Aidoc

Aidoc aiOS™ Architectural Assessment

The Aidoc platform functions as an enterprise-scale clinical AI operating system (aiOS™). Unlike legacy point-solutions, aiOS™ serves as a centralized orchestration layer that unifies DICOM imaging, HL7 FHIR clinical data, and generative AI reporting assistants into a single governed environment 📑. In 2026, the architecture is defined by its transition to Foundation Model-based Triage and Agentic Patient Management, allowing the system to not only identify acute findings but autonomously manage the 'last mile' of follow-up care 📑.

Clinical Orchestration & Triage Engine

The system utilizes a modular processing pipeline to identify life-threatening conditions and incidental findings without disrupting the primary diagnostic viewer.

• Multitriage Foundation Models: A single architectural core capable of identifying over a dozen acute conditions (e.g., hemorrhage, fractures, free air) across various body regions in a single scan pass 📑.
• CARE™ (Clinical AI Response Engine): An agentic workflow layer that mining radiology reports and EHR notes to extract confirmed findings and automate PCP communication via Epic MyChart/In Basket 📑.
• NVIDIA MONAI Integration: A specialized API gateway that allows health systems to deploy, monitor, and govern homegrown or open-source models directly within the aiOS™ infrastructure 📑.

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Operational Scenarios

• Acute Trauma Triage: Input: Full-body CT (DICOM) via PACS gateway → Process: Foundation-model multitriage identifies intracranial hemorrhage and rib fractures simultaneously while updating the radiologist's worklist → Output: High-priority worklist notification and mobile alert to the trauma team 📑.
• Incidental Nodule Management: Input: Routine Abdominal CT (DICOM) + Radiology Report (Text) → Process: AI identifies an incidental lung nodule; agentic logic checks EHR for follow-up orders and identifies a care gap → Output: Automated message to the PCP and patient via MyChart with a sortable care-coordination dashboard entry 📑.

Evaluation Guidance

Technical evaluators should verify the following architectural characteristics:

• Orchestration Latency: Benchmark the 'time-to-notification' for multitriage foundation models compared to single-finding legacy algorithms in high-volume environments 🧠.
• Bidirectional FHIR Integration: Verify the stability of automated task creation within the EHR when the agentic tracking system identifies non-adherence to follow-up guidelines 🌑.
• Homegrown Model Governance: Request technical documentation on the monitoring and drift-detection protocols used for third-party or MONAI-based models hosted on the aiOS™ 🌑.