How One Developer Built an AI Clinical Memory System Using Knowledge Graphs

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A developer built Anamnesis, a clinical memory tool, during the Cognee hackathon organized by wemakedevs.org, to address a core problem in healthcare: medical records exist but lack meaningful connections between them. Unlike standard AI document tools that chunk and embed text for similarity search, Anamnesis uses Cognee's graph-based memory framework to link clinical facts such as diagnoses, medications, and lab results as interconnected nodes. The system uses Gemini's vision-based OCR to extract structured entities from uploaded PDFs like blood reports and discharge summaries, then stores them as a relational knowledge graph per patient. Doctors querying the system receive answers with traceable evidence chains pointing to specific source documents and dates, addressing the trust gap in AI-assisted clinical decisions. The tool also supports corrections, allowing clinicians to mark conditions as ruled out or medications as discontinued, with changes propagating through the graph rather than simply appending conflicting facts.

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