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Healthcare & Life Sciences

Clinical Decision Support You Can Verify

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The Problem

Clinical AI without transparency is a liability

Healthcare AI operates in one of the most regulated environments on earth. A black box diagnosis isn't just unhelpful—it's potentially dangerous and legally indefensible.

FDA Scrutiny
Software as Medical Device (SaMD) requires documented clinical reasoning and validation.
Malpractice Exposure
AI-assisted misdiagnosis without explanation creates massive liability for health systems.
Clinician Trust
Doctors won't follow recommendations they can't understand or verify.
Health Equity
Algorithmic bias in healthcare has life-or-death consequences. Regulators are watching.
The Solution

Every clinical recommendation with complete reasoning

Aequitas provides the clinical transparency that regulators require and clinicians trust, while maintaining diagnostic accuracy.

Deterministic Causal Tracing™
Maps every diagnosis to specific clinical evidence. See exactly which symptoms and labs drove each recommendation.
Structural Logic Mapping™
Validates that diagnostic logic follows evidence-based clinical guidelines.
Adversarial Self-Correction™
Three independent clinical auditors verify every recommendation for accuracy and safety.

See It In Action

Watch how patient data flows through causal filters and clinical guidelines to produce verified diagnoses. Every path is traceable.

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TRY IT: Hover over flows to see data transformation at each stage

Real-World Applications

UseCases

See how organizations transform their operations with transparent AI governance

01

Clinical Decision Support

Diagnostic recommendations clinicians can verify and trust.

BeforeAI suggestions ignored because physicians can't verify reasoning
AfterTransparent reasoning increases clinician adoption by 3x
02

Prior Authorization

Automated medical necessity documentation.

BeforeDays of back-and-forth with payers over undocumented decisions
AfterInstant, complete clinical justification for every authorization
03

Clinical Trial Matching

Explainable patient-trial matching with eligibility reasoning.

BeforeMissed trial opportunities due to opaque matching algorithms
AfterClear eligibility explanations for every recommended trial
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For the first time, our physicians can actually see why the AI made each recommendation. Adoption went from 20% to 80% in three months.
Chief Medical Information Officer
Clinical AI Implementation•Academic Medical Center

Ready to transform healthcare governance?

See how Aequitas can bring transparency to your healthcare AI decisions.

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