Loading…
Best Practices for Large Language Models in Radiology
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context...
Saved in:
Published in: | arXiv.org 2024-12 |
---|---|
Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | |
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Bluethgen, Christian Dave Van Veen Zakka, Cyril Link, Katherine Fanous, Aaron Daneshjou, Roxana Frauenfelder, Thomas Langlotz, Curtis Gatidis, Sergios Chaudhari, Akshay |
description | At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies. |
format | article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3138996684</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3138996684</sourcerecordid><originalsourceid>FETCH-proquest_journals_31389966843</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQwdUotLlEIKEpMLslMTi1WSMsvUvBJLEpPBZJ56aWJQIZvfkpqTrFCZp5CUGJKZn5OfnolDwNrWmJOcSovlOZmUHZzDXH20C0oyi8sBZoYn5VfWpQHlIo3NjS2sLQ0M7MwMSZOFQBDijRz</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3138996684</pqid></control><display><type>article</type><title>Best Practices for Large Language Models in Radiology</title><source>Publicly Available Content Database</source><creator>Bluethgen, Christian ; Dave Van Veen ; Zakka, Cyril ; Link, Katherine ; Fanous, Aaron ; Daneshjou, Roxana ; Frauenfelder, Thomas ; Langlotz, Curtis ; Gatidis, Sergios ; Chaudhari, Akshay</creator><creatorcontrib>Bluethgen, Christian ; Dave Van Veen ; Zakka, Cyril ; Link, Katherine ; Fanous, Aaron ; Daneshjou, Roxana ; Frauenfelder, Thomas ; Langlotz, Curtis ; Gatidis, Sergios ; Chaudhari, Akshay</creatorcontrib><description>At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Best practice ; Communication ; Information management ; Large language models ; Machine learning ; Radiology</subject><ispartof>arXiv.org, 2024-12</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/3138996684?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25753,37012,44590</link.rule.ids></links><search><creatorcontrib>Bluethgen, Christian</creatorcontrib><creatorcontrib>Dave Van Veen</creatorcontrib><creatorcontrib>Zakka, Cyril</creatorcontrib><creatorcontrib>Link, Katherine</creatorcontrib><creatorcontrib>Fanous, Aaron</creatorcontrib><creatorcontrib>Daneshjou, Roxana</creatorcontrib><creatorcontrib>Frauenfelder, Thomas</creatorcontrib><creatorcontrib>Langlotz, Curtis</creatorcontrib><creatorcontrib>Gatidis, Sergios</creatorcontrib><creatorcontrib>Chaudhari, Akshay</creatorcontrib><title>Best Practices for Large Language Models in Radiology</title><title>arXiv.org</title><description>At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies.</description><subject>Best practice</subject><subject>Communication</subject><subject>Information management</subject><subject>Large language models</subject><subject>Machine learning</subject><subject>Radiology</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQwdUotLlEIKEpMLslMTi1WSMsvUvBJLEpPBZJ56aWJQIZvfkpqTrFCZp5CUGJKZn5OfnolDwNrWmJOcSovlOZmUHZzDXH20C0oyi8sBZoYn5VfWpQHlIo3NjS2sLQ0M7MwMSZOFQBDijRz</recordid><startdate>20241202</startdate><enddate>20241202</enddate><creator>Bluethgen, Christian</creator><creator>Dave Van Veen</creator><creator>Zakka, Cyril</creator><creator>Link, Katherine</creator><creator>Fanous, Aaron</creator><creator>Daneshjou, Roxana</creator><creator>Frauenfelder, Thomas</creator><creator>Langlotz, Curtis</creator><creator>Gatidis, Sergios</creator><creator>Chaudhari, Akshay</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20241202</creationdate><title>Best Practices for Large Language Models in Radiology</title><author>Bluethgen, Christian ; Dave Van Veen ; Zakka, Cyril ; Link, Katherine ; Fanous, Aaron ; Daneshjou, Roxana ; Frauenfelder, Thomas ; Langlotz, Curtis ; Gatidis, Sergios ; Chaudhari, Akshay</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_31389966843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Best practice</topic><topic>Communication</topic><topic>Information management</topic><topic>Large language models</topic><topic>Machine learning</topic><topic>Radiology</topic><toplevel>online_resources</toplevel><creatorcontrib>Bluethgen, Christian</creatorcontrib><creatorcontrib>Dave Van Veen</creatorcontrib><creatorcontrib>Zakka, Cyril</creatorcontrib><creatorcontrib>Link, Katherine</creatorcontrib><creatorcontrib>Fanous, Aaron</creatorcontrib><creatorcontrib>Daneshjou, Roxana</creatorcontrib><creatorcontrib>Frauenfelder, Thomas</creatorcontrib><creatorcontrib>Langlotz, Curtis</creatorcontrib><creatorcontrib>Gatidis, Sergios</creatorcontrib><creatorcontrib>Chaudhari, Akshay</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bluethgen, Christian</au><au>Dave Van Veen</au><au>Zakka, Cyril</au><au>Link, Katherine</au><au>Fanous, Aaron</au><au>Daneshjou, Roxana</au><au>Frauenfelder, Thomas</au><au>Langlotz, Curtis</au><au>Gatidis, Sergios</au><au>Chaudhari, Akshay</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Best Practices for Large Language Models in Radiology</atitle><jtitle>arXiv.org</jtitle><date>2024-12-02</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2024-12 |
issn | 2331-8422 |
language | eng |
recordid | cdi_proquest_journals_3138996684 |
source | Publicly Available Content Database |
subjects | Best practice Communication Information management Large language models Machine learning Radiology |
title | Best Practices for Large Language Models in Radiology |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T16%3A50%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Best%20Practices%20for%20Large%20Language%20Models%20in%20Radiology&rft.jtitle=arXiv.org&rft.au=Bluethgen,%20Christian&rft.date=2024-12-02&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3138996684%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_31389966843%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3138996684&rft_id=info:pmid/&rfr_iscdi=true |