About Diagnoxal

The Technology

Diagnoxal uses a multi-stage Reasoning System that mirrors clinical diagnostic thinking. Unlike general AI systems that produce unstructured probability lists, Diagnoxal validates every criterion before suggesting a diagnosis.

The Reasoning Pipeline

• 1. Extraction: Natural language processing identifies symptoms, timeline, severity, and functional impairment from your free-text vignette.
• 2. Hypothesis Generation: The system generates multiple diagnostic possibilities simultaneously to ensure rare presentations are not overlooked.
• 3. Structured Evaluation: Each hypothesis is tested against established diagnostic criteria with explicit evidence validation.
• 4. Evidence Verification: Diagnoses are challenged through an adversarial review; weak matches are penalized.
• 5. Hierarchical Exclusion: The system applies clinical logic to rule out disorders based on hierarchy (e.g., substance use or medical causes first).
• 6. Diagnostic Parsimony: When multiple explanations fit, the system favors the simplest diagnosis that accounts for all findings.
• 7. Comorbidity Analysis: Overlapping symptoms are analyzed to distinguish between true comorbidities versus symptom clusters.

What Makes This Different

• Structured Output: Diagnostic considerations include explicit reasoning explaining why each element fits or doesn't fit the presentation.
• Evidence Attribution: The system references specific findings from your vignette to support each consideration.
• Conservative Reasoning: Unknown information is explicitly marked as "needs verification" rather than assumed.
• Targeted Inquiry: The system generates specific questions to help you clarify ambiguous criteria during your patient interview.

Privacy Architecture

• Clinical vignettes exist only in memory during analysis (stateless processing).
• No patient data is stored or logged.
• All transmissions encrypted via HTTPS.
• Aliases recommended for complete de-identification.