Core Differences Between VKG and LLM Technologies

In regulated domains like healthcare, transparency and explainability aren’t optional—they’re essential.

MST’s VKG ensures that every result is fully traceable to its source. Each output is accompanied by a clear path through a data provenance map, showing exactly how it was derived from the original data.

In contrast, large language models (LLMs) function as black boxes. While their responses may be fluent, they lack direct links to source material, making results difficult to audit or verify—an unacceptable risk in clinical decision-making and regulatory compliance.

Critically, VKGs never rewrite or alter the original medical record, preserving the legal and ethical integrity of patient data. LLMs, by design, regenerate content—a behavior that can introduce inaccuracies or misinterpretations.

Most AI applications in healthcare focus on detecting low-frequency signals such as diagnoses, symptoms, or abnormal lab values. To be clinically useful, these outputs must minimize false positives. That demands deep contextual understanding—not only of a single health encounter but across the patient’s entire longitudinal record.