Vectorized Knowledge Graph (VKG)-based AI systems offer superior precision, explainability, and integration with structured medical data, further enhanced by semantic understanding, making them highly effective for clinical decision support, research, and regulatory compliance. LLMs, while flexible and powerful for processing unstructured text, face challenges with accuracy, explainability, and reliability in medical contexts. For critical medical applications, Vectorized Knowledge Graphs provide a more robust, semantically rich, and trustworthy solution, though they require careful setup and maintenance.
When working with medical data, both Vectorized Knowledge Graphs (VKG) and Large Language Models (LLMs) offer distinct advantages. Often, the most powerful solutions, like MST’s, involve integrating both.
Technical Comparison
Data Structure
Vectorized Knowledge Graphs: Highly structured, explicit, semantic relationships between entities (e.g., “Drug A treats Disease B”, “Patient X has symptom Y), with entities and relationships also represented as dense vector embeddings to capture semantic similarity.
Large Language Models: Unstructured text, implicitly learned patterns and relationships from massive text corpuses, typically processed and understood via vector embeddings.
MST’s Combined Approach: Our LLM processes unstructured medical text (e.g., clinical notes, research papers) to extract and vectorize entities and relationships, which are then used to populate or update our vectorized knowledge graph by aligning new data semantically. Conversely, our VKG provides a structured framework, enhanced by semantic search capabilities, for the LLM to ground its understanding and generation, ensuring data is organized and interconnected for precise, vectorized, and explicit queries.
Knowledge Representation
Vectorized Knowledge Graphs: Explicit and interpretable. Knowledge is stored as nodes (entities) and edges (relationships), further enriched by vector embeddings that capture implicit semantic meaning and contextual similarity.
Large Language Models: Implicit and emergent from statistical patterns in training data; often a “black box,” primarily representing knowledge as high-dimensional vectors.
MST’s Combined Approach: Our VKG explicitly stores curated, factual medical knowledge (e.g., drug interactions, disease pathways) in a verifiable format. The LLM leverages its implicit understanding from vast text data to interpret nuanced language, fill in gaps, or infer new potential relationships. These new insights can then be vectorized and semantically matched or added to the VKG, creating a more comprehensive, accessible, and semantically navigable knowledge base.
Accuracy & Factual Consistency
Vectorized Knowledge Graphs: High accuracy and reliability due to structured, curated, and verifiable information. Vector embeddings enable semantic search for highly relevant facts. Can provide full transparency on how a result was derived.
Large Language Models: Prone to “hallucinations” (generating plausible but inaccurate information) as they prioritize linguistic fluency over factual accuracy.
MST’s Combined Approach: Our VKG serves as a factual anchor, providing verifiable information to the LLM. When an LLM generates a response, it can retrieve highly relevant supporting facts from the VKG using rapid semantic (vector) search, significantly reducing hallucinations and ensuring factual consistency crucial for medical applications. The LLM can then phrase these accurate facts in a human-friendly way.
Reasoning & Inferencing
Vectorized Knowledge Graphs: Excellent for logical, multi-hop reasoning (e.g., “What drugs treat diseases that cause symptom X?”). Vector embeddings also enable similarity-based inference and connection discovery, even without explicit links.
Large Language Models: Limited inherent reasoning capabilities; primarily pattern matching. Struggles with complex, multi-step logical deductions without external grounding.
MST’s Combined Approach: Our state-of-the-art VKG performs precise, multi-hop logical reasoning over its explicit structure, and also leverages its vector embeddings for semantic inference and pattern recognition (e.g., “find all drugs that treat conditions caused by a specific genetic mutation, or are semantically similar to effective treatments”). Our LLM interprets complex natural language queries for this reasoning and synthesizes the VKG’s logical and semantic output into coherent, clinically relevant explanations or recommendations, combining deep reasoning with intuitive understanding.
Explainability & Transparency
Vectorized Knowledge Graphs: Highly explainable; the path of reasoning through the explicit graph structure can be traced, and the semantic search results can be linked back to their source nodes/edges. Easy to audit and debug.
Large Language Models: Low explainability; difficult to understand why a particular output was generated.
MST’s Combined Approach: Our proprietary VKG provides an auditable and transparent path for how information was retrieved (both explicitly and via semantic search) and reasoned upon. Our LLM can then use this transparent path to generate clear, human-readable explanations for its responses, increasing trust and allowing clinicians to understand the rationale behind AI-driven suggestions, even when leveraging vector similarities.
Updating & Maintenance
Vectorized Knowledge Graphs: Easier to update and modify specific pieces of knowledge and their corresponding vector embeddings. New entities and relationships can be added incrementally.
Large Language Models: Requires expensive and time-consuming retraining (fine-tuning) to incorporate new knowledge or correct errors.
MST’s Combined Approach: Our VKG can be updated incrementally with new medical guidelines, research findings, or patient data, with the new information automatically vectorized and integrated. The LLM assists in this process by efficiently extracting new entities and relationships from unstructured medical literature, streamlining the maintenance and expansion of the VKG’s explicit and vectorized knowledge.
Data Integration
Vectorized Knowledge Graphs: Excellent for integrating disparate, heterogeneous data sources into a unified, semantically rich view, both through explicit links and through semantic similarity of their vectorized representations.
Large Language Models: Can process and summarize unstructured data from various sources but struggles with deep, semantic integration across diverse data formats without explicit structure.
MST’s Combined Approach: The MST VKG excels at integrating diverse, structured, and semi-structured medical data (EHRs, notes, clinical trials) into a unified semantic model. The LLM facilitates the extraction of knowledge from unstructured data (clinical notes, radiology reports) by converting it into vectors that are then seamlessly mapped to the VKG’s schema via semantic matching, enhancing holistic patient views and enabling more comprehensive data linking.
Contextual Understanding
Vectorized Knowledge Graphs: Provides rich, explicit contextual understanding by defining relationships between concepts, enhanced by vector embeddings that capture nuanced semantic context, enabling more precise retrieval based on implied meaning.
Large Language Models: Can process limited context windows at a time; struggles to “remember” an entire dataset or complex, long-range dependencies without external mechanisms.
MST’s Combined Approach: Our VKG provides rich, explicit contextual relationships, ensuring the LLM understands the precise meaning of medical terms within their specific domain. The LLM then contributes broad linguistic context and can infer nuances from conversational inputs. The vectorized component allows for rapid semantic retrieval of the most relevant contextual subgraphs, combining deep domain-specific context with general language understanding.
Handling Novelty & Rare Events
Vectorized Knowledge Graphs: While explicitly represented knowledge might be curated, the vectorized component allows for semantic comparison with novel concepts or rare events, enabling the system to find similarities and potential relationships even without direct links.
Large Language Models: Can generalize to novel situations and generate text for unseen scenarios based on learned patterns.
MST’s Combined Approach: Our LLM can process and generalize from novel or rare textual descriptions. By vectorizing this new information, the system can identify semantic similarities within the VKG, allowing for intelligent handling of emerging medical discoveries. New, validated insights identified by the LLM (e.g., emerging disease patterns) can then be explicitly added to the VKG, continuously expanding its formal and vectorized knowledge.
Scalability of Creation
Vectorized Knowledge Graphs: While KGs traditionally require manual effort, LLMs significantly automate and accelerate the extraction of entities and relationships from vast medical texts. By converting these into vectors for efficient storage and retrieval in a vectorized knowledge graph, it substantially reduces the manual burden, making VKG creation more scalable for vast domains like medicine.
Large Language Models: Can leverage vast amounts of unstructured text for training, potentially reducing manual curation effort for initial knowledge acquisition.
MST’s Combined Approach: While VKGs involve initial setup, our LLM, combined with vectorized techniques, significantly automates and accelerates the extraction of entities and relationships from vast medical texts, streamlining the large-scale construction and continuous population of the VKG. This drastically reduces the manual burden, making VKG creation and enrichment highly scalable for vast domains.
Natural Language Understanding (NLU) & Generation (NLG)
Vectorized Knowledge Graphs: Enhance NLU/NLG by providing precise, semantically rich factual context and structured data for the LLM. Vector embeddings allow for more nuanced retrieval of relevant information to inform generation.
Large Language Models: Exceptional NLU for understanding human language queries and NLG for generating fluent, human-like text.
MST’s Combined Approach: The MST LLM provides superior NLU to understand complex medical queries in natural language and excels at NLG to generate coherent, fluent, and contextually appropriate responses. Our VKG then provides the factual, structured, and semantically accessible data (via vector search) that ensures the generated text is accurate, grounded in medical reality, and precisely tailored to the query’s intent.
Bias & Ethical Concerns
Vectorized Knowledge Graphs: Biases can exist if the underlying data or ontology creation is biased, but the explicit graph structure allows for easier identification and mitigation. The vectorized layer can also be refined to reduce embedded biases by aligning with the curated knowledge.
Large Language Models: Can perpetuate and amplify biases present in their massive training datasets, which can be subtle and difficult to detect or mitigate.
MST’s Combined Approach: MST’s explicit VKG structure allows for easier identification and mitigation of biases in the represented knowledge and its relationships. The LLM’s outputs are then checked against the VKG’s factual and semantically structured data for accuracy and bias, enabling a more controlled, auditable, and ethically robust system than a standalone LLM, fostering more equitable AI applications in medicine.
Cost
Vectorized Knowledge Graphs: Initial setup can be resource-intensive, but the efficiency of vector search and LLM-assisted population can reduce long-term maintenance costs compared to purely manual KGs.
Large Language Models: High computational cost for training and large-scale inference; more accessible through API services.
MST’s Combined Approach: While requiring investment in both technologies, our LLM and vectorized techniques significantly reduce the labor cost of VKG creation and continuous enrichment. Our VKG, by providing highly precise and relevant context via semantic search for the LLM, can reduce the computational load and cost of complex LLM inference, resulting in a more cost-effective and sustainable solution for maintaining our high-quality, up-to-date system for critical medical applications.
Synergy:
Medical Search Technology is leveraging the combined strengths of both vectorized knowledge graphs and large language models. This synergistic approach allows for the best of both worlds: the robust, verifiable, and structured knowledge of VKGs, enhanced by semantic vector embeddings, can “ground” the often-hallucinatory tendencies of LLMs. This provides a factual backbone for their impressive natural language understanding and generation capabilities. This integration is crucial for tasks requiring high accuracy and explainability, such as clinical decision support, patient de-identification, fraud detection, and personalized medicine. By allowing LLMs to interpret complex natural language queries and then retrieve precise, evidence-based answers from a curated and semantically-searchable Vectorized Knowledge Graph, MST’s hybrid system can deliver more reliable, transparent, and contextually rich insights, driving significant advancements in healthcare.