AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study
Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial...
Gespeichert in:
Veröffentlicht in: | Journal of medical Internet research 2024-12, Vol.26 (7), p.e60684 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 7 |
container_start_page | e60684 |
container_title | Journal of medical Internet research |
container_volume | 26 |
creator | Stephan, Daniel Bertsch, Annika Burwinkel, Matthias Vinayahalingam, Shankeeth Al-Nawas, Bilal Kämmerer, Peer W Thiem, Daniel Ge |
description | Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation.
This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students.
A total of 100 dental students were tasked with analyzing panoramic radiographs and generating radiology reports manually or assisted by ChatGPT using a standardized prompt derived from a diagnostic checklist.
Reports generated by ChatGPT showed a high degree of textual similarity to reference reports; however, they often lacked critical diagnostic information typically included in reports authored by students. Despite this, the AI-generated reports were consistent in being error-free and matched the readability of student-generated reports.
The findings from this study suggest that ChatGPT has considerable potential for generating radiology reports, although it currently faces challenges in accuracy and reliability. This underscores the need for further refinement in the AI's prompt design and the development of robust validation mechanisms to enhance its use in clinical settings. |
doi_str_mv | 10.2196/60684 |
format | Article |
fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_crossref_primary_10_2196_60684</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A821086953</galeid><doaj_id>oai_doaj_org_article_a7248f92b3d4413aab3a8a4d6512c791</doaj_id><sourcerecordid>A821086953</sourcerecordid><originalsourceid>FETCH-LOGICAL-c360t-a051376d5d62aeace4e39f27e892c1e19d1aea9850a899e678fdb197fcfc54a23</originalsourceid><addsrcrecordid>eNptkt9rFDEQgBdRbK39F2RBBF-2ZpJsNvFFjrPWg4JSK74IYS4_7lJ2N2t27-D-e3O9WnogeUiY-eZjJkxRnAO5oKDEB0GE5M-KU-BMVlI28PzJ-6R4NY53hFDCFbwsTphqgJNGnha_Z4sy9OVn10_YljdoQ2zjalctuiHFbehX5bR25aX3wQTXm10ZfXnjhpimfe5XmNblfI3T1ffbj-U8dgMmnMLWlT-mjd29Ll54bEd3_nCfFT-_XN7Ov1bX364W89l1ZZggU4WkBtYIW1tB0aFx3DHlaeOkogYcKAs5rGRNUCrlRCO9XYJqvPGm5kjZWbE4eG3EOz2k0GHa6YhB3wdiWmnMDZvWaWwol17RJbOcA0NcMpTIraiBmkZBdn06uIbNsnPW5I9J2B5JjzN9WOtV3GqAhnDBWTa8fzCk-Gfjxkl3YTSubbF3cTNqBjyPAqDqjL49oCvMvYXex6w0e1zPJAUiRYYydfEfKh_rumBi73zI8aOCd4cCk-I4Jucf2wei99ui77clc2-ezvpI_VsP9hcWDbfK</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3148501195</pqid></control><display><type>article</type><title>AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>PubMed Central</source><creator>Stephan, Daniel ; Bertsch, Annika ; Burwinkel, Matthias ; Vinayahalingam, Shankeeth ; Al-Nawas, Bilal ; Kämmerer, Peer W ; Thiem, Daniel Ge</creator><creatorcontrib>Stephan, Daniel ; Bertsch, Annika ; Burwinkel, Matthias ; Vinayahalingam, Shankeeth ; Al-Nawas, Bilal ; Kämmerer, Peer W ; Thiem, Daniel Ge</creatorcontrib><description>Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation.
This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students.
A total of 100 dental students were tasked with analyzing panoramic radiographs and generating radiology reports manually or assisted by ChatGPT using a standardized prompt derived from a diagnostic checklist.
Reports generated by ChatGPT showed a high degree of textual similarity to reference reports; however, they often lacked critical diagnostic information typically included in reports authored by students. Despite this, the AI-generated reports were consistent in being error-free and matched the readability of student-generated reports.
The findings from this study suggest that ChatGPT has considerable potential for generating radiology reports, although it currently faces challenges in accuracy and reliability. This underscores the need for further refinement in the AI's prompt design and the development of robust validation mechanisms to enhance its use in clinical settings.</description><identifier>ISSN: 1438-8871</identifier><identifier>ISSN: 1439-4456</identifier><identifier>EISSN: 1438-8871</identifier><identifier>DOI: 10.2196/60684</identifier><identifier>PMID: 39714078</identifier><language>eng</language><publisher>Canada: Journal of Medical Internet Research</publisher><subject>Analysis ; Artificial Intelligence ; Automation ; Computational linguistics ; Humans ; Language processing ; Mechanization ; Medical imaging equipment ; Medical research ; Medicine, Experimental ; Natural language interfaces ; Original Paper ; Radiography, Panoramic - methods ; Radiology ; Radiology - methods ; Radiology - standards ; Radiology, Medical</subject><ispartof>Journal of medical Internet research, 2024-12, Vol.26 (7), p.e60684</ispartof><rights>Daniel Stephan, Annika Bertsch, Matthias Burwinkel, Shankeeth Vinayahalingam, Bilal Al-Nawas, Peer W Kämmerer, Daniel GE Thiem. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.12.2024.</rights><rights>COPYRIGHT 2024 Journal of Medical Internet Research</rights><rights>Daniel Stephan, Annika Bertsch, Matthias Burwinkel, Shankeeth Vinayahalingam, Bilal Al-Nawas, Peer W Kämmerer, Daniel GE Thiem. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.12.2024. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c360t-a051376d5d62aeace4e39f27e892c1e19d1aea9850a899e678fdb197fcfc54a23</cites><orcidid>0009-0000-4159-7745 ; 0000-0002-2679-3841 ; 0000-0002-8665-5803 ; 0000-0002-1671-3764 ; 0009-0007-2480-1231 ; 0009-0002-1780-4796 ; 0000-0002-4081-1487</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,723,776,780,860,881,2095,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39714078$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Stephan, Daniel</creatorcontrib><creatorcontrib>Bertsch, Annika</creatorcontrib><creatorcontrib>Burwinkel, Matthias</creatorcontrib><creatorcontrib>Vinayahalingam, Shankeeth</creatorcontrib><creatorcontrib>Al-Nawas, Bilal</creatorcontrib><creatorcontrib>Kämmerer, Peer W</creatorcontrib><creatorcontrib>Thiem, Daniel Ge</creatorcontrib><title>AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study</title><title>Journal of medical Internet research</title><addtitle>J Med Internet Res</addtitle><description>Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation.
This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students.
A total of 100 dental students were tasked with analyzing panoramic radiographs and generating radiology reports manually or assisted by ChatGPT using a standardized prompt derived from a diagnostic checklist.
Reports generated by ChatGPT showed a high degree of textual similarity to reference reports; however, they often lacked critical diagnostic information typically included in reports authored by students. Despite this, the AI-generated reports were consistent in being error-free and matched the readability of student-generated reports.
The findings from this study suggest that ChatGPT has considerable potential for generating radiology reports, although it currently faces challenges in accuracy and reliability. This underscores the need for further refinement in the AI's prompt design and the development of robust validation mechanisms to enhance its use in clinical settings.</description><subject>Analysis</subject><subject>Artificial Intelligence</subject><subject>Automation</subject><subject>Computational linguistics</subject><subject>Humans</subject><subject>Language processing</subject><subject>Mechanization</subject><subject>Medical imaging equipment</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Natural language interfaces</subject><subject>Original Paper</subject><subject>Radiography, Panoramic - methods</subject><subject>Radiology</subject><subject>Radiology - methods</subject><subject>Radiology - standards</subject><subject>Radiology, Medical</subject><issn>1438-8871</issn><issn>1439-4456</issn><issn>1438-8871</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNptkt9rFDEQgBdRbK39F2RBBF-2ZpJsNvFFjrPWg4JSK74IYS4_7lJ2N2t27-D-e3O9WnogeUiY-eZjJkxRnAO5oKDEB0GE5M-KU-BMVlI28PzJ-6R4NY53hFDCFbwsTphqgJNGnha_Z4sy9OVn10_YljdoQ2zjalctuiHFbehX5bR25aX3wQTXm10ZfXnjhpimfe5XmNblfI3T1ffbj-U8dgMmnMLWlT-mjd29Ll54bEd3_nCfFT-_XN7Ov1bX364W89l1ZZggU4WkBtYIW1tB0aFx3DHlaeOkogYcKAs5rGRNUCrlRCO9XYJqvPGm5kjZWbE4eG3EOz2k0GHa6YhB3wdiWmnMDZvWaWwol17RJbOcA0NcMpTIraiBmkZBdn06uIbNsnPW5I9J2B5JjzN9WOtV3GqAhnDBWTa8fzCk-Gfjxkl3YTSubbF3cTNqBjyPAqDqjL49oCvMvYXex6w0e1zPJAUiRYYydfEfKh_rumBi73zI8aOCd4cCk-I4Jucf2wei99ui77clc2-ezvpI_VsP9hcWDbfK</recordid><startdate>20241223</startdate><enddate>20241223</enddate><creator>Stephan, Daniel</creator><creator>Bertsch, Annika</creator><creator>Burwinkel, Matthias</creator><creator>Vinayahalingam, Shankeeth</creator><creator>Al-Nawas, Bilal</creator><creator>Kämmerer, Peer W</creator><creator>Thiem, Daniel Ge</creator><general>Journal of Medical Internet Research</general><general>JMIR Publications</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0000-4159-7745</orcidid><orcidid>https://orcid.org/0000-0002-2679-3841</orcidid><orcidid>https://orcid.org/0000-0002-8665-5803</orcidid><orcidid>https://orcid.org/0000-0002-1671-3764</orcidid><orcidid>https://orcid.org/0009-0007-2480-1231</orcidid><orcidid>https://orcid.org/0009-0002-1780-4796</orcidid><orcidid>https://orcid.org/0000-0002-4081-1487</orcidid></search><sort><creationdate>20241223</creationdate><title>AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study</title><author>Stephan, Daniel ; Bertsch, Annika ; Burwinkel, Matthias ; Vinayahalingam, Shankeeth ; Al-Nawas, Bilal ; Kämmerer, Peer W ; Thiem, Daniel Ge</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-a051376d5d62aeace4e39f27e892c1e19d1aea9850a899e678fdb197fcfc54a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Analysis</topic><topic>Artificial Intelligence</topic><topic>Automation</topic><topic>Computational linguistics</topic><topic>Humans</topic><topic>Language processing</topic><topic>Mechanization</topic><topic>Medical imaging equipment</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Natural language interfaces</topic><topic>Original Paper</topic><topic>Radiography, Panoramic - methods</topic><topic>Radiology</topic><topic>Radiology - methods</topic><topic>Radiology - standards</topic><topic>Radiology, Medical</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stephan, Daniel</creatorcontrib><creatorcontrib>Bertsch, Annika</creatorcontrib><creatorcontrib>Burwinkel, Matthias</creatorcontrib><creatorcontrib>Vinayahalingam, Shankeeth</creatorcontrib><creatorcontrib>Al-Nawas, Bilal</creatorcontrib><creatorcontrib>Kämmerer, Peer W</creatorcontrib><creatorcontrib>Thiem, Daniel Ge</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of medical Internet research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stephan, Daniel</au><au>Bertsch, Annika</au><au>Burwinkel, Matthias</au><au>Vinayahalingam, Shankeeth</au><au>Al-Nawas, Bilal</au><au>Kämmerer, Peer W</au><au>Thiem, Daniel Ge</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study</atitle><jtitle>Journal of medical Internet research</jtitle><addtitle>J Med Internet Res</addtitle><date>2024-12-23</date><risdate>2024</risdate><volume>26</volume><issue>7</issue><spage>e60684</spage><pages>e60684-</pages><issn>1438-8871</issn><issn>1439-4456</issn><eissn>1438-8871</eissn><abstract>Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation.
This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students.
A total of 100 dental students were tasked with analyzing panoramic radiographs and generating radiology reports manually or assisted by ChatGPT using a standardized prompt derived from a diagnostic checklist.
Reports generated by ChatGPT showed a high degree of textual similarity to reference reports; however, they often lacked critical diagnostic information typically included in reports authored by students. Despite this, the AI-generated reports were consistent in being error-free and matched the readability of student-generated reports.
The findings from this study suggest that ChatGPT has considerable potential for generating radiology reports, although it currently faces challenges in accuracy and reliability. This underscores the need for further refinement in the AI's prompt design and the development of robust validation mechanisms to enhance its use in clinical settings.</abstract><cop>Canada</cop><pub>Journal of Medical Internet Research</pub><pmid>39714078</pmid><doi>10.2196/60684</doi><orcidid>https://orcid.org/0009-0000-4159-7745</orcidid><orcidid>https://orcid.org/0000-0002-2679-3841</orcidid><orcidid>https://orcid.org/0000-0002-8665-5803</orcidid><orcidid>https://orcid.org/0000-0002-1671-3764</orcidid><orcidid>https://orcid.org/0009-0007-2480-1231</orcidid><orcidid>https://orcid.org/0009-0002-1780-4796</orcidid><orcidid>https://orcid.org/0000-0002-4081-1487</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1438-8871 |
ispartof | Journal of medical Internet research, 2024-12, Vol.26 (7), p.e60684 |
issn | 1438-8871 1439-4456 1438-8871 |
language | eng |
recordid | cdi_crossref_primary_10_2196_60684 |
source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; PubMed Central |
subjects | Analysis Artificial Intelligence Automation Computational linguistics Humans Language processing Mechanization Medical imaging equipment Medical research Medicine, Experimental Natural language interfaces Original Paper Radiography, Panoramic - methods Radiology Radiology - methods Radiology - standards Radiology, Medical |
title | AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-23T19%3A09%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=AI%20in%20Dental%20Radiology-Improving%20the%20Efficiency%20of%20Reporting%20With%20ChatGPT:%20Comparative%20Study&rft.jtitle=Journal%20of%20medical%20Internet%20research&rft.au=Stephan,%20Daniel&rft.date=2024-12-23&rft.volume=26&rft.issue=7&rft.spage=e60684&rft.pages=e60684-&rft.issn=1438-8871&rft.eissn=1438-8871&rft_id=info:doi/10.2196/60684&rft_dat=%3Cgale_doaj_%3EA821086953%3C/gale_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3148501195&rft_id=info:pmid/39714078&rft_galeid=A821086953&rft_doaj_id=oai_doaj_org_article_a7248f92b3d4413aab3a8a4d6512c791&rfr_iscdi=true |