Aidoc

What Aidoc does in a health system

Aidoc provides a clinical AI platform for health systems. Its tools analyze supported medical imaging, surface suspected findings in clinical workflows, communicate with care teams and help organize follow-up. The intended users include radiologists, clinical specialists, care coordinators and the IT teams responsible for connecting hospital systems.

The platform's role extends beyond producing an image-analysis result. A finding needs to reach the appropriate worklist or clinician, be reviewed in context and, where relevant, enter a care pathway. Aidoc combines these stages through its aiOS platform, coordination software and patient-management products.

Imaging orchestration and existing clinical systems

aiOS orchestrates algorithms across supported scans and uses scan and patient context to route results into clinical workflows. Aidoc describes prioritization, monitoring and governance functions, including validation, drift detection and tracking clinician overrides. These are platform functions rather than a promise that every condition can be detected from every scan.

The integration documentation covers PACS, electronic health records, radiology worklists and other hospital systems, with DICOM, HL7 and FHIR among the interfaces. A hospital needs to establish which systems, study types and destinations are included in its implementation. Existing infrastructure, configuration and local responsibilities shape how an AI result reaches the person who must act on it.

Care-team communication and follow-up

Aidoc's care-coordination tools provide desktop and mobile notifications, image and patient-data sharing, team activation and configurable escalation. These functions can support a multidisciplinary pathway where an imaging finding needs review by several services. Routing and escalation rules should reflect the hospital's actual staffing and responsibilities.

The separate patient-management offering examines radiology reports for confirmed findings and combines relevant electronic-record information to organize patients for follow-up. It describes notifying clinic staff, nurses and schedulers. This addresses a different problem from urgent image triage: helping teams manage findings that require subsequent evaluation rather than an immediate response.

An implementation workflow

For a radiology-led evaluation, begin with one supported examination and one defined care pathway. Agree on which studies enter analysis, where results appear and which clinician reviews them. Connect the necessary imaging and record systems, then evaluate representative local cases before expanding the workflow.

Include cases without the target finding, difficult image quality and situations requiring escalation. Check whether notifications reach the correct service and whether someone owns the next step. Review overrides and missed or delayed handoffs with clinical and operational staff. For follow-up work, distinguish a report-identified finding from a completed appointment; a software notification alone does not establish that the patient received care.

Clinical scope, limits and access

Supported indications and imaging protocols depend on the particular algorithm and product. Aidoc's FAQ directs users to the respective device user guides and regulatory information for intended use. Confirm local availability, instructions, contraindications and the required clinician oversight with the supplier. An AI flag is not a complete diagnosis, and an unflagged study should not be treated as proof that no abnormality exists.

Aidoc offers demonstrations and institutional sales discussions through its website. A public self-service price is not listed on the reviewed product pages. Request a proposal covering the selected algorithms, integrations, deployment, support and training rather than assuming all platform modules are included in one purchase.