Evidence map›Paper›PMID 41347921›Full record

ArticleRadiology. Imaging cancer2026

Zero-Shot PI-RADS Version 2.1 Scoring with ChatGPT-4 Turbo and Llama 3: Diagnostic Performance and Agreement with Abdominal Radiologists.

Negar Firoozeh, Domenico Mastrodicasa, Spencer Behr, Valdair Francisco Muglia, Antonio C Westphalen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Imaging cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Negar Firoozeh *Department of Radiology, University of Washington School of Medicine, 1959 NE Pacific St, BB308, Seattle, WA 981952.ORCID 0000-0002-5423-4406
Domenico Mastrodicasa *Department of Radiology, University of Washington School of Medicine, 1959 NE Pacific St, BB308, Seattle, WA 981952.ORCID 0000-0001-8227-0757
Spencer BehrDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco School of Medicine, Calif.ORCID 0000-0001-9400-0543
Valdair Francisco MugliaDepartment of Medical Imaging and Clinical Hematology and Oncology, University of São Paulo School of Medicine, Ribeirão Preto, Brazil.ORCID 0000-0002-4700-0599
Antonio C WestphalenDepartment of Radiology, University of Washington School of Medicine, 1959 NE Pacific St, BB308, Seattle, WA 981952.ORCID 0000-0001-5323-7632

Funding

Multi-Disciplinary Training Program in Cardiovascular Imaging at StanfordT32EB009035 · STANFORD UNIVERSITY · 2025 to 2025
$196k
NIBIB NIH HHS T32 EB009035
6 · The paper itself

Abstract

This retrospective, single-center study aimed to assess the diagnostic performance and agreement of two large language models (LLMs), ChatGPT-4 Turbo (OpenAI) and Llama 3 (Meta AI), in assigning Prostate Imaging Reporting and Data System (PI-RADS) scores to prostate MRI reports and to compare their performance with two abdominal radiologists. Structured prostate MRI reports (

Indexed as

Magnetic Resonance ImagingProstatic NeoplasmsAgedGenerative Artificial IntelligenceHumansMaleMiddle AgedObserver VariationProstateRadiologistsRetrospective StudiesLarge Language ModelOncologyProstate

Identifiers

PMID41347921
PMCPMC12862468

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.