AI Integration for IDEXX Neo Diagnostic Imaging

The integration connects at two primary surfaces within the IDEXX Neo ecosystem: the PACS (Picture Archiving and Communication System) interface and the patient medical record. When a new DICOM study (e.g., radiograph, ultrasound) is saved to the PACS server, an event webhook or a monitored directory trigger initiates the AI workflow. The system extracts the study's metadata—patient ID, study date, modality—and the anonymized image files, sending them to a secure inference endpoint. The AI model, trained on veterinary-specific pathology, analyzes the study and returns structured findings, such as a prioritized list of detected abnormalities (e.g., cardiomegaly, hip dysplasia, osteosarcoma suspicion) with confidence scores and relevant image slices annotated.

Returning results, the integration writes a structured preliminary report into a dedicated AI Findings custom object linked to the patient record in Neo. This does not auto-populate the final radiologist report, preserving the legal record. Instead, it surfaces within the radiologist's worklist in Neo, flagging high-priority cases and providing a draft narrative. For common, high-confidence conditions, the system can auto-suggest ICD-10 codes and generate a client-friendly summary in a draft status, ready for the clinician to review, edit, and attach to the client portal. The workflow reduces manual measurement, comparison to prior studies, and initial dictation time from 15-20 minutes to 2-3 minutes of review and validation.

A staged rollout is critical. Start with a silent pilot, where AI findings are logged but not shown to clinicians, to validate accuracy and build trust in the model's performance within your specific caseload. Then, enable the worklist prioritization feature, allowing radiologists to triage their queue based on AI-detected urgency. Finally, introduce draft findings for a subset of high-prevalence, high-accuracy conditions (e.g., osteoarthritis). Governance requires clear protocols: all AI-generated content must be signed off by a licensed DVM, and an audit trail must log the original AI output alongside the clinician's final edits. This ensures clinical responsibility while capturing the efficiency gains of AI-assisted diagnosis.

The integration is triggered within IDEXX Neo when a new diagnostic imaging study (e.g., radiograph, ultrasound) is saved to a patient record. A secure webhook or API listener captures this event, extracting the study's metadata (patient ID, study type, DICOM UID) and the location of the image file in IDEXX Neo's PACS or cloud storage. This payload is queued in a secure, HIPAA-compliant message broker (e.g., AWS SQS, Azure Service Bus) to ensure reliable delivery and handle processing spikes during busy clinic hours.

A dedicated processing service retrieves the queued job, fetches the anonymized DICOM image via a secure, temporary access link, and submits it to the configured AI inference endpoint. This could be a specialized veterinary imaging model (e.g., for osteoarthritis, cardiomegaly, or pulmonary patterns) or a general vision model fine-tuned on veterinary data. The AI service returns structured findings, including detected abnormalities, confidence scores, and relevant anatomical landmarks. These results are formatted into a draft preliminary report and linked back to the original study UID.

The draft findings are not written directly into the patient's primary medical record. Instead, they are posted to a dedicated "AI Insights" sidebar or worklist within the radiologist's or veterinarian's IDEXX Neo view. This design enforces a human-in-the-loop governance model. The clinician reviews, edits, and approves the AI-generated notes. Only upon explicit approval are the finalized findings appended to the official patient record, creating a clear audit trail. This architecture minimizes disruption, maintains clinician authority, and seamlessly embeds AI as a clinical support tool within the existing Neo workflow.

A production integration for IDEXX Neo Diagnostic Imaging is architected with a clear separation of duties: the AI system acts as a preliminary findings assistant, not an autonomous diagnostician. The typical implementation pattern involves a secure middleware layer that listens for new DICOM study completions via Neo's API or a monitored network folder. This layer anonymizes and routes the study to a dedicated AI inference service—hosted in your compliant cloud or on-premises environment—which returns structured observations (e.g., "potential osteoarthritis in left stifle, confidence: 85%") and draft narrative text. These findings are then attached to the patient's imaging record in Neo as a draft note, clearly flagged for veterinarian review and signature. All data exchanges are logged with study IDs and timestamps for a complete audit trail.

Security is paramount. The integration should enforce strict access controls, ensuring only authorized veterinary users can view or modify AI-generated drafts. Patient data in transit and at rest must be encrypted. If using a cloud-based AI model, a Business Associate Agreement (BAA) is required, and data residency rules must be respected. A key governance step is establishing a radiologist-in-the-loop approval workflow within Neo. The veterinarian must actively review, edit if necessary, and formally sign off on the AI's preliminary findings before they become part of the official medical record. This maintains clinical responsibility and allows for continuous feedback to improve the AI system's performance over time.

We recommend a phased rollout to manage risk and build trust. Start with a pilot on a single, high-volume study type (e.g., routine canine stifle radiographs) in one location. During this phase, run the AI in shadow mode, where it generates findings but does not populate draft notes into Neo, allowing you to compare its output against standard practice. After validating accuracy and workflow fit, enable the draft-note creation for a small group of veterinarians. Gather feedback, refine prompts, and adjust confidence thresholds before gradually expanding to additional study types, body parts, and clinic locations. This measured approach ensures the integration enhances—rather than disrupts—the critical diagnostic workflow.