Abstract: Clinical workflows demand sub-second interpretation of multimodal patient data. This document outlines Aevum Zenth's proprietary cross-modal transformer architecture (AZ-CMT) designed to process DICOM-standard imaging (CT, MRI, X-Ray, Histopathology) alongside electronic health record (EHR) text streams. By implementing a sparse-attention vision encoder, bidirectional cross-modal fusion layers, and a quantized autoregressive text decoder, AZ-CMT achieves <85ms end-to-end latency on standard GPU clusters while maintaining clinical-grade diagnostic alignment (CIDEr: 0.89, ROUGE-L: 0.84). The system is deployed across 14 integrated health networks, supporting radiology report synthesis, multi-modal triage, and research-scale cohort annotation.
System Architecture Overview
The AZ-CMT pipeline is structured around three primary computational modules optimized for throughput, memory efficiency, and modality alignment. Each stage operates asynchronously with dynamic batching to handle variable clinical data loads.
Vision Encoding & Feature Extraction
Raw DICOM slices undergo automated preprocessing: HU normalization, bone/soft-tissue segmentation, and noise reduction via a lightweight U-Net prior. The vision backbone utilizes a shifted-window transformer architecture pre-trained on 12M anonymized medical images, reduced to 45% parameter count via structured pruning without accuracy degradation.
Cross-Modal Alignment
Image embeddings and tokenized EHR/clinical notes are projected into a shared latent space using a symmetric contrastive loss. The fusion layer employs sparse cross-attention to isolate anatomically relevant features, ignoring non-diagnostic artifacts (motion blur, implant artifacts) through a learnable confidence mask.
Real-Time Text Synthesis
The decoder generates structured clinical reports using a constrained vocabulary of 42,000 medical terms. Beam search is replaced with stochastic top-k sampling with temperature annealing to balance speed and coherence. Output includes differential diagnoses, abnormality bounding boxes (mapped back to pixel coordinates), and standardized ICD-11/SNOMED-CT coding suggestions.
Benchmark & Performance Metrics
Evaluated on MIMIC-CXR, CheXpert, and internal multi-center validation cohorts. All metrics averaged across 5 inference runs.
| Metric | Result | Benchmark |
|---|---|---|
| End-to-End Latency (P95) | 82ms | Standard: 340ms |
| Diagnostic Recall | 94.2% | Radiologist avg: 91.8% |
| Text Fluency (CIDEr) | 0.89 | Prev. SOTA: 0.81 |
| VRAM Footprint (FP16) | 6.8 GB | Equivalent: 14.2 GB |
| Throughput (Images/sec) | 118 | Baseline: 64 |
Regulatory & Security Compliance
AZ-CMT is engineered for clinical deployment under strict regulatory frameworks. All inference pipelines operate in isolated, air-gapped environments where required.
On-prem deployment supports zero data exfiltration via local DICOM routing and HL7/FHIR message brokering. Audit trails log every inference timestamp, model version, and confidence score for regulatory traceability.
Integration & SDK Access
Access AZ-CMT via REST/gRPC endpoints or native Python SDK. Full DICOMweb, Orthanc, and Agfa compatibility included.
Publications & References
1. Chen, L. et al. (2025). "Sparse Cross-Attention for Latency-Constrained Medical Vision-Language Models". Nature Machine Intelligence, 7(4), 312-329.
2. Aevum Zenth Research Lab. (2025). "AZ-CMT v2.1 Technical Specification: Real-Time Inference Pipelines for Clinical NLP". Zenth Whitepaper Series #44.
3. FDA. (2024). "Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan".
4. Wang, Y. et al. (2026). "Quantized Transformer Decoders for On-Edge Radiology Reporting". IEEE TMI, Early Access.
Access AZ-CMT for Clinical Research
Request API credentials, on-prem deployment documentation, or schedule a technical architecture review with our Zenth Health Sciences engineering team.