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Diagnostics
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AI in Radiology and Nuclear Medicine: Challenges and Opportunities
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AI in Radiology and Nuclear Medicine: Challenges and Opportunities

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 639

Special Issue Editor

Dr. Ioannis M. Tsougos

E-Mail Website
Guest Editor
Medical Physics Department, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
Interests: medical physics; radiology; nuclear medicine; artificial intelligence in biomedical imaging and radiotherapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled “AI in Radiology and Nuclear Medicine: Challenges and Opportunities”, delves into the ever-evolving intersection of artificial intelligence with the fields of radiology and nuclear medicine. It compiles a range of manuscripts that explore the cutting-edge applications of AI in enhancing diagnostic accuracy, optimizing workflow efficiency, and facilitating personalized treatment plans. From deep learning algorithms that augment image analysis to AI-driven decision support systems, this Special Issue highlights both the transformative potential and the inherent challenges faced in integrating AI into clinical practice.

Dr. Ioannis M. Tsougos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diagnosis
  • prognosis
  • radiology
  • nuclear medicine
  • artificial intelligence (AI)

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Published Papers (1 paper)

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Research

12 pages, 3173 KiB  
Article
Information Extraction from Lumbar Spine MRI Radiology Reports Using GPT4: Accuracy and Benchmarking Against Research-Grade Comprehensive Scoring
by Katharina Ziegeler, Virginie Kreutzinger, Michelle W. Tong, Cynthia T. Chin, Emma Bahroos, Po-Hung Wu, Noah Bonnheim, Aaron J. Fields, Jeffrey C. Lotz, Thomas M. Link and Sharmila Majumdar
Diagnostics 2025, 15(7), 930; https://doi.org/10.3390/diagnostics15070930 - 4 Apr 2025
Viewed by 494
Abstract
Background/Objectives: This study aimed to create a pipeline for standardized data extraction from lumbar-spine MRI radiology reports using a large language model (LLM) and assess the agreement of the extracted data with research-grade semi-quantitative scoring. Methods: We included a subset of [...] Read more.
Background/Objectives: This study aimed to create a pipeline for standardized data extraction from lumbar-spine MRI radiology reports using a large language model (LLM) and assess the agreement of the extracted data with research-grade semi-quantitative scoring. Methods: We included a subset of data from a multi-site NIH-funded cohort study of chronic low back pain (cLBP) participants. After initial prompt development, a secure application programming interface (API) deployment of OpenAIs GPT-4 was used to extract different classes of pathology from the clinical radiology report. Unsupervised UMAP and agglomerative clustering of the pathology terms’ embeddings provided insight into model comprehension for optimized prompt design. Model extraction was benchmarked against human extraction (gold standard) with F1 scores and false-positive and false-negative rates (FPR/FNR). Then, an expert MSK radiologist provided comprehensive research-grade scores of the images, and agreement with report-extracted data was calculated using Cohen’s kappa. Results: Data from 230 patients with cLBP were included (mean age 53.2 years, 54% women). The overall model performance for extracting data from clinical reports was excellent, with a mean F1 score of 0.96 across pathologies. The mean FPR was marginally higher than the FNR (5.1% vs. 3.0%). Agreement with comprehensive scoring was moderate (kappa 0.424), and the underreporting of lateral recess stenosis (FNR 63.6%) and overreporting of disc pathology (FPR 42.7%) were noted. Conclusions: LLMs can accurately extract highly detailed information on lumbar spine imaging pathologies from radiology reports. Moderate agreement between the LLM and comprehensive scores underscores the need for less subjective, machine-based data extraction from imaging. Full article
(This article belongs to the Special Issue AI in Radiology and Nuclear Medicine: Challenges and Opportunities)
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