BERLIN - The fundamental flaw of current medical AI is not ignorance, but creativity. Large Language Models (LLMs) are designed to generate plausible text, a feature that becomes a liability when precision is required. When faced with a gap in data, a standard model may bridge it with a statistically likely—but factually invented—detail. In a hospital, such "hallucinations" are not merely glitches; they are liabilities.
Today, aiomics announces a structural solution to this problem with the launch of its Medical GraphRAG (Retrieval-Augmented Generation) engine aligned with FHIR standards. Moving beyond the industry standard of "vector search," which retrieves text based on similarity, aiomics has implemented a Knowledge Graph architecture. This system does not just read patient records; it maps them into a rigid logical framework looking at both temporal and causal relationships.
This shift is necessitated by the "Silver Tsunami"—the surge of elderly patients with "multimorbidity" who are currently straining the German healthcare system. These patients present with dense, chaotic medical histories spanning decades. A standard AI, treating these files as a stream of text, often struggles to distinguish between a "current medication" and a "discontinued medication" mentioned in a ten-year-old fax.
Aiomics’ Med-Graph addresses this by deconstructing clinical notes into discrete entities and relationships. Instead of predicting the next word, the system traces the connection: Patient A → diagnosedWith → Condition B → treatedWith → Medication C.
"We are replacing probabilistic guessing with deterministic constraints," explains Dr. Nikita Tarasov, Chief Technology Officer at Aiomics. "Standard AI functions like an improvisational actor; it tries to keep the story going. Our GraphRAG system functions like a court clerk. It is constrained to report only those relationships that explicitly exist in the graph. While no system is immune to error, this architecture forces the AI to 'cite its sources' for every assertion, shifting the workflow from blind trust to rapid verification."
This "verification" is critical. The system utilizes a "Truth Anchor" mechanism, ensuring that every data point in the graph is hyperlinked to the specific pixel in the original source document. This allows human clinicians to audit the AI’s reasoning in seconds—a "human-in-the-loop" safeguard that is essential for compliance with German MD audit standards.
By enabling "multi-hop reasoning"—the ability to logically connect facts across different documents and time periods—the Med-Graph allows Aiomics to flag contraindications or missing values that a simple text search would miss. This moves the technology from a passive summary tool to an active "reasoning engine" capable of navigating the complexity of modern geriatric care.
About Aiomics
Aiomics is a Berlin-based health-tech company automating the administrative intake of patients for European hospitals. Its "Intelligent Pre-Admission Validation Gateway" structures and audits referral data before it enters the clinic. By combining Large Language Models with strict Medical Knowledge Graph logic, Aiomics supports healthcare providers in securing revenue and reducing the administrative burden on clinical staff.
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Company Name: Aiomics GmbH
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Country: Germany
Website: https://www.aiomics.io/