Reliable Healthcare Data without Risk

Discover how SemDB leverages data retrieval to transform healthcare without compromising care.

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The illustration depicts an AI-driven semantic processing system centered around a large computer monitor displaying a stylized brain, symbolizing artificial intelligence and neural networks. The brain is connected to various nodes, representing data flow and interconnections. To the left and right of the monitor, there are two browser-like windows with structured data in blue, white, and orange blocks, symbolizing code, data processing, or visualized results. A glowing lightbulb at the top-left corner represents innovation or an idea, while two gears at the bottom-right indicate processes, optimization, or automation. The color palette includes soft purples, whites, and orange accents, emphasizing technology and clean design.

The SemDB Opportunity

Unlocking the Potential of SemDB in Healthcare

Semantic technologies and knowledge graphs are reshaping the healthcare industry by addressing critical challenges:

Improved Data Integration: Integrate diverse sources, such as electronic health records (EHRs), clinical trials, and research databases, for a comprehensive patient overview.
Enhanced Patient Care: Leverage insights for clinical decision-making, personalized medicine, and better patient outcomes.
Accelerated Research: Analyze vast datasets to identify patterns, correlations, and insights for new treatments and cures.
Interoperable Health Data: Support initiatives like the European Health Data Space with reusable and secure personal health records.
Semantic Interoperability: Enable precise information exchange while preserving meaning, a cornerstone for reliable healthcare operations.

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The diagram illustrates the integration of EHR/EMR and Patient Data with SemDB (Semantic Database) and a Knowledge Graph. Multiple data sources, represented as databases and documents, feed into SemDB, which processes and connects the information. SemDB is linked to a Knowledge Graph, shown with interconnected nodes, highlighting its role in structuring and organizing the data for advanced semantic understanding and retrieval. This setup emphasizes how SemDB integrates electronic health records and patient data into a structured, meaningful format to support data-driven insights.

The Risks of Generative AI

Generative AI: A Risky Proposition for Healthcare

Generative AI, while powerful, has shown significant shortcomings in healthcare applications. A 2024 study by the University of Massachusetts Amherst and Mendel uncovered alarming rates of hallucinations in medical summaries generated by frontier AI models.

Key Findings:

Nearly all summaries analyzed contained hallucinations.
Inconsistencies included:
327 medical event errors in GPT-4o summaries.
271 medical event errors in Llama-3 summaries.
Incorrect reasoning and chronological inconsistencies were common.
The most frequent hallucinations were related to symptoms, diagnoses, and medicinal instructions, posing serious risks such as misdiagnosis or incorrect treatment.

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The image compares the medical errors between GPT-4o and Llama-3. On the left, GPT-4o is displayed with 327 Medical Errors in large, bold white text, and on the right, Llama-3 is shown with 271 Medical Errors in similar bold white text. Below, there is a note stating: "Hallucinations in NEARLY ALL summaries", with "NEARLY ALL" highlighted in orange. To the left of the note, there is an icon of a speech bubble containing a jumbled line, symbolizing a hallucination or nonsensical output. The background is dark gray, with the text in white and orange for emphasis. This visual highlights the high frequency of hallucinations and errors in medical summaries generated by both models.

The Reliable Alternative

Why SemDB is Better Than RAG for Your Legacy Data

Unlike generative AI, SemDB uses a retrieval-based approach that ensures data integrity and reliability. Our solution delivers:

Zero Hallucinations: Every response is backed by validated data from trusted sources.
Security First: No data leaves your system, eliminating cloud dependency and leakage into large language models.
Ontology-Guided Agentic Retrieval (OGAR): Industry-specific jargon and terminology are handled seamlessly.
Seamless Integration: API-based design works with your existing systems, ensuring minimal disruption.
Optimized for Legacy Systems: Leverage insights without the need for expensive infrastructure upgrades.

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The image highlights the key features of SemDB (Semantic Database), each represented by an icon. Hallucination Free is depicted with a speech bubble containing a jumbled line crossed out by an orange diagonal line, symbolizing no hallucinations in outputs. Secure is illustrated by a database icon with a padlock shield, emphasizing robust security. Precise features a target with an arrow hitting the bullseye, representing high accuracy. Seamlessly Integrated is shown with a microchip icon and an orange lightning bolt, symbolizing smooth integration with other systems. Lastly, Optimized for Legacy Systems is depicted with a gear and magnifying glass, highlighting compatibility and performance with older or existing systems. The design uses a dark gray background with white and orange accents to emphasize the features and visuals of SemDB.

SemDB allows healthcare providers to harness the benefits of AI without the risks of hallucinations, ensuring reliable and actionable insights at every step.

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