Meeting Banner
Abstract #3386

Predicting MRI Protocol Using an Adapted Large Language Model

Peyman Shokrollahi1, Allison Li2, Iman Zare Estakhraji2, Akshay S. Chaudhari1, and Andreas M. Loening1
1Stanford University, Stanford, CA, United States, 2GE Healthcare, Mountain View, CA, United States

Synopsis

Keywords: Language Models, Machine Learning/Artificial Intelligence, Radiology Protocols, Decision Support System, Large Language Models, All-Body MR Protocols, Upstream Processing

Motivation: Developing a system to predict radiology protocols for incoming MRI orders.

Goal(s): Optimizing protocol selections by developing a decision-support system, enhancing efficiency and accuracy in selecting protocol elements

Approach: We adapted an open-source LLM using parameter-efficient fine tuning to ingest free-text inputs generated from ordering physicians to predict four key elements of MRI protocols: broad anatomical region, the focus target organ, contrast use, and protocol title.

Results: The model achieved high F1-scores (approximately 95%) for Region and Contrast predictions, while Focus and Protocol metrics were lower due to missing labels and broad anatomical terms, challenging accurate predictions.

Impact: The proposed system would offer radiologists a privacy-preserving decision-support tool, potentially reducing protocol mismatches, enhancing diagnostic accuracy, and optimizing workflow. Streamlining MRI protocoling aims to enhance diagnostic quality, safeguard patient health, expedite treatment, and lower healthcare costs.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords