Evidence map›Paper›PMID 40699279›Full record

ReviewLa Radiologia medica2025

Hi ChatGPT, I am a Radiologist, How can you help me?

Davide Bellini, Riccardo Ferrari, Simone Vicini, Marco Rengo, Carlos Leon Saletti, Iacopo Carbone

Abstract readReview
PubMed Publisher
In one paragraph

Review in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Davide BelliniAcademic Diagnostic Imaging Division - I.C.O.T. Hospital, Department of Medical-Surgical Sciences and Biotechnologies, Faculty of Pharmacy and Medicine, University of Rome "Sapienza", Via F. Faggiana 1668, 04100, Latina, Italy. bellinidavide29@gmail.com.ORCID http://orcid.org/0000-0002-2731-6959
Riccardo FerrariDepartment of Emergency Radiology, Azienda Ospedaliera S. Camillo-Forlanini, Rome, Italy.
Simone ViciniAcademic Diagnostic Imaging Division - I.C.O.T. Hospital, Department of Medical-Surgical Sciences and Biotechnologies, Faculty of Pharmacy and Medicine, University of Rome "Sapienza", Via F. Faggiana 1668, 04100, Latina, Italy.
Marco RengoAcademic Diagnostic Imaging Division - I.C.O.T. Hospital, Department of Medical-Surgical Sciences and Biotechnologies, Faculty of Pharmacy and Medicine, University of Rome "Sapienza", Via F. Faggiana 1668, 04100, Latina, Italy.
Carlos Leon SalettiAcademic Diagnostic Imaging Division - I.C.O.T. Hospital, Department of Medical-Surgical Sciences and Biotechnologies, Faculty of Pharmacy and Medicine, University of Rome "Sapienza", Via F. Faggiana 1668, 04100, Latina, Italy.
Iacopo CarboneAcademic Diagnostic Imaging Division - I.C.O.T. Hospital, Department of Medical-Surgical Sciences and Biotechnologies, Faculty of Pharmacy and Medicine, University of Rome "Sapienza", Via F. Faggiana 1668, 04100, Latina, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review paper explores the integration of ChatGPT, a generative AI model developed by OpenAI, into radiological practices, focusing on its potential to enhance the operational efficiency of radiologists. ChatGPT operates on the GPT architecture, utilizing advanced machine learning techniques, including unsupervised pre-training and reinforcement learning, to generate human-like text responses. While AI applications in radiology predominantly focus on imaging acquisition, reconstruction, and interpretation-commonly embedded directly within hardware-the accessibility and functional breadth of ChatGPT make it a unique tool. This interview-based review should not be intended as a detailed evaluation of all ChatGPT features. Instead, it aims to test its utility in everyday radiological tasks through real-world examples. ChatGPT demonstrated strong capabilities in structuring radiology reports according to international guidelines (e.g., PI-RADS, CT reporting for diverticulitis), designing a complete research protocol, and performing advanced statistical analysis from Excel datasets, including ROC curve generation and intergroup comparison. Although not capable of directly interpreting DICOM images, ChatGPT provided meaningful assistance in image post-processing and interpretation when images were converted to standard formats. These findings highlight its current strengths and limitations as a supportive tool for radiologists.

Indexed as

Artificial IntelligenceMachine LearningRadiologistsRadiologyRadiology Information SystemsGenerative Artificial IntelligenceHumansArtificial intelligenceChatGPT applicationsMedical imagingRadiology workflow

Identifiers

What OpenQuestion 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 OpenQuestion graph.