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Objectives <p>This study evaluated GPT-4’s accuracy in MRI sequence selection based on radiology request forms (RRFs), comparing its performance to radiology residents.</p> Materials and methods <p>This retrospective study included 100 RRFs across four subspecialties (cardiac imaging, neuroradiology, musculoskeletal, and oncology). GPT-4 and two radiology residents (R1: 2 years, R2: 5 years MRI experience) selected sequences based on each patient’s medical history and clinical questions. Considering imaging society guidelines, five board-certified specialized radiologists assessed protocols based on completeness, quality, and utility in consensus, using 5-point Likert scales. Clinical applicability was rated binarily by the institution’s lead radiographer.</p> Results <p>GPT-4 achieved median scores of 3 (1–5) for completeness, 4 (1–5) for quality, and 4 (1–5) for utility, comparable to R1 (3 (1–5), 4 (1–5), 4 (1–5); each <i>p</i> &gt; 0.05) but inferior to R2 (4 (1–5), 5 (1-5); <i>p</i> &lt; 0.01, respectively, and 5 (1–5); <i>p</i> &lt; 0.001). Subspecialty protocol quality varied: GPT-4 matched R1 (4 (2–4) vs. 4 (2–5), <i>p</i> = 0.20) and R2 (4 (2–5); <i>p</i> = 0.47) in cardiac imaging; showed no differences in neuroradiology (all 5 (1–5), <i>p</i> &gt; 0.05); scored lower than R1 and R2 in musculoskeletal imaging (3 (2–5) vs. 4 (3–5); <i>p</i> &lt; 0.01, and 5 (3–5); <i>p</i> &lt; 0.001); and matched R1 (4 (1–5) vs. 2 (1–4), <i>p</i> = 0.12) as well as R2 (5 (2–5);<i> p</i> = 0.20) in oncology. GPT-4-based protocols were clinically applicable in 95% of cases, comparable to R1 (95%) and R2 (96%).</p> Conclusion <p>GPT-4 generated MRI protocols with notable completeness, quality, utility, and clinical applicability, excelling in standardized subspecialties like cardiac and neuroradiology imaging while yielding lower accuracy in musculoskeletal examinations.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Long MRI acquisition times limit patient access, making accurate protocol selection crucial for efficient diagnostics, though it’s time-consuming and error-prone, especially for inexperienced residents.</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>GPT-4 generated MRI protocols of remarkable yet inconsistent quality, performing on par with an experienced resident in standardized fields, but moderately in musculoskeletal examinations.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The large language model can assist less experienced radiologists in determining detailed MRI protocols and counteract increasing workloads. The model could function as a semi-automatic tool, generating MRI protocols for radiologists’ confirmation, optimizing resource allocation, and improving diagnostics and cost-effectiveness.</i></p> Graphical Abstract <p></p>

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GPT-4 for automated sequence-level determination of MRI protocols based on radiology request forms from clinical routine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月8日

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  • Robert Terzis,
  • Kenan Kaya,
  • Thomas Schömig,
  • Jan Paul Janssen,
  • Andra-Iza Iuga,
  • Jonathan Kottlors,
  • Simon Lennartz,
  • Carsten Gietzen,
  • Cansin Gözdas,
  • Lukas Müller,
  • Robert Hahnfeldt,
  • David Maintz,
  • Thomas Dratsch,
  • Lenhard Pennig

摘要

Objectives

This study evaluated GPT-4’s accuracy in MRI sequence selection based on radiology request forms (RRFs), comparing its performance to radiology residents.

Materials and methods

This retrospective study included 100 RRFs across four subspecialties (cardiac imaging, neuroradiology, musculoskeletal, and oncology). GPT-4 and two radiology residents (R1: 2 years, R2: 5 years MRI experience) selected sequences based on each patient’s medical history and clinical questions. Considering imaging society guidelines, five board-certified specialized radiologists assessed protocols based on completeness, quality, and utility in consensus, using 5-point Likert scales. Clinical applicability was rated binarily by the institution’s lead radiographer.

Results

GPT-4 achieved median scores of 3 (1–5) for completeness, 4 (1–5) for quality, and 4 (1–5) for utility, comparable to R1 (3 (1–5), 4 (1–5), 4 (1–5); each p > 0.05) but inferior to R2 (4 (1–5), 5 (1-5); p < 0.01, respectively, and 5 (1–5); p < 0.001). Subspecialty protocol quality varied: GPT-4 matched R1 (4 (2–4) vs. 4 (2–5), p = 0.20) and R2 (4 (2–5); p = 0.47) in cardiac imaging; showed no differences in neuroradiology (all 5 (1–5), p > 0.05); scored lower than R1 and R2 in musculoskeletal imaging (3 (2–5) vs. 4 (3–5); p < 0.01, and 5 (3–5); p < 0.001); and matched R1 (4 (1–5) vs. 2 (1–4), p = 0.12) as well as R2 (5 (2–5); p = 0.20) in oncology. GPT-4-based protocols were clinically applicable in 95% of cases, comparable to R1 (95%) and R2 (96%).

Conclusion

GPT-4 generated MRI protocols with notable completeness, quality, utility, and clinical applicability, excelling in standardized subspecialties like cardiac and neuroradiology imaging while yielding lower accuracy in musculoskeletal examinations.

Key Points

Question Long MRI acquisition times limit patient access, making accurate protocol selection crucial for efficient diagnostics, though it’s time-consuming and error-prone, especially for inexperienced residents.

Findings GPT-4 generated MRI protocols of remarkable yet inconsistent quality, performing on par with an experienced resident in standardized fields, but moderately in musculoskeletal examinations.

Clinical relevance The large language model can assist less experienced radiologists in determining detailed MRI protocols and counteract increasing workloads. The model could function as a semi-automatic tool, generating MRI protocols for radiologists’ confirmation, optimizing resource allocation, and improving diagnostics and cost-effectiveness.

Graphical Abstract