Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation
arXiv:2607.04907v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over tabular patient data, and omissions in vector retrieval. This paper presents the architecture and validation of Medi-Gemma, a Clinical Decision Support System (CDSS) for wound pathology triage and workflow automation. The platform introduces a decoupled framework that separates clinical perception from data orchestration while preserving traceable reasoning. Medi-Gemma uses a multi-stage pipeline
The development of Medi-Gemma addresses critical limitations of Large Language Models (LLMs) in high-stakes clinical settings, specifically structural hallucinations and weak deterministic reasoning, which have been significant barriers to their widespread medical integration.
This development is crucial as it outlines a path toward more reliable and traceable AI applications in healthcare, mitigating risks associated with current LLM shortcomings and paving the way for automated and more efficient clinical workflows.
The architecture introduces a decoupled framework for clinical perception and data orchestration, moving away from monolithic LLM applications to a more robust, auditable hybrid system that instills greater confidence in AI-driven medical decisions.
- · Healthcare Providers
- · Patients
- · AI-powered CDSS Developers
- · Medical Technology Sector
- · Developers of unspecialized LLMs for healthcare
- · Traditional EMR analytics systems not integrated with AI-driven insights
Medi-Gemma's validation demonstrates a viable hybrid model for AI in sensitive domains, combining advanced AI with deterministic analytics for improved reliability.
This approach could accelerate the adoption of AI in other high-stakes corporate and governmental settings by providing a blueprint for trustworthy, auditable systems.
The success of such a system may spur regulatory bodies to develop specific frameworks for hybrid AI systems that prioritize transparency and deterministic reasoning over pure black-box models.
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Read at arXiv cs.AI