Adept
Assessment
Medical Technology & Oncology Diagnostics
Case StudyScope: FDA Software as a Medical Device (SaMD) Validation & Real-Time Hallucination Interception

Sub-Millisecond Hallucination Gating and Automated Validation for Clinical Oncology Diagnostic LLM

A fast-growing computational pathology firm integrated multimodal LLMs to assist radiologists in staging complex thoracic tumors. Prior to Adept engagement, probabilistic variance in model inference posed non-deterministic clinical risks that blocked FDA premarket notification (510(k)). Adept deployed the Mayar test harness and engineered deterministic hallucination boundary gates.

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99.98%
Factual Alignment
Measured against board-certified oncology reference standards across 45,000 evaluation vectors.
0.00%
Critical Diagnostic Hallucinations
Zero unverified clinical assertions bypassed the Mayar real-time inference interceptor.
68%
Reduction in Validation Cycle Time
Automated test suite cut pre-release clinical validation turnaround from 6 weeks to 4 business days.
01. The Problem & Attack Surface

Production Bottlenecks & Architectural Risks

The client deployed a foundation multimodal model to synthesize radiologic images, genomic biomarkers, and oncological clinical histories into structured diagnostic briefs.

During stress testing, the pipeline exhibited a 3.8% rate of ungrounded biomarker correlation hallucinations—an unacceptable risk profile for Class II medical software.

Traditional deterministic integration tests failed repeatedly because model outputs were semantically correct but syntactically divergent across minor temperature fluctuations.

Regulatory compliance under ISO 13485 and FDA SaMD guidance required exhaustive traceability from clinical intent to test assertion across every model revision.

Identified Vulnerabilities & Attack Vectors
•Non-deterministic inference pipeline feeding clinical report generators without schema-level invariant validation.
•Unconstrained context retrieval from patient electronic health records (EHR) allowing unstructured clinical notes to induce prompt hijacking.
•Absence of continuous model evaluation harness during fine-tuning iterations, leading to silent regression in diagnostic sensitivity.
02. The Engineering Architecture

Adept Intervention & Validation

Constructed a synthetic multimodal test suite of 45,000 clinically validated oncology vignettes spanning 11 thoracic staging permutations.
Implemented Adept Mayar with dual-stage LLM-as-judge evaluation, mathematically bounding factual consistency against NCCN clinical practice guidelines.
Engineered sub-millisecond deterministic semantic boundary interceptors that trap unverified clinical assertions before rendering to the clinician interface.
Automated continuous regression gates within GitHub Actions, establishing automated traceability matrices required for FDA 510(k) submission.
03. Verified Outcomes

Production Impact & Regulatory Clearance

Achieved full compliance clearance for FDA 510(k) clinical validation protocol under ISO 13485 standards.
Clinical oncologists reported 42% decrease in documentation fatigue with verified zero-trust brief generation.
Engineering gained automated CI/CD gating that prevents non-deterministic model regression on every weights update.
Executive & Technical Verification

“Adept transformed our AI validation from an existential clinical bottleneck into our strongest regulatory asset. Their precision harness gave our medical board unshakeable confidence in model determinism.”

Chief Medical & Technology Officer
Vanguard Thoracic Diagnostics
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