Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0's Accuracy with the American Journal of Neuroradiology's (AJNR) "Case of the Month"
The advent of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT 4.0, holds significant potential in healthcare, specifically in radiology. This study examined the accuracy of ChatGPT 4.0 (July 20, 2023, version) in solving diagnostic quizzes from the American Jo...
Gespeichert in:
| Veröffentlicht in: | Curēus (Palo Alto, CA) CA), 2023-08, Vol.15 (8), p.e43958-e43958 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e43958 |
|---|---|
| container_issue | 8 |
| container_start_page | e43958 |
| container_title | Curēus (Palo Alto, CA) |
| container_volume | 15 |
| creator | Suthar, Pokhraj P Kounsal, Avin Chhetri, Lavanya Saini, Divya Dua, Sumeet G |
| description | The advent of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT 4.0, holds significant potential in healthcare, specifically in radiology. This study examined the accuracy of ChatGPT 4.0 (July 20, 2023, version) in solving diagnostic quizzes from the American Journal of Neuroradiology's (AJNR) "Case of the Month." We evaluated the diagnostic accuracy of ChatGPT 4.0 when provided with a patient's history and imaging findings weekly over four weeks, using 140 cases from the AJNR "Case of the Month" portal (from November 2011 to July 2023). The overall diagnostic accuracy was found to be 57.86% (81 out of 140 cases). The diagnostic performance varied across brain, head and neck, and spine subgroups, with accuracy rates of 54.65%, 67.65%, and 55.0%, respectively. These findings suggest that AI models such as ChatGPT 4.0 could serve as useful adjuncts in radiological diagnostics, thus potentially enhancing patient care and revolutionizing medical education. |
| doi_str_mv | 10.7759/cureus.43958 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10516448</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2868672432</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2358-f2a359cdb85717fb9eb60882fb64aff0ddbe27300b11b7979e6c83138f9efd333</originalsourceid><addsrcrecordid>eNpdkd1u1DAQhSMEElXpHQ9glQu2Erv1T2I73FTRlpatSkFVubYcZ7xxlbUXOynaZ-Flm7AFFa5mpPnmzNGcLHtL8EKIojw1Q4QhLXJWFvJFdkAJl3NJZP7yWf86O0rpHmNMsKBY4IPsVxV7Z51xukMr30PXuTV4A2hWrU6Q8-hWNy50Yb37iCp0DrBF5-4BJjagZav7y293KF_g9wlVZrSgzQ79dH2L-hZQtYHojPboKgzRjxeCRTcwxBD_iI5rs-rq5vYEHS91ggmYFr8E37fHb7JXVncJjp7qYfb94tPd8vP8-uvlalldzw1lhZxbqllRmqaWhSDC1iXUHEtJbc1zbS1umhqoYBjXhNSiFCVwIxlh0pZgG8bYYXa2190O9QYaA76PulPb6DY67lTQTv078a5V6_CgCC4Iz3M5KsyeFGL4MUDq1cYlMz5TewhDUlRyyQXNGR3Rd_-h9_vnTJTAnBeUi5H6sKdMDClFsH_dEKymuNU-bvU7bvYI88adlQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2870665267</pqid></control><display><type>article</type><title>Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0's Accuracy with the American Journal of Neuroradiology's (AJNR) "Case of the Month"</title><source>PubMed Central Open Access</source><source>PubMed Central</source><creator>Suthar, Pokhraj P ; Kounsal, Avin ; Chhetri, Lavanya ; Saini, Divya ; Dua, Sumeet G</creator><creatorcontrib>Suthar, Pokhraj P ; Kounsal, Avin ; Chhetri, Lavanya ; Saini, Divya ; Dua, Sumeet G</creatorcontrib><description>The advent of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT 4.0, holds significant potential in healthcare, specifically in radiology. This study examined the accuracy of ChatGPT 4.0 (July 20, 2023, version) in solving diagnostic quizzes from the American Journal of Neuroradiology's (AJNR) "Case of the Month." We evaluated the diagnostic accuracy of ChatGPT 4.0 when provided with a patient's history and imaging findings weekly over four weeks, using 140 cases from the AJNR "Case of the Month" portal (from November 2011 to July 2023). The overall diagnostic accuracy was found to be 57.86% (81 out of 140 cases). The diagnostic performance varied across brain, head and neck, and spine subgroups, with accuracy rates of 54.65%, 67.65%, and 55.0%, respectively. These findings suggest that AI models such as ChatGPT 4.0 could serve as useful adjuncts in radiological diagnostics, thus potentially enhancing patient care and revolutionizing medical education.</description><identifier>ISSN: 2168-8184</identifier><identifier>EISSN: 2168-8184</identifier><identifier>DOI: 10.7759/cureus.43958</identifier><language>eng</language><publisher>Palo Alto: Cureus Inc</publisher><subject>Accuracy ; Artificial intelligence ; Brain research ; Chatbots ; Ethics ; Histology ; Other ; Patient assessment ; Radiology ; Students</subject><ispartof>Curēus (Palo Alto, CA), 2023-08, Vol.15 (8), p.e43958-e43958</ispartof><rights>Copyright © 2023, Suthar et al. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright © 2023, Suthar et al. 2023 Suthar et al.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2358-f2a359cdb85717fb9eb60882fb64aff0ddbe27300b11b7979e6c83138f9efd333</citedby><cites>FETCH-LOGICAL-c2358-f2a359cdb85717fb9eb60882fb64aff0ddbe27300b11b7979e6c83138f9efd333</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516448/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516448/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids></links><search><creatorcontrib>Suthar, Pokhraj P</creatorcontrib><creatorcontrib>Kounsal, Avin</creatorcontrib><creatorcontrib>Chhetri, Lavanya</creatorcontrib><creatorcontrib>Saini, Divya</creatorcontrib><creatorcontrib>Dua, Sumeet G</creatorcontrib><title>Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0's Accuracy with the American Journal of Neuroradiology's (AJNR) "Case of the Month"</title><title>Curēus (Palo Alto, CA)</title><description>The advent of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT 4.0, holds significant potential in healthcare, specifically in radiology. This study examined the accuracy of ChatGPT 4.0 (July 20, 2023, version) in solving diagnostic quizzes from the American Journal of Neuroradiology's (AJNR) "Case of the Month." We evaluated the diagnostic accuracy of ChatGPT 4.0 when provided with a patient's history and imaging findings weekly over four weeks, using 140 cases from the AJNR "Case of the Month" portal (from November 2011 to July 2023). The overall diagnostic accuracy was found to be 57.86% (81 out of 140 cases). The diagnostic performance varied across brain, head and neck, and spine subgroups, with accuracy rates of 54.65%, 67.65%, and 55.0%, respectively. These findings suggest that AI models such as ChatGPT 4.0 could serve as useful adjuncts in radiological diagnostics, thus potentially enhancing patient care and revolutionizing medical education.</description><subject>Accuracy</subject><subject>Artificial intelligence</subject><subject>Brain research</subject><subject>Chatbots</subject><subject>Ethics</subject><subject>Histology</subject><subject>Other</subject><subject>Patient assessment</subject><subject>Radiology</subject><subject>Students</subject><issn>2168-8184</issn><issn>2168-8184</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkd1u1DAQhSMEElXpHQ9glQu2Erv1T2I73FTRlpatSkFVubYcZ7xxlbUXOynaZ-Flm7AFFa5mpPnmzNGcLHtL8EKIojw1Q4QhLXJWFvJFdkAJl3NJZP7yWf86O0rpHmNMsKBY4IPsVxV7Z51xukMr30PXuTV4A2hWrU6Q8-hWNy50Yb37iCp0DrBF5-4BJjagZav7y293KF_g9wlVZrSgzQ79dH2L-hZQtYHojPboKgzRjxeCRTcwxBD_iI5rs-rq5vYEHS91ggmYFr8E37fHb7JXVncJjp7qYfb94tPd8vP8-uvlalldzw1lhZxbqllRmqaWhSDC1iXUHEtJbc1zbS1umhqoYBjXhNSiFCVwIxlh0pZgG8bYYXa2190O9QYaA76PulPb6DY67lTQTv078a5V6_CgCC4Iz3M5KsyeFGL4MUDq1cYlMz5TewhDUlRyyQXNGR3Rd_-h9_vnTJTAnBeUi5H6sKdMDClFsH_dEKymuNU-bvU7bvYI88adlQ</recordid><startdate>20230823</startdate><enddate>20230823</enddate><creator>Suthar, Pokhraj P</creator><creator>Kounsal, Avin</creator><creator>Chhetri, Lavanya</creator><creator>Saini, Divya</creator><creator>Dua, Sumeet G</creator><general>Cureus Inc</general><general>Cureus</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230823</creationdate><title>Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0's Accuracy with the American Journal of Neuroradiology's (AJNR) "Case of the Month"</title><author>Suthar, Pokhraj P ; Kounsal, Avin ; Chhetri, Lavanya ; Saini, Divya ; Dua, Sumeet G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2358-f2a359cdb85717fb9eb60882fb64aff0ddbe27300b11b7979e6c83138f9efd333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Artificial intelligence</topic><topic>Brain research</topic><topic>Chatbots</topic><topic>Ethics</topic><topic>Histology</topic><topic>Other</topic><topic>Patient assessment</topic><topic>Radiology</topic><topic>Students</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Suthar, Pokhraj P</creatorcontrib><creatorcontrib>Kounsal, Avin</creatorcontrib><creatorcontrib>Chhetri, Lavanya</creatorcontrib><creatorcontrib>Saini, Divya</creatorcontrib><creatorcontrib>Dua, Sumeet G</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Access via ProQuest (Open Access)</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Curēus (Palo Alto, CA)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Suthar, Pokhraj P</au><au>Kounsal, Avin</au><au>Chhetri, Lavanya</au><au>Saini, Divya</au><au>Dua, Sumeet G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0's Accuracy with the American Journal of Neuroradiology's (AJNR) "Case of the Month"</atitle><jtitle>Curēus (Palo Alto, CA)</jtitle><date>2023-08-23</date><risdate>2023</risdate><volume>15</volume><issue>8</issue><spage>e43958</spage><epage>e43958</epage><pages>e43958-e43958</pages><issn>2168-8184</issn><eissn>2168-8184</eissn><abstract>The advent of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT 4.0, holds significant potential in healthcare, specifically in radiology. This study examined the accuracy of ChatGPT 4.0 (July 20, 2023, version) in solving diagnostic quizzes from the American Journal of Neuroradiology's (AJNR) "Case of the Month." We evaluated the diagnostic accuracy of ChatGPT 4.0 when provided with a patient's history and imaging findings weekly over four weeks, using 140 cases from the AJNR "Case of the Month" portal (from November 2011 to July 2023). The overall diagnostic accuracy was found to be 57.86% (81 out of 140 cases). The diagnostic performance varied across brain, head and neck, and spine subgroups, with accuracy rates of 54.65%, 67.65%, and 55.0%, respectively. These findings suggest that AI models such as ChatGPT 4.0 could serve as useful adjuncts in radiological diagnostics, thus potentially enhancing patient care and revolutionizing medical education.</abstract><cop>Palo Alto</cop><pub>Cureus Inc</pub><doi>10.7759/cureus.43958</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2168-8184 |
| ispartof | Curēus (Palo Alto, CA), 2023-08, Vol.15 (8), p.e43958-e43958 |
| issn | 2168-8184 2168-8184 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10516448 |
| source | PubMed Central Open Access; PubMed Central |
| subjects | Accuracy Artificial intelligence Brain research Chatbots Ethics Histology Other Patient assessment Radiology Students |
| title | Artificial Intelligence (AI) in Radiology: A Deep Dive Into ChatGPT 4.0's Accuracy with the American Journal of Neuroradiology's (AJNR) "Case of the Month" |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T08%3A20%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20Intelligence%20(AI)%20in%20Radiology:%20A%20Deep%20Dive%20Into%20ChatGPT%204.0's%20Accuracy%20with%20the%20American%20Journal%20of%20Neuroradiology's%20(AJNR)%20%22Case%20of%20the%20Month%22&rft.jtitle=Cur%C4%93us%20(Palo%20Alto,%20CA)&rft.au=Suthar,%20Pokhraj%20P&rft.date=2023-08-23&rft.volume=15&rft.issue=8&rft.spage=e43958&rft.epage=e43958&rft.pages=e43958-e43958&rft.issn=2168-8184&rft.eissn=2168-8184&rft_id=info:doi/10.7759/cureus.43958&rft_dat=%3Cproquest_pubme%3E2868672432%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2870665267&rft_id=info:pmid/&rfr_iscdi=true |