#44 — How "Reverse RAG" eliminates healthcare hallucinations

Why it matters: Mayo Clinic has developed a novel solution to one of AI's most persistent problems—hallucinations—that could transform healthcare data management while creating new opportunities for health tech startups.§

The big picture: Even as LLMs become more sophisticated, they continue to produce inaccurate information that can be particularly dangerous in healthcare settings where precision is critical.§

The innovation: "Reverse RAG"

Mayo Clinic has implemented what they call "backwards RAG" (Retrieval-Augmented Generation), where:§

• The model extracts relevant information, then links every data point back to its original source
• This approach has nearly eliminated data-retrieval hallucinations in non-diagnostic use cases
• The system uses the CURE (Clustering Using Representatives) algorithm paired with vector databases

How it works: Mayo's LLM splits generated summaries into individual facts, matches them back to source documents, then uses a second LLM to score how well the facts align with those sources.§

By the numbers:§

• Tasks that once took clinicians 90 minutes now take just 10 minutes with AI assistance
• Mayo has already converted 1.5 million chest X-rays and plans to process another 11 million

Deeper dive: The CURE algorithm simplified

Think of CURE as Mayo Clinic's fact-checking system for AI. Here's how it works in simple terms:§

• Smart grouping: CURE organizes similar pieces of medical information together, like sorting patient symptoms into related categories.§

• Multiple reference points: Unlike simpler systems that use just one reference point per group, CURE uses several. This is like having multiple witnesses verify a story instead of relying on just one person.§

• Outlier detection: The system can spot unusual data that doesn't fit normal patterns—similar to how fraud detection systems flag suspicious transactions.§

• Quality control: When the AI generates information, CURE verifies each fact against original medical records, ensuring nothing is fabricated.§

• Confidence scoring: The system rates how confident it is about each piece of information, helping doctors know what they can trust.§

This approach gives Mayo a practical way to use AI in healthcare without the risk of made-up information that could affect patient care.§

Market opportunities

For founders: The healthcare AI space is ripe for innovation in:§

1. EHR data extraction: Mayo initially focused on discharge summaries but sees applications across clinical workflows
2. Outside record synthesis: Converting complex patient records from various formats into usable summaries
3. Imaging analysis: Mayo's work with Microsoft on chest X-rays demonstrates the potential for AI-enhanced diagnostics
4. Genomics platforms: Mayo is working with Cerebras Systems on genomic models for personalized treatment predictions

The bottom line: Mayo's approach solves a critical trust problem in healthcare AI. As Matthew Callstrom, Mayo's medical director for strategy and chair of radiology, puts it: "Physicians are very skeptical, and they want to make sure that they're not being fed information that isn't trustworthy."§

What's next

Mayo is expanding this capability across its practice while exploring more advanced applications in:§

• Diagnostic support tools for neurological conditions
• Personalized medicine through patient cohort mapping
• Genomics and proteomics research applications

The vision: "Our goal is to return humanity to healthcare as we use these tools," says Callstrom, pointing to a future where AI handles administrative burdens while physicians focus on patient care.§