Cutting Edge to Cutting Time: Can ChatGPT Improve the Radiologist's Reporting?
Radiology-structured reports (SR) have many advantages over free text (FT), but the wide implementation of SR is still lagging. A powerful tool such as GPT-4 can address this issue. We aim to employ a web-based reporting tool powered by GPT-4 capable of converting FT to SR and then evaluate its impa...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 346 - 357 |
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| Main Authors: | , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471484&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471484 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471484 184471484 184471484 10.1007/s10278-024-01196-6 184471484 ppf: 346 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cutting Edge to Cutting Time: Can ChatGPT Improve the Radiologist's Reporting? aug: au: Ahyad, Rayan A. Zaylaee, Yasir Hassan, Tasneem Khoja, Ohood Noorelahi, Yasser Alharthy, Ahmed Alabsi, Hatim Mimish, Reem Badeeb, Arwa affil: https://ror.org/02ma4wv74 Department of Radiology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia sug: subj: Radiologists Reports Chatbot Utilization Quality Assessment Human Male Female Retrospective Design Record Review Case Control Studies Tomography, X-Ray Computed Descriptive Statistics Data Analysis Software Confidence Intervals T-Tests Mann-Whitney U Test Male Female ab: Radiology-structured reports (SR) have many advantages over free text (FT), but the wide implementation of SR is still lagging. A powerful tool such as GPT-4 can address this issue. We aim to employ a web-based reporting tool powered by GPT-4 capable of converting FT to SR and then evaluate its impact on reporting time and report quality. Thirty abdominopelvic CT scans were reported by two radiologists across two sessions (15 scans each): a control session using traditional reporting methods and an AI-assisted session employing a GPT-4-powered web application to structure free text into structured reports. For each radiologist, the output included 15 control finalized reports, 15 AI-assisted pre-edits, and 15 post-edit finalized reports. Reporting turnaround times were assessed, including total reporting time (TRT) and case reporting time (TATc). Quality assessments were conducted by two blinded radiologists. TRT and TATc have decreased with the use of the AI-assisted reporting tool, although statistically not significant (p-value > 0.05). Mean TATc for RAD-1 decreased from 00:20:08 to 00:16:30 (hours:minutes:seconds) and TRT decreased from 05:02:00 to 04:08:00. Mean TATc for RAD-2 decreased from 00:12:04 to 00:10:04 and TRT decreased from 03:01:00 to 02:31:00. Quality scores of the finalized reports with and without AI-assistance were comparable with no significant differences. Adjusting the AI-assisted TATc by removing the editing time showed statistically significant results compared to the control for both radiologists (p-value < 0.05). The AI-assisted reporting tool can generate SR while reducing TRT and TATc without sacrificing report quality. Editing time is a potential area for further improvement. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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