Two independent AI models that can assist with multiple stages of patient management, from diagnosis to treatment decisions, are presented in Nature this week.
The systems—MIRA (Medical Intelligence for Reasoning and Action) and Google's AMIE (Articulate Medical Intelligence Explorer)—perform at least as well as physicians, demonstrating the potential for conversational AI tools to help with disease management.
In the first paper, Jakob Kather and colleagues describe MIRA, an AI model that has access to patient data in an isolated electronic health record system.
MIRA gathers information via chat with a patient AI agent whose responses match documented histories taken from clinical notes.
It achieved an average diagnostic accuracy of 87.8%, compared with 78.1% from a panel of six physicians across specialties.
In the second paper, Mike Schaekermann and colleagues describe AMIE, an LLM-based system optimized for clinical management and conversations.
The model can perform continuous reasoning over multiple patient visits to map the progression of disease and responses to treatment.
AMIE uses Gemini to analyze the information retrieved from the patient and align its output with relevant and up-to-date clinical practice guidelines and drug formularies (lists of approved, clinically preferred medications).
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