-mlx>=0.6.4\""
    },
    {
      "name": "TotalSegmentator",
      "distribution": "totalsegmentator-mlx",
      "version": "0.1.6",
      "task": "Segmentation",
      "description": "Identify 117 organs and other body structures in CT scans.",
      "page": "https://medmlx.org/models/totalsegmentator-mlx/",
      "pypi": "https://pypi.org/project/totalsegmentator-mlx/",
      "source": "https://github.com/MedMLX/totalsegmentator-mlx/tree/v0.1.6",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"totalsegmentator-mlx>=0.1.6\""
    },
    {
      "name": "MedSAM2",
      "distribution": "medsam2-mlx",
      "version": "0.2.8",
      "task": "Segmentation",
      "description": "Lesion or organ segmentation from a box, point or mask prompt, tracked through the volume.",
      "page": "https://medmlx.org/models/medsam2-mlx/",
      "pypi": "https://pypi.org/project/medsam2-mlx/",
      "source": "https://github.com/MedMLX/medsam2-mlx/tree/v0.2.8",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"medsam2-mlx>=0.2.8\""
    },
    {
      "name": "BioMedParse",
      "distribution": "biomedparse-mlx",
      "version": "0.1.5",
      "task": "Segmentation",
      "description": "Text-prompted 3D segmentation with Microsoft BioMedParse v2.",
      "page": "https://medmlx.org/models/biomedparse-mlx/",
      "pypi": "https://pypi.org/project/biomedparse-mlx/",
      "source": "https://github.com/MedMLX/biomedparse-mlx/tree/v0.1.5",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"biomedparse-mlx>=0.1.5\""
    },
    {
      "name": "MLX-Reason-CT",
      "distribution": "mlx-reason-ct",
      "version": "0.3.6",
      "task": "Vision-language",
      "description": "Writes a structured report for a chest or abdomen CT volume, or answers a question about it.",
      "page": "https://medmlx.org/models/mlx-reason-ct/",
      "pypi": "https://pypi.org/project/mlx-reason-ct/",
      "source": "https://github.com/MedMLX/mlx-reason-ct/tree/v0.3.6",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"mlx-reason-ct>=0.3.6\""
    },
    {
      "name": "FDG PET/CT",
      "distribution": "fdg-pet-ct-mlx",
      "version": "0.2.6",
      "task": "Generation",
      "description": "Synthetic FDG PET from CT and organ labels, with scanner blur, scatter and count noise.",
      "page": "https://medmlx.org/models/fdg-pet-ct-mlx/",
      "pypi": "https://pypi.org/project/fdg-pet-ct-mlx/",
      "source": "https://github.com/MedMLX/fdg-pet-ct-mlx/tree/v0.2.6",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"fdg-pet-ct-mlx>=0.2.6\""
    },
    {
      "name": "NV-Segment-CT",
      "distribution": "mlx-segment-ct",
      "version": "0.3.5",
      "task": "Segmentation",
      "description": "Automatic and prompted 3D CT segmentation with NVIDIA VISTA-3D.",
      "page": "https://medmlx.org/models/mlx-segment-ct/",
      "pypi": "https://pypi.org/project/mlx-segment-ct/",
      "source": "https://github.com/MedMLX/mlx-segment-ct/tree/v0.3.5",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"mlx-segment-ct>=0.3.5\""
    },
    {
      "name": "SegResNet",
      "distribution": "segresnet-mlx",
      "version": "0.3.3",
      "task": "Segmentation",
      "description": "Segmentation of brain tumors, kidney structures and whole-body anatomy from MRI or CT.",
      "page": "https://medmlx.org/models/segresnet-mlx/",
      "pypi": "https://pypi.org/project/segresnet-mlx/",
      "source": "https://github.com/MedMLX/segresnet-mlx/tree/v0.3.3",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"segresnet-mlx>=0.3.3\""
    },
    {
      "name": "UNesT",
      "distribution": "unest-mlx",
      "version": "0.2.7",
      "task": "Segmentation",
      "description": "Kidney cortex, medulla and collecting system segmentation in contrast-enhanced CT.",
      "page": "https://medmlx.org/models/unest-mlx/",
      "pypi": "https://pypi.org/project/unest-mlx/",
      "source": "https://github.com/MedMLX/unest-mlx/tree/v0.2.7",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"unest-mlx>=0.2.7\""
    },
    {
      "name": "SwinUNETR",
      "distribution": "swin-unetr-mlx",
      "version": "0.2.6",
      "task": "Segmentation",
      "description": "Organ segmentation in abdominal CT.",
      "page": "https://medmlx.org/models/swin-unetr-mlx/",
      "pypi": "https://pypi.org/project/swin-unetr-mlx/",
      "source": "https://github.com/MedMLX/swin-unetr-mlx/tree/v0.2.6",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"swin-unetr-mlx>=0.2.6\""
    },
    {
      "name": "SynthSeg",
      "distribution": "synthseg-mlx",
      "version": "0.3.4",
      "task": "Segmentation",
      "description": "Brain segmentation and parcellation.",
      "page": "https://medmlx.org/models/synthseg-mlx/",
      "pypi": "https://pypi.org/project/synthseg-mlx/",
      "source": "https://github.com/MedMLX/synthseg-mlx/tree/v0.3.4",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"synthseg-mlx>=0.3.4\""
    },
    {
      "name": "FleXray",
      "distribution": "flexray-mlx",
      "version": "0.2.6",
      "task": "Segmentation",
      "description": "Radiograph segmentation.",
      "page": "https://medmlx.org/models/flexray-mlx/",
      "pypi": "https://pypi.org/project/flexray-mlx/",
      "source": "https://github.com/MedMLX/flexray-mlx/tree/v0.2.6",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"flexray-mlx>=0.2.6\""
    },
    {
      "name": "FlexibleUNet",
      "distribution": "flexible-unet-mlx",
      "version": "0.2.6",
      "task": "Segmentation",
      "description": "Identify surgical instruments in endoscopy images.",
      "page": "https://medmlx.org/models/flexible-unet-mlx/",
      "pypi": "https://pypi.org/project/flexible-unet-mlx/",
      "source": "https://github.com/MedMLX/flexible-unet-mlx/tree/v0.2.6",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"flexible-unet-mlx>=0.2.6\""
    },
    {
      "name": "3D RetinaNet",
      "distribution": "lung-nodule-mlx",
      "version": "0.2.6",
      "task": "Detection",
      "description": "MONAI RetinaNet lung nodule detection in 3D chest CT.",
      "page": "https://medmlx.org/models/lung-nodule-mlx/",
      "pypi": "https://pypi.org/project/lung-nodule-mlx/",
      "source": "https://github.com/MedMLX/lung-nodule-mlx/tree/v0.2.6",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"lung-nodule-mlx>=0.2.6\""
    },
    {
      "name": "NV-Generate",
      "distribution": "mlx-generate-ctmr",
      "version": "0.5.2.post2",
      "task": "Generation",
      "description": "Synthetic 3D CT and MRI generation with NVIDIA NV-Generate (MAISI).",
      "page": "https://medmlx.org/models/mlx-generate-ctmr/",
      "pypi": "https://pypi.org/project/mlx-generate-ctmr/",
      "source": "https://github.com/MedMLX/mlx-generate-ctmr/tree/v0.5.2.post2",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"mlx-generate-ctmr>=0.5.2.post2\""
    },
    {
      "name": "LesionDiffusion",
      "distribution": "lesiondiffusion-mlx",
      "version": "0.2.6.post2",
      "task": "Generation",
      "description": "Synthetic lesions in CT, with control of organ, size, placement and phenotype.",
      "page": "https://medmlx.org/models/lesiondiffusion-mlx/",
      "pypi": "https://pypi.org/project/lesiondiffusion-mlx/",
      "source": "https://github.com/MedMLX/lesiondiffusion-mlx/tree/v0.2.6.post2",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"lesiondiffusion-mlx>=0.2.6.post2\""
    },
    {
      "name": "MedGemma",
      "distribution": "med-vlm-mlx",
      "version": "0.3.2",
      "task": "Vision-language",
      "description": "Ask questions about medical images and receive written responses on your Mac.",
      "page": "https://medmlx.org/models/med-vlm-mlx/",
      "pypi": "https://pypi.org/project/med-vlm-mlx/",
      "source": "https://github.com/MedMLX/med-vlm-mlx/tree/v0.3.2",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"med-vlm-mlx>=0.3.2\""
    },
    {
      "name": "RadImageNet",
      "distribution": "radimagenet-mlx",
      "version": "0.3.3",
      "task": "Feature extraction",
      "description": "Extract numerical image features for research with models trained on RadImageNet.",
      "page": "https://medmlx.org/models/radimagenet-mlx/",
      "pypi": "https://pypi.org/project/radimagenet-mlx/",
      "source": "https://github.com/MedMLX/radimagenet-mlx/tree/v0.3.3",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"radimagenet-mlx>=0.3.3\""
    },
    {
      "name": "PyRadiomics",
      "distribution": "mlxradiomics",
      "version": "0.1.0.dev10",
      "task": "Quantitative imaging",
      "description": "Measure image intensity, texture and shape within a selected region.",
      "page": "https://medmlx.org/models/mlxradiomics/",
      "pypi": "https://pypi.org/project/mlxradiomics/",
      "source": "https://github.com/MedMLX/mlx-radiomics/tree/v0.1.0.dev10",
      "install": "uv venv --python 3.13\nsource .venv/bin/activate\nuv pip install \"mlxradiomics>=0.1.0.dev10\""
    }
  ]
}