What it is
The medical-AI tier covers everything from a DICOM image archive (the canonical storage format for medical imaging) to specialty inference servers tuned for clinical workloads. Orthanc is the most-deployed open-source DICOM PACS. dcm4che / dcm4chee-arc is the Java-based enterprise option. DICOMweb (QIDO-RS, WADO-RS, STOW-RS) is the HTTP-API standard. MONAI Label is the NVIDIA-sponsored medical-imaging annotation server. NVIDIA NIM is NVIDIA’s containerized model-serving platform increasingly used for clinical inference.
What goes wrong
DICOM servers ship without auth by default on the DICOM protocol port (104 / 11112) and frequently on the HTTP plugin (DICOMweb on :8042 for Orthanc, varied for dcm4che). Operators frequently use the protocol port as the public boundary, with no auth, because that’s what every DICOM tutorial in 2010-2018 told them to do. Once reachable, the QIDO-RS endpoint discloses studies (patient identifiers, accession numbers, modality, study date). Orthanc’s REST API exposes the same plus image data. MONAI Label’s /info/ discloses loaded trainers and datasets, operator-attribution-rich for any deployment doing custom finetuning.
How we test
For each platform we probe the documented identity endpoint: Orthanc’s /system, dcm4che’s /dcm4chee-arc/aets, DICOMweb’s /studies (with ?limit=1), MONAI’s /info/, NIM’s /v1/metadata. We confirm protocol shape (DICOM tag 0020000D is the StudyInstanceUID; its presence in a JSON ?limit=1 response is the high-confidence DICOM marker). We never fetch image data; the study and series counts plus the operator-attribution metadata are sufficient for severity. Disclosure pathways are clinical-data adjacent (HIPAA / GDPR / equivalent) and follow the hold-cluster-detail rule until acknowledged.