The AI Decision Support Platform

Argeron Medical has developed an optional AI-based clinical decision support platform designed to enhance the interpretation of biological aging rate measurements generated by its patented diagnostic kit.

The AI platform does not measure biological aging rate and does not generate diagnoses. Its role is to provide contextual, reference-based insight after the diagnostic result has already been produced locally.

A Clear Separation of Roles

The Argeron system is built on a strict functional separation:

• Diagnostic kit measures biological aging rate using a patented LC-MS assay
• AI decision support interprets results by comparing them to aggregated reference data

This separation ensures:

• Clinical transparency
• Regulatory clarity
• Preservation of local clinical autonomy

What the AI Platform Does

When users choose to upload their results, the AI platform provides:

• Comparison against aggregated reference datasets
• Longitudinal trend analysis across repeated measurements
• Contextual interpretation relative to age, sex, and biological patterns
• Generation of detailed analytical reports for clinical or research use

These reports are designed to support, not replace, professional judgment.

What the AI Platform Does NOT Do

To avoid ambiguity, the AI platform explicitly does not:

• Measure biological aging rate
• Alter diagnostic assay results
• Issue medical diagnoses
• Override clinical decision-making

Artificial intelligence functions strictly as a post-measurement decision support layer.

Reference-Based Intelligence

The strength of the AI platform lies in its reference architecture. Participating hospitals, laboratories, and clinics may optionally contribute anonymized results. As the reference dataset grows, the platform evolves into a globally informed analytical system, while each institution retains full control over its own diagnostic processes.

This enables:

• More precise contextual interpretation
• Identification of emerging biological patterns
• Continuous refinement of reference ranges

A Globally Scalable Algorithmic Framework

Because the AI platform operates independently of measurement hardware, it is:

• Compatible with results generated on different LC-MS systems
• Scalable across geographies and institutions
• Designed for continuous learning without centralizing diagnosis

The algorithmic framework improves through reference expansion, not through direct clinical intervention.

Privacy, Autonomy, and Control

Data contribution to the AI platform is:

• Optional
• Anonymized
• Controlled by the contributing institution

Clinical decisions always remain local. The AI platform functions as an external analytical reference, not a centralized authority.

Clinical and Research Value

By combining local diagnostic measurement with global reference intelligence, the platform enables:

• Deeper understanding of biological aging trajectories
• Earlier identification of atypical aging patterns
• Support for preventive and longitudinal health strategies
• Generation of high-quality, real-world evidence

This model bridges the gap between individual diagnostics and population-level insight.