Metadata-Version: 2.4
Name: healthcare-ai-guardrails
Version: 0.2.0
Summary: Guardrails for AI input/output validation in healthcare, with DICOM support
Author-email: Sam Ingram <sampingram12@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Sam Ingram
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/SamPIngram/healthcare-ai-guardrails
Project-URL: Issues, https://github.com/SamPIngram/healthcare-ai-guardrails/issues
Keywords: AI,ML,Healthcare,DICOM,Validation,Guardrails
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pydicom>=2.3
Requires-Dist: PyYAML>=6.0
Requires-Dist: jsonschema>=4.20
Requires-Dist: numpy>=1.22
Provides-Extra: test
Requires-Dist: pytest>=8.4.2; extra == "test"
Provides-Extra: lint
Requires-Dist: ruff; extra == "lint"
Requires-Dist: black; extra == "lint"
Dynamic: license-file

# Healthcare AI Guardrails

![PyPI](https://img.shields.io/pypi/v/healthcare-ai-guardrails.svg?logo=pypi&label=PyPI)
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![License](https://img.shields.io/badge/License-MIT-blue.svg)

Lightweight validation guardrails for AI model inputs/outputs in healthcare workflows, with first-class DICOM support.

## Features

- Declarative YAML spec for checks on input and output data
- Built-in validators: numeric ranges, choices, required fields
- **Specific DICOM validators:** patient age, modality, patient sex, patient position, slice thickness, pixel spacing, image orientation, SOP Class UID, BodyPartExamined, PhotometricInterpretation, pixel intensity range, KVP, X-Ray Tube Current, Exposure Time, Protocol Name, and RT Structure Set ROI presence.
- **Generic DICOM validators:** check if a tag's value is in a list, check a tag's value representation (VR), and check if a tag's numeric value is within a range.
- Output structure validation via JSON Schema
- Simple Python API and CLI (`hc-guardrails`)

## Install (users)

Install from PyPI:

```bash
pip install healthcare-ai-guardrails
```

This installs the Python API and a CLI named `hc-guardrails`.

Quick CLI check:

```bash
hc-guardrails examples/spec.example.yaml examples/output.sample.json --mode output
```

If you’re validating DICOM, `pydicom` and `numpy` are already included as dependencies.

## Install (contributors)

Dev install:

```bash
python -m venv .venv
source .venv/bin/activate
pip install -e .
```

With uv (fast Python package manager):

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
# create and use a virtualenv automatically
uv venv
source .venv/bin/activate
uv pip install -e .
```

## Quick start

Spec (`examples/spec.example.yaml`):

- Input: verify DICOM patient age in [18, 90], modality in {CT, MR}, patient sex in {M,F,O}, slice thickness/pixel spacing ranges, and sane image orientation
- Output: ensure probability ∈ [0, 1] and match a JSON Schema

Run on a DICOM file:

```bash
hc-guardrails examples/spec.example.yaml path/to/file.dcm --mode input
```

Run on a JSON output:

```bash
hc-guardrails examples/spec.example.yaml path/to/output.json --mode output
```

## Python API

```python
from healthcare_ai_guardrails import GuardrailRunner
from healthcare_ai_guardrails.validators.dicom import (
    DICOMPatientAgeCheck,
    DICOMModalityCheck,
    DICOMPatientSexCheck,
    DICOMPatientPositionCheck,
    DICOMSliceThicknessCheck,
    DICOMPixelSpacingCheck,
    DICOMImageOrientationCheck,
    DICOMKVPCheck,
    DICOMTubeCurrentCheck,
    DICOMExposureTimeCheck,
    DICOMProtocolNameCheck,
    DICOMRTStructureCheck,
)
from healthcare_ai_guardrails.validators.generic_dicom import (
    DICOMGenericNumericRangeCheck,
    DICOMGenericValueInListCheck,
    DICOMGenericTagTypeCheck,
)
import pydicom

runner = GuardrailRunner(
    [
        # Specific Validators
        DICOMPatientAgeCheck(min_years=18, max_years=90),
        DICOMModalityCheck(allowed_modalities=["CT", "MR"]),
        DICOMPatientSexCheck(allowed=["M", "F", "O"]),
        DICOMPatientPositionCheck(allowed=["HFS", "FFP", "FFS"]),
        DICOMSliceThicknessCheck(min_mm=0.5, max_mm=5),
        DICOMPixelSpacingCheck(min_mm=0.2, max_mm=2.0),
        DICOMImageOrientationCheck(tolerance=1e-3),
        DICOMKVPCheck(min_kvp=80, max_kvp=140),
        DICOMTubeCurrentCheck(min_ma=100, max_ma=500),
        DICOMExposureTimeCheck(min_ms=50, max_ms=200),
        DICOMProtocolNameCheck(allowed=["Axial Brain", "Sagittal Spine"]),
        DICOMRTStructureCheck(required_rois=["Heart", "Lungs"]),
        # Generic Validators
        DICOMGenericValueInListCheck(
            tag="Manufacturer", allowed_values=["SIEMENS", "GE"]
        ),
        DICOMGenericTagTypeCheck(tag="PatientName", expected_vr="PN"),
        DICOMGenericNumericRangeCheck(tag="BeamNumber", min_val=1, max_val=10),
    ]
)

ds = pydicom.dcmread("/path/to/file.dcm")
results = runner.run(ds)
for r in results:
    print(r.name, r.passed, r.message)
```

## YAML Spec schema

Specific DICOM validators:

- `dicom_patient_age_range` – `min_years`, `max_years`, `inclusive` (default: true)
- `dicom_modality_allowed` – `allowed_modalities: ["CT", "MR", ...]`
- `dicom_patient_sex_allowed` – `allowed: ["M", "F", "O"]`
- `dicom_patient_position_allowed` – `allowed: ["HFS", "FFP", "FFS"]`
- `dicom_slice_thickness_range` – `min_mm`, `max_mm`, `inclusive`
- `dicom_pixel_spacing_range` – `min_mm`, `max_mm`, `inclusive`
- `dicom_image_orientation_sane` – `tolerance` (default: 1e-3)
- `dicom_kvp_range` – `min_kvp`, `max_kvp`, `inclusive`
- `dicom_tube_current_range` – `min_ma`, `max_ma`, `inclusive`
- `dicom_exposure_time_range` – `min_ms`, `max_ms`, `inclusive`
- `dicom_protocol_name_allowed` – `allowed: ["Axial Brain", ...]`
- `dicom_rt_structure_present` – `required_rois: ["Heart", ...]`

Generic DICOM validators:

- `dicom_generic_numeric_range` – `tag`, `unit`, `min_val`, `max_val`, `inclusive`
- `dicom_generic_value_in_list` – `tag`, `allowed_values: [...]`
- `dicom_generic_tag_type_check` – `tag`, `expected_vr`

Other generic validators:

- `range` – `path: [..]`, `min`, `max`, `inclusive`
- `choice` – `path: [..]`, `allowed: [...]`, `case_insensitive`
- `required_fields` – `paths: [[..], [..]]`

Output validators:

- `json_schema` – `schema: {..}` (JSON Schema Draft 2020-12 compatible via `jsonschema`)
- All generic validators above

Example output schema:

```yaml
output:
  - type: json_schema
    name: output_schema
    schema:
      type: object
      required: ["probability", "label"]
      properties:
        probability:
          type: number
          minimum: 0
          maximum: 1
        label:
          type: string
```

## Development

Supported Python: 3.9–3.13 (tested in CI on Linux; library is pure Python and should work across platforms).

Run tests locally:

```bash
pytest -q
```

With uv:

```bash
uv run pytest -q
```

Lint/type-check (optional suggestions):

```bash
pip install ruff mypy
ruff check .
mypy src
```

Code style:

```bash
pip install black
black .
```

With uv:

```bash
uv pip install black
uv run black .
```

## Notes

- DICOM tags used include: `PatientAge`, `PatientBirthDate`, `StudyDate`, `SeriesDate`, `ContentDate`, `Modality`, `PatientSex`, `PatientPosition`, `SliceThickness`, `PixelSpacing`, `ImageOrientationPatient`, `KVP`, `XRayTubeCurrent`, `ExposureTime`, `ProtocolName`, `SOPClassUID`, `StructureSetROISequence`.
- Age parsing supports Y/M/W/D suffixes (per DICOM), falls back to birthdate computation.
- Validators never raise; failures are returned as `ValidationResult` and can be surfaced as warnings or errors.

## Contributing

PRs welcome. Please add/update tests for new validators or behavior and update `examples/spec.example.yaml` when adding new spec types.

To create DICOMs in tests, use `create_test_dicom` from `healthcare_ai_guardrails.testing.dicom_factory`.

## Releases and changelog

- PyPI: https://pypi.org/project/healthcare-ai-guardrails/
- Changelog: see [CHANGELOG.md](./CHANGELOG.md)

Maintainers (publishing):

- Create a GitHub Release on the `main` branch. The workflow runs tests across Python 3.9–3.13, builds the sdist and universal wheel, and publishes to PyPI.
- Ensure the repository has `PYPI_API_TOKEN` set in Secrets.

## License

MIT
