A medication list can reveal more than just the drugs a patient is taking. Viewed alongside symptoms and possible diagnoses, it can provide essential insight into the broader clinical story. But for an AI agent to put those pieces together, it first needs a reliable way to understand and interpret how those pieces of data relate to one another.
In this short demo – less than two minutes – we show how a simple agent uses IMO Health’s clinical Knowledge Graph to explore those connections.
The scenario begins with a patient following up with neurology for chronic headaches. The clinical note includes symptoms, current medications, and several possible diagnoses. Using medication information already available in the record, the agent queries the Knowledge Graph to understand which conditions are commonly treated by those drugs.
The result isn’t a diagnosis. Instead, the agent gains clinically grounded evidence that helps it evaluate the possible diagnoses being considered. In this example, medications such as Sumatriptan and Topiramate provide stronger support for migraine-related diagnoses than for other headache conditions.
Just as important, the reasoning is transparent. The agent can surface the clinical relationships behind its conclusions, showing which medication-to-condition connections contributed to the outcome. Rather than relying on a black-box prediction, AI can incorporate explainable clinical evidence into its decision-making process.
That context can be useful to health technology teams, product leaders, clinicians, data specialists, and others shaping AI-enabled healthcare solutions. By making relationships across clinical concepts usable and visible, IMO Health’s Knowledge Graph can help teams build solutions and applications that reason with more relevant clinical information and make their inferences easier to understand.
