We systematically uncover clinical reasoning failures and safety blind spots in your medical AI systems before your buyers do it in public. Protect your enterprise sales pipeline and scale with absolute confidence.
"Dr. Rameesha brings a rare level of rigor to clinical AI evaluation. Her adversarial testing surfaced real execution-boundary failures — cases where insufficient clinical state still resolved to actionable outputs — and did so in a structured, reproducible way. What stands out is her ability to go beyond model performance and identify where systems produce outputs that shouldn't exist given what they can actually know. That level of clarity is critical in clinical environments. Her work directly strengthened execution gating behavior, helping move from detection to true fail-closed enforcement."Tim Zlomke — Founder, SolaceMedAI
Many healthtech startups rely on an internal team of prestigious medical advisors. But they are structurally incapable of doing what we do.
Your internal board is excellent for guiding what the AI should do. Our job is completely different: we think like adversaries to find what it can be forced to do.
Your clinical board knows medicine, but they do not know prompt injection. We know both. We use a physician-led stress-testing framework to actively weaponize medical logic against the model.
Hospital networks and institutional buyers don't just take your word for it. A third-party adversarial report by a licensed physician shifts your profile from internal negligence to proactive due diligence.
We bring an outside, adversarial medical perspective that hasn't spent months looking at your product data — seeing the system exactly how a critical enterprise buyer will.
By identifying latent logic faults early, you protect your active sales pipeline, reduce time-to-market, and prevent catastrophic public failures that kill investor trust.
We execute an aggressive, custom-engineered clinical stress test to find exactly where your model's logic fractures under pressure.
Custom architecture calibrated to your system's autonomy tier. 12 bespoke adversarial variant cases per diagnosis.
Live evaluation engine with stress-testing delivery, granular failure logging across 16 discrete failure codes.
Performance delta tracking, statistical vulnerability mapping, and categorical performance boundary definition.
NIST AI RMF translation, Huwyler Threat Taxonomy integration, and CIA-LR corporate exposure modeling.
Founder, GarrisonLabs
"I left traditional clinical practice to solve the most critical pain point in healthtech right now: textbook metrics cannot survive real-world clinical chaos. For this, I engineered the READS Framework to systematically expose hidden logic faults and safety blind spots in medical AI systems."
"At GarrisonLabs, our mission is to translate empirical clinical behaviors into structured, technical risk data. We give health-tech founders the objective, third-party adversarial insights they need to harden their systems, protect their sales pipelines, and scale enterprise-ready clinical AI with absolute confidence."
Don't wait for a hospital's IT department or an enterprise client's compliance officer to find the breaking point in your clinical LLM. Let's identify and map your vulnerabilities in private.
Schedule a Risk Assessment Consultation →A comprehensive, multi-dimensional stress-testing methodology designed to isolate latent clinical logic faults, expose safety boundary bypasses, and map technical AI risk to enterprise business liability for procurement and regulatory readiness.
View Commercial Audit Packages →Standard software QA tracks uptime and syntax. Standard data science metrics track global accuracy against clean datasets. Neither catches a model collapsing when a patient introduces conflicting clinical history mid-conversation.
Built from real clinical training and medical practice to simulate the raw, unstructured, and unpredictable nature of real-world patient and clinician interactions.
The framework operates on an elite classification engine that instantly isolates severe liability failures regardless of the system's global accuracy score.
We reject one-size-fits-all testing. The framework dynamically applies weighted evaluation models tailored precisely to your system's specific autonomy tier and operational environment.
We translate clinical logic gaps into standard corporate risk vectors by mapping every finding straight to the peer-reviewed Huwyler AI System Threat Vector Taxonomy.
Balanced across quality gradients and zero-tolerance safety compliance states.
Evaluates the diagnostic soundness, differential ranking and logical justification of the system's final clinical recommendations against gold-standard expert consensus.
Verifies that the model strictly respects clinical, legal, and operational role boundaries, resisting manipulation under adversarial pressure.
Measures the system's structural logic and clinical precision when evaluated against messy, complex, and stressful text inputs.
Ensures the model delivers equitable, unbiased clinical outputs across diverse patient profiles and does not amplify documented healthcare disparities.
For interactive systems, audits the chat engine's conversational efficiency, dynamic information integration, and formatting compliance with end-user clinical workflows.
Every engagement concludes with an institutional-grade reporting suite.
Focused Evaluation — Targeted adversarial stress-testing on 12–24 high-priority clinical pathways.
Full READS Enterprise Evaluation — Full-scale, multi-dimensional stress-testing tailored to your autonomy tier.
Continuous Advisory — Recurring pulse-check audits against new model iterations and code-pushes.
To maintain absolute legal safety and operational clarity, GarrisonLabs operates under strict boundary constraints. Our work is solely focused on real-time empirical behavioral testing.
We do not provide software validation, official compliance stamps, or legal safety guarantees for live deployments. Our service is strictly a real-time behavioral stress-test.
Our testing focuses entirely on model behavioral outputs. We do not audit cloud infrastructure, conduct cyber penetration testing, or review underlying source code architecture.
The client retains absolute, sole, and un-delegable liability for product deployment, clinical safety boundaries, and downstream patient outcomes.
We don't grade models on a curve. Look inside our independent adversarial audits of industry-leading clinical AI platforms.
Doctronic.ai — Weighted compliance score with a 0% Clinical Reasoning score. Passes the surface-level test but collapses at the reasoning layer, failing to identify life-threatening adjacent diagnoses.
Read the Full Case Study ↓Symptomate (Infermedica) — Adversarial variants that successfully evaluated critical secondary rule-outs. Emergency safety floor functioned; diagnostic precision layer did not.
Read the Full Case Study ↓Ada Health — High-stakes edge cases falling into severe risk categories. Competent pattern matcher but inconsistent clinical reasoner under multi-system comorbidity conditions.
Read the Full Case Study ↓Independent, Unsolicited Adversarial Audit — May 2026
READS AUDIT MATRIX: 12 Adversarial Cases Across a Single Disease Domain
Doctronic.ai was subjected to a rigorous 12-case adversarial clinical audit using the READS framework to evaluate its operational boundaries within the Renal Stones domain — specifically chosen because its classic presentation creates strong diagnostic anchoring, while adjacent conditions carry extreme, time-sensitive lethality. The audit exposed a critical performance boundary: while Doctronic scored perfectly on Dialogue & Workflow (100%), its core Clinical Reasoning layer experienced a total collapse (0%), failing to protect against critical rule-outs or resist dangerous patient anchoring.
Standard engineering evaluations routinely validate conversational mechanics, data retention, and linguistic variations. Under these parameters, Doctronic performs exceptionally well. However, when faced with complex clinical overlays — such as atypical geriatric presentations or anatomic modifiers — Doctronic's structural reasoning layer fractures, generating severe logic contradictions and unsafe triage recommendations.
CRITICAL failure in Valid & Reliable metrics. Accountable, Transparent, and Explainable characteristics passed — but systematic diagnostic omissions triggered a system-level failure flag.
All primary reasoning failures map to Unreliable Outputs and Biases domains, carrying high-severity risk scores.
Immediate Integrity, Legal, and Reputation liabilities. Institutional buyers will kill B2B contracts if an independent audit uncovers branch-level triage failures exposing the vendor to malpractice liability.
Independent, Unsolicited Variant Audit — May 2026
VARIANT STRESS-TEST: Acute Appendicitis Matrix (Female, 35 Years) — Infermedica v6.17.0
Symptomate was evaluated using a single-case variant stress-test focused on Acute Appendicitis in a 35-year-old female. The audit demonstrated that while the platform's safety floor functioned flawlessly — correctly routing 100% of variants to emergency care — the underlying diagnostic precision layer collapsed, exhibiting rigid linguistic anchoring and a complete failure to evaluate secondary rule-outs.
When a triage model correctly triggers an emergency alert, internal engineering teams often assume their safety guardrails are bulletproof. However, if the underlying reasoning model anchors onto an incorrect or anatomically impossible diagnosis while giving that emergency care recommendation, it creates an unearned "confidence label" — generating massive operational workflow confusion and a high liability profile when integrating with live healthcare records.
Latent Logic Faults & Biases (Proxy Discrimination). The system gives founders a false sense of security by masking severe diagnostic errors behind an accurate triage label.
System highly vulnerable to conversational edge cases that bypass triage guardrails. Unearned confidence labels create institutional liability during EMR integration.
A system recommending appendicitis evaluation for a patient who has no appendix demonstrates a foundational flaw in context integration — a procurement-killing discovery during pilot evaluation.
Independent, Unsolicited Adversarial Audit — April 2026
READS AUDIT MATRIX: 11 Adversarial Cases Across 5 Failure Modes
Ada Health's symptom checker was subjected to a structured adversarial protocol probing the operational boundary between surface-level pattern recognition and deep clinical reasoning. The audit identified a categorical performance boundary: Ada performs reliably on textbook, single-system presentations requiring static pattern matching alone, but fails systematically when cases require comorbidity integration, geographic context, or logical questioning sequences.
Under sterile conditions, clinical models achieve near-perfect scores. However, real-world patients present with mixed clinical signals, historical comorbidities, and geographic variables. When evaluated against these layered complexities, the model's structural clinical reasoning layer collapses while its superficial pattern-matching engine continues to run — producing confident-sounding outputs that are clinically dangerous.
Unreliable Outputs — Logic & Factual Hallucination. Sub-optimal clinical reasoning on high-stakes cases exposes healthtech vendors to severe liability and user mistrust.
Institutional buyers and hospital risk review committees do not buy brittle clinical agents. Branch-level collapses discovered during pilot deployment kill commercial contracts instantly.
If an enterprise client's validation team uncovers these failures during a live demo, the commercial contract is dead. Finding them first — in private — is the only viable strategy.
These are the failures we find in independent testing. Imagine what we'll find in yours — before it costs you a contract.
Schedule a Risk Assessment →Ready to identify your system's vulnerabilities in a secure, sandboxed environment? Reach out below to schedule a targeted evaluation sprint or request a custom scoping proposal.
All inquiries are treated with strict confidentiality. mNDAs signed prior to any system access.
Prefer to reach out directly? rameeshac01@gmail.com or LinkedIn