Artificial intelligence (AI) shows promise in streamlining MRI workflows by reducing radiologists’ workload and improving diagnostic accuracy. Despite MRI’s extensive clinical use, systematic evaluation of AI-driven productivity gains in MRI remains limited. This review addresses that gap by synthesizing evidence on how AI can shorten scanning and reading times, optimize worklist triage, and automate segmentation. On 15 November 2024, we searched PubMed, EMBASE, MEDLINE, Web of Science, Google Scholar, and Cochrane Library for English-language studies published between 2000 and 15 November 2024, focusing on AI applications in MRI. Additional searches of grey literature were conducted. After screening for relevance and full-text review, 66 studies met inclusion criteria. Extracted data included study design, AI techniques, and productivity-related outcomes such as time savings and diagnostic accuracy. The included studies were categorized into five themes: reducing scan times, automating segmentation, optimizing workflow, decreasing reading times, and general time-saving or workload reduction. Convolutional neural networks (CNNs), especially architectures like ResNet and U-Net, were commonly used for tasks ranging from segmentation to automated reporting. A few studies also explored machine learning-based automation software and, more recently, large language models. Although most demonstrated gains in efficiency and accuracy, limited external validation and dataset heterogeneity could reduce broader adoption. AI applications in MRI offer potential to enhance radiologist productivity, mainly through accelerated scans, automated segmentation, and streamlined workflows. Further research, including prospective validation and standardized metrics, is needed to enable safe, efficient, and equitable deployment of AI tools in clinical MRI practice.

Background: Large language models (LLMs) have emerged as powerful tools in healthcare. In diagnostic radiology, LLMs can assist in the computation of the Spine Instability Neoplastic Score (SINS), which is a critical tool for assessing spinal metastases. However, the accuracy of LLMs in calculating the SINS based on radiological reports remains underexplored. Objective: This study evaluates the accuracy of two institutional privacy-preserving LLMs—Claude 3.5 and Llama 3.1—in computing the SINS from radiology reports and electronic medical records, comparing their performance against clinician readers. Methods: A retrospective analysis was conducted on 124 radiology reports from patients with spinal metastases. Three expert readers established a reference standard for the SINS calculation. Two orthopaedic surgery residents and two LLMs (Claude 3.5 and Llama 3.1) independently calculated the SINS. The intraclass correlation coefficient (ICC) was used to measure the inter-rater agreement for the total SINS, while Gwet’s Kappa was used to measure the inter-rater agreement for the individual SINS components. Results: Both LLMs and clinicians demonstrated almost perfect agreement with the reference standard for the total SINS. Between the two LLMs, Claude 3.5 (ICC = 0.984) outperformed Llama 3.1 (ICC = 0.829). Claude 3.5 was also comparable to the clinician readers with ICCs of 0.926 and 0.986, exhibiting a near-perfect agreement across all individual SINS components [0.919–0.990]. Conclusions: Claude 3.5 demonstrated high accuracy in calculating the SINS and may serve as a valuable adjunct in clinical workflows, potentially reducing clinician workload while maintaining diagnostic reliability. However, variations in LLM performance highlight the need for further validation and optimisation before clinical integration.

Background: Privacy-preserving large language models (PP-LLMs) hold potential for assisting clinicians with documentation. We evaluated a PP-LLM to improve the clinical information on radiology request forms for musculoskeletal magnetic resonance imaging (MRI) and to automate protocoling, which ensures that the most appropriate imaging is performed. Methods: The present retrospective study included musculoskeletal MRI radiology request forms that had been randomly collected from June to December 2023. Studies without electronic medical record (EMR) entries were excluded. An institutional PP-LLM (Claude Sonnet 3.5) augmented the original radiology request forms by mining EMRs, and, in combination with rule-based processing of the LLM outputs, suggested appropriate protocols using institutional guidelines. Clinical information on the original and PP-LLM radiology request forms were compared with use of the RI-RADS (Reason for exam Imaging Reporting and Data System) grading by 2 musculoskeletal (MSK) radiologists independently (MSK1, with 13 years of experience, and MSK2, with 11 years of experience). These radiologists established a consensus reference standard for protocoling, against which the PP-LLM and of 2 second-year board-certified radiologists (RAD1 and RAD2) were compared. Inter-rater reliability was assessed with use of the Gwet AC1, and the percentage agreement with the reference standard was calculated. Results: Overall, 500 musculoskeletal MRI radiology request forms were analyzed for 407 patients (202 women and 205 men with a mean age [and standard deviation] of 50.3 ± 19.5 years) across a range of anatomical regions, including the spine/pelvis (143 MRI scans; 28.6%), upper extremity (169 scans; 33.8%) and lower extremity (188 scans; 37.6%). Two hundred and twenty-two (44.4%) of the 500 MRI scans required contrast. The clinical information provided in the PP-LLM-augmented radiology request forms was rated as superior to that in the original requests. Only 0.4% to 0.6% of PP-LLM radiology request forms were rated as limited/deficient, compared with 12.4% to 22.6% of the original requests (p < 0.001). Almost-perfect inter-rater reliability was observed for LLM-enhanced requests (AC1 = 0.99; 95% confidence interval [CI], 0.99 to 1.0), compared with substantial agreement for the original forms (AC1 = 0.62; 95% CI, 0.56 to 0.67). For protocoling, MSK1 and MSK2 showed almost-perfect agreement on the region/coverage (AC1 = 0.96; 95% CI, 0.95 to 0.98) and contrast requirement (AC1 = 0.98; 95% CI, 0.97 to 0.99). Compared with the consensus reference standard, protocoling accuracy for the PP-LLM was 95.8% (95% CI, 94.0% to 97.6%), which was significantly higher than that for both RAD1 (88.6%; 95% CI, 85.8% to 91.4%) and RAD2 (88.2%; 95% CI, 85.4% to 91.0%) (p < 0.001 for both). Conclusions: Musculoskeletal MRI request form augmentation with an institutional LLM provided superior clinical information and improved protocoling accuracy compared with clinician requests and non-MSK-trained radiologists. Institutional adoption of such LLMs could enhance the appropriateness of MRI utilization and patient care. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Background: Magnetic resonance imaging (MRI) spine studies frequently reveal extraspinal findings (ESFs) that require further evaluation; yet, the current process of manually reviewing radiology reports and navigating electronic medical records (EMRs) is time-consuming, labor-intensive, and prone to human error. Objective: To address this challenge, we propose using a privacy-preserving large language model (PP-LLM) to automate the identification, classification, and referral assessment of ESFs. Methods: A retrospective analysis of 405 consecutive MRI spine reports from the National University Hospital database, covering February to June 2024, was conducted. Two independent clinicians reviewed the reports and cross-referenced them with EMRs to identify ESFs from the imaging reports. The CT Extracolonic Findings Reporting and Data System (C-RADS) was adapted to determine the clinical significance of ESFs and whether specialty referral was required. The PP-LLM was designed to extract these findings, differentiate between new and preexisting conditions, classify their clinical significance, and generate appropriate referrals. Results: A total of 405 MRI spine reports were initially identified. Five reports were excluded because no relevant EMRs were available, leaving 400 MRI reports from 395 patients for analysis. Among 395 patients (male: 48.1%, n=190; female: 51.9%, n=205; age: mean 54.7, SD 16.7, range 17-89 years), 163 (41.3%) had no ESFs and 232 (58.7%) were reported to have had at least one ESF. A total of 401 ESFs were identified, with the most common findings being renal (n=128, 31.9%), gynecological (n=110, 27.4%), and endocrine-related (n=48, 12%). The PP-LLM correctly detected 99.8% (400/401) of all ESFs and correctly identified all clinically urgent findings (3%, 12/401 of the total, for example, aortic dissection). It misclassified 2% (8/401) of cases into lower C-RADS categories, potentially downgrading clinically significant findings (eg, paranasal sinus mucosal thickening), and 1% (4/401) into higher C-RADS categories, upgrading clinically insignificant findings (eg, dependent changes in the lungs). Additionally, it achieved 98.5% accuracy in distinguishing new from preexisting findings and 96.3% accuracy in assigning the correct specialty referral decision (whether referral is needed, and correct subspecialty referral suggested). The PP-LLM demonstrated almost perfect agreement with the reference standard (Gwet κ=0.959, 95% CI 0.937-0.981), comparable to two human readers (reader 1: κ=0.943; reader 2: κ=0.934), with no statistically significant difference in C-RADS classification accuracy. Notably, it completed the analysis of each report in less than 5 seconds. Conclusions: The PP-LLM demonstrated high accuracy and efficiency in automating the identification and classification of ESFs in MRI spine reports. By integrating this AI-driven automation into clinical workflows, this technology has the potential to enhance efficiency, reduce clinician administrative burden, ensure timely specialist referrals, and improve patient care.

BACKGROUND MRI spine studies frequently reveal extra-spinal findings (ESFs) that require further evaluation, yet the current process of manually reviewing radiology reports and navigating electronic medical records (EMRs) is time-consuming, labour-intensive and prone to human error. OBJECTIVE To address this challenge, we propose using a privacy-preserving large language model (PP-LLM) to automate the identification, classification, and referral assessment of extra-spinal findings (ESFs). METHODS A retrospective analysis of 400 consecutive MRI spine reports from the National University Hospital (NUH) database, covering February to June 2024, was conducted. Two independent clinicians reviewed the reports and cross-referenced them with EMRs to identify ESFs from the imaging reports. The CT Extracolonic Findings Reporting and Data System (C-RADS system), was adapted to determine the clinical significance of ESFs and whether specialty referral was required. The PP-LLM was designed to extract these findings, differentiate between new and pre-existing conditions, classify their clinical significance, and generate appropriate referrals. RESULTS A retrospective analysis of 400 consecutive MRI spine reports from the National University Hospital (NUH) database, covering February to June 2024, was conducted. Two independent clinicians reviewed the reports and cross-referenced them with EMRs to identify ESFs from the imaging reports. The CT Extracolonic Findings Reporting and Data System (C-RADS system), was adapted to determine the clinical significance of ESFs and whether specialty referral was required. The PP-LLM was designed to extract these findings, differentiate between new and pre-existing conditions, classify their clinical significance, and generate appropriate referrals. CONCLUSIONS The PP-LLM demonstrated high accuracy and efficiency in automating the identification and classification of extra-spinal findings in MRI spine reports. By integrating this AI-driven automation into clinical workflows, this technology has the potential to enhance efficiency, reduce clinician administrative burden, ensure timely specialist referrals and improve patient care. CLINICALTRIAL NA