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AI finally has a fact-checker—and it’s changing the game for healthcare.
What if every answer your AI gave came with receipts you could actually trust?
Read on to see how Mayo Clinic cracked the code on trustworthy AI—or skip the deep dive and book a quick, no-pressure chat with Michael Weinberger to explore how verifiable AI could work for your practice.
Mayo Clinic has developed a groundbreaking approach to ensure AI systems provide accurate, reliable information in healthcare settings. In fact, a team working with Mayo pioneered an innovative approach to combat AI hallucinations in healthcare applications using a technique called reverse Retrieval-Augmented Generation (RAG).
This groundbreaking method has effectively eliminated nearly all data-retrieval-based hallucinations in non-diagnostic healthcare use cases, doing so by linking every extracted data point from the patient medical record back to its original source in a human-verifiable, human-readable way.
The technique represents a significant advancement in making AI more reliable for healthcare applications, allowing Mayo Clinic to deploy these models across clinical practices while maintaining the high standards of accuracy required in medical contexts.
This innovation has already demonstrated remarkable efficiency improvements in real-world clinical settings.
For instance, reviewing and summarizing external medical records—which typically consumed around 90 minutes of a clinician’s day—can now be accomplished in approximately 10 minutes using this verified AI approach.
This dramatic time savings has generated “remarkable interest” across Mayo’s operations, as it directly addresses administrative burdens that frustrate physicians and staff.
Below, we outline how Mayo achieved this impressive result, describe the technologies involved, and importantly, strive to help you understand how to make verifiable AI and trustworthy LLM generations a part of your own business.
Despite their sophisticated capabilities, AI systems sometimes produce incorrect information – known as hallucinations – essentially making things up while presenting them as facts.
Traditional RAG systems, while helpful, still face limitations that contribute to hallucination problems: retrieving irrelevant or low-quality data, struggling to assess information relevance, or producing outputs that don’t conform to requested formats.
In healthcare, where precision is critical, these errors pose serious risks to patient care and organizational liability.
Traditional AI systems struggle with consistently delivering reliable information, which has slowed adoption in medical practices where patient safety is paramount.
Mayo Clinic’s innovative solution involves a verification-first approach.
Rather than simply pulling information from databases, their system first extracts relevant details and then carefully traces every piece of information back to its original source in the patient record.
This creates a clear audit trail for all information produced by the AI, dramatically reducing the risk of fabricated or hallucinated content.
The reverse RAG approach fundamentally differs from traditional RAG by emphasizing verification rather than just retrieval.
While conventional RAG focuses on finding relevant information to augment the model’s knowledge, reverse RAG prioritizes confirming that each piece of generated information can be traced back to a legitimate source.
The major innovation is really just that Mayo’s approach emphasizes verification above all else.
This fundamental shift prioritizes accuracy and reliability – the two factors most critical for healthcare applications.
Mayo’s implementation follows a practical verification process that any medical practice can adapt:
As Dr. Callstrom of the Mayo Clinic emphasized, “Every data point is linked to the laboratory source or imaging. The system guarantees that references are authentic and accurately retrieved, effectively resolving most retrieval-related hallucinations.”
Mayo Clinic started with non-diagnostic uses, particularly discharge summaries – an area where administrative burden is high but clinical risk is manageable. This practical approach allowed them to refine the process before expanding to other areas, applying it to more sensitive clinical contexts.
At the heart of Mayo Clinic’s approach is the CURE algorithm, which plays a critical role in organizing and classifying data points based on similarities. CURE enhances standard clustering through a hierarchical approach:
Mayo’s team utilized vector databases to initially process patient records, enabling the model to quickly access information. They started with a local database for proof of concept, while the production version utilizes a more generic database integrated with the CURE algorithm’s logic.
The results demonstrate clear business value. Mayo’s verification system eliminated nearly all data-retrieval-based errors in non-diagnostic uses, allowing them to deploy AI across their practice with confidence. More impressively, tasks that previously took clinicians 90 minutes now take just 10 minutes with AI assistance – an 89% time savings.
Callstrom told VentureBeat: “Our goal is to simplify the processing of content — how can I augment the abilities and simplify the work of the physician?”
Despite its success, reverse RAG isn’t without challenges. Implementing verified AI systems still requires thoughtful planning. Successful implementation also requires expert human oversight and careful engineering of AI workflows.
Fortunately, a human in the loop review system integrated with a reverse RAG system addresses these ethical AI concerns.
Building on initial success with administrative tasks, this approach can be extended to various areas within medical practices:
Mayo Clinic is exploring more advanced applications of AI in genomics and imaging to enhance treatment predictions. Potential applications extend beyond summarizing patient records to more complex tasks such as tumor classification, patient stratification, cancer gene discovery, and drug response prediction.
Mayo Clinic’s verification approach represents a significant advancement in making AI reliable for medical practices of all sizes. By ensuring all information is traceable to patient records, they’ve overcome one of the biggest barriers to AI adoption in healthcare settings.
This approach delivers the dual benefits every medical practice needs: improved accuracy and significant time savings for staff. The result is more time for patient care and greater confidence in administrative processes.
Are you ready to leverage the power of reverse RAG to make AI hallucinations a thing of the past?
Are you ready to bring the benefits of verified AI to your medical practice or healthcare organization?
Whether you’re managing a clinic, overseeing a physician group, or evaluating technology investments for healthcare portfolios, now is the time to implement AI systems that deliver both accuracy and efficiency.
Connect with me to discuss practical implementation strategies that fit your practice’s specific needs and budget.
Let’s make AI work reliably for your healthcare organization, just as it has for Mayo Clinic.
Bring Verifiable AI to your clinical practice or professional services business today, leveraging the power of reverse RAG to make AI hallucinations a thing of the past for your business.
