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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Towards accurate differential diagnosis with large language models.

Daniel McDuff¹, Mike Schaekermann², Tao Tu³

¹Google Research, Seattle, WA, USA. dmcduff@google.com.

|April 9, 2025

Summary

Articulate Medical Intelligence Explorer (AMIE), a new large language model, improved diagnostic accuracy for challenging medical cases. AMIE outperformed unassisted clinicians and enhanced diagnostic reasoning when used as an assistive tool.

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Area of Science:

Artificial Intelligence in Medicine
Clinical Decision Support Systems
Medical Diagnostics

Background:

Differential diagnosis is crucial for patient care, involving complex interpretation of clinical data.
Large language models (LLMs) offer potential for automating and assisting diagnostic reasoning.
Existing tools like search engines provide information but may not optimize diagnostic processes.

Purpose of the Study:

To introduce and evaluate the Articulate Medical Intelligence Explorer (AMIE), an LLM optimized for diagnostic reasoning.
To assess AMIE's standalone diagnostic performance.
To compare the effectiveness of AMIE as an assistive tool against standard resources and search engines for clinicians.

Main Methods:

Twenty clinicians evaluated 302 challenging medical cases from published reports.
Clinicians generated differential diagnoses with and without assistive tools.
Assistive conditions included search engines/standard resources, or AMIE plus standard resources.

Main Results:

AMIE's standalone performance exceeded unassisted clinicians' accuracy (59.1% vs. 33.6%).
Clinicians assisted by AMIE achieved higher differential diagnosis quality scores (51.7%) compared to those without AMIE (36.1%) or with search engines (44.4%).
AMIE-assisted clinicians generated more comprehensive differential diagnosis lists.

Conclusions:

AMIE demonstrates potential to significantly improve diagnostic reasoning and accuracy in complex medical cases.
The LLM shows promise as a valuable assistive tool for clinicians.
Further real-world evaluation is warranted to confirm AMIE's ability to enhance physician capabilities and patient access to expertise.