Research Paper

Core Innovation

This paper introduces EviDx, which integrates patient-specific diagnostic environments with scaffolded LLM agents and an observer-guided runtime to actively acquire and manage clinical evidence. Unlike prior static diagnosis models, EviDx dynamically tracks uncertainty and evidence coverage to regulate diagnostic decisions, improving performance and process stability.

Why It Matters

Accurate and timely diagnosis is critical in healthcare but often hindered by static, incomplete evidence processing. EviDx improves diagnostic workflows by dynamically acquiring and integrating patient evidence, reducing errors and uncertainty. This approach can scale across clinical settings to support better patient outcomes and more efficient use of medical expertise.

Potential Customers & Pain Points

• Hospitals – Need improved diagnostic accuracy and workflow efficiency
• Medical AI companies – Require robust evidence integration for clinical tools
• Healthcare providers – Face challenges in managing evolving patient data during diagnosis

Business Model

Subscription-based SaaS platform for hospitals and healthcare providers with tiered pricing based on usage and integration complexity; potential licensing to medical AI companies.

Competitive Landscape

• IBM Watson Health
• Google DeepMind Health
• Tempus Labs
• PathAI

Implementation Challenges

• Integration with existing clinical workflows and electronic health records
• Regulatory approval and compliance for AI diagnostic tools
• Clinician trust and adoption of AI-driven diagnostic processes

Validation Strategy

• Pilot deployments in hospital diagnostic departments to measure accuracy improvements and workflow impact
• Clinical trials comparing EviDx-assisted diagnosis versus standard practice
• User feedback collection from clinicians to refine interface and evidence management