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name: chest-xray-analysis description: "Operate MedRAX chest-X-ray classification, segmentation, report, visual-QA, phrase-grounding, and optional image-generation tools with explicit resource and output checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Chest X-ray analysis

Use this skill when a Researcher needs to operate one of MedRAX's chest-X-ray model tools on an already available JPG or PNG image. Treat all model outputs as assistive observations, not a diagnosis or a measurement suitable for clinical decision-making without qualified review.

Scope and routing

• Use classification for a bounded screen across the 18 TorchXRayVision pathology labels.
• Use segmentation for anatomical masks, an overlay, and approximate image metrics; route DICOM conversion or display concerns to image-data-utilities.
• Use report generation for a deterministic two-section findings/impression draft.
• Use CheXagent VQA for multi-image visual questions and broad CXR reasoning.
• Use MAIRA-2 grounding for a phrase and its localized bounding boxes.
• Use LLaVA-Med only for optional, broad medical visual QA; it is not the preferred detailed CXR interpreter.
• Use RoentGen generation only for synthetic-image experiments when its manually supplied weights are already available.
• Route tool selection, graph execution, and agent state to agent-orchestration; route benchmark claims to benchmark-evaluation.

Do not accept DICOM as a direct input to these tools: the tool schemas describe JPG/PNG paths. Use the image-data utility workflow before analysis when a DICOM study must be converted, and preserve the fact that conversion can change metadata and pixel semantics.

Fast decision matrix

Need	Select	Main cost or caveat
18-label abnormality likelihoods	ChestXRayClassifierTool	DenseNet weights; values are probabilities, not calibrated diagnoses
Organ boundaries/overlay	ChestXRaySegmentationTool	PSPNet; area is not accurate for non-DICOM input
Findings plus impression prose	ChestXRayReportGeneratorTool	Two encoder-decoder models; substantial model storage
Ask one or more image questions	XRayVQATool	CheXagent 2-3B, remote code, usually CUDA and bfloat16
Locate a phrase	XRayPhraseGroundingTool	MAIRA-2; CUDA and optional bitsandbytes are practical constraints
General medical image QA	LlavaMedTool	Large optional model; implementation uses CUDA tensors directly
Create a synthetic CXR	ChestXRayGeneratorTool	RoentGen weights must be supplied manually; no clinical use

For a low-memory run, start with CPU-side path/schema validation, then choose one CUDA tool: classifier for labels, segmentation for anatomy, grounding with 8-bit loading for localization, or CheXagent only when QA is essential. Keep report generation, LLaVA-Med, and RoentGen disabled until their weights, CUDA memory, and compatible dependencies are proven. This selection verifies resource discipline, not model quality; unrun models remain unverified.

Operating procedure

1. Preflight without loading models. Run the bundled validator from scripts/validate_inputs.py. Confirm regular files, .jpg, .jpeg, or .png suffixes, required text fields, positive token/image parameters, and exact segmentation organ names.
2. Declare resources. Record the requested device, dtype, quantization, caller-managed cache/temp locations, whether weights are already present, and whether the run may use external model access. Never silently download a large model or require credentials.
3. Construct only the selected tool. Constructor options and model prerequisites are in references/api-reference.md and references/model-overview.md. Avoid initializing every tool at once.
4. Invoke with the tool schema. MedRAX tools are LangChain BaseTool objects; pass the documented field names. They return a value plus metadata (except an error may be encoded in the value). Check analysis_status and preserve returned paths before interpreting content.
5. Check output semantics. Classifier scores are per-label likelihood values. Segmentation metrics are mask-derived image metrics. Grounding boxes use normalized coordinates unless converted to original-image pixels. Generated prose and images require human review.
6. Recover narrowly. For an error, use the matching row in references/troubleshooting.md; do not switch devices, quantization, or model sources blindly. Re-run only after recording the changed setting.

Output checklist

• Record input image identity, requested tool, constructor settings, device, dtype/quantization, model availability, and analysis_status.
• Classification returns (scores, metadata) where scores map the 18 labels to numeric values in the source's 0–1 convention.
• Segmentation returns (output, metadata): segmentation_image_path, a per-organ metrics dictionary, original/model sizes, requested and processed organs, and the fixed pixel-spacing assumption. An empty organ result is not proof of absence.
• Reports contain CHEST X-RAY REPORT, FINDINGS, and IMPRESSION, plus metadata listing both generated sections.
• VQA returns {"response": text} and metadata containing image paths, prompt, token limit, and status. It does not expose a calibrated confidence score.
• Grounding returns predictions with model and original-image boxes and may return visualization_path; completed_no_finding is distinct from failure.
• LLaVA-Med returns answer text and question/image/status metadata.
• RoentGen returns a generated image_path and prompt, sampling, device, and size metadata. Generated images are synthetic artifacts, not evidence.

High-risk interpretation rules

• Do not threshold classifier scores as diagnoses unless a separately validated threshold is supplied; the tool supplies no threshold or calibration.
• Do not report segmentation area_cm2 as physically accurate for JPG/PNG. The implementation uses a fixed 0.2 mm/pixel assumption; request DICOM-aware processing for measurement work and route conversion/display separately.
• Grounding coordinates are [x_min, y_min, x_max, y_max] in normalized model/image conventions as documented in the API reference. Use the returned image_coordinates for the original image and do not mix the two spaces.
• A missing/empty prediction, a failed model load, and a model's negative finding are different observations. Preserve status and error metadata.
• Reports, VQA answers, LLaVA answers, and generated images can hallucinate; corroborate with the image and an appropriate validated workflow.

References

• API reference: exact fields, constructors, outputs, labels, coordinates, and artifacts.
• Model overview: weights, devices, dtypes, quantization, caches, and external-resource boundaries.
• Workflows: bounded selection and low-memory runs.
• Troubleshooting: predictable failures and recovery actions.
• Safe input validator: local path/schema checks only; it never imports model libraries, downloads weights, or runs inference.