Evidence map›Paper›PMID 37425598›Full record

ArticleCureus2023

Radiology Gets Chatty: The ChatGPT Saga Unfolds.

Harpreet Grewal, Gagandeep Dhillon, Varun Monga, Pranjal Sharma, Venkata S Buddhavarapu, Gurmanpreet Sidhu, Rahul Kashyap

Abstract read
In one paragraph

Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–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

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  15. Article
  16. Applications of Large Language Models in Pathology.Bioengineering (Basel, Switzerland) · 2024
    Review
  17. 21st century critical care medicine: An overview.World journal of critical care medicine · 2024
    Review
  18. AI in imaging: the regulatory landscape.The British journal of radiology · 2024
    Article
  19. Article
  20. Review
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

7 authors.

Harpreet GrewalRadiology, Florida State University College of Medicine, Pensacola, USA.
Gagandeep DhillonInternal Medicine, Baltimore Washington Medical Center, Glen Burnie, USA.
Varun MongaPsychiatry, Banner Health, Phoenix, USA.
Pranjal SharmaNephrology, Northeast Ohio Medical University, Rootstown, USA.
Venkata S BuddhavarapuHospital Medicine, Banner Health, Phoenix, USA.
Gurmanpreet SidhuPathology, Government Medical College, Patiala, Patiala, IND.
Rahul KashyapMedicine, Drexel University College of Medicine, Philadelphia, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As artificial intelligence (AI) continues to evolve and mature, it is increasingly finding applications in the field of healthcare, particularly in specialties like radiology that are data-heavy and image-focused. Language learning models (LLMs) such as OpenAI's Generative Pre-trained Transformer-4 (GPT-4) are new in the field of medicine and there is a paucity of literature regarding the possible utilities of GPT-4 given its novelty. We aim to present an in-depth exploration of the role of GPT-4, an advanced language model, in radiology. Giving the GPT-4 model prompts for generating reports, template generation, enhancing clinical decision-making, and suggesting captivating titles for research articles, patient communication, and education, can occasionally be quite generic, and at times, it may present factually incorrect content, which could lead to errors. The responses were then analyzed in detail regarding their potential utility in day-to-day radiologist workflow, patient education, and research processes. Further research is required to evaluate LLMs' accuracy and safety in clinical practice and to develop comprehensive guidelines for their implementation.

Indexed as

ai chatbotartificial intelligence in radiologychat gptgeneral radiologylanguage learning modelopenai

Identifiers

PMID37425598
PMCPMC10329466

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.