Current applications and future potential of ChatGPT in radiology: A systematic review.
ChatGPT
artificial intelligence
large language mode
machine learning
radiology
Journal
Journal of medical imaging and radiation oncology
ISSN: 1754-9485
Titre abrégé: J Med Imaging Radiat Oncol
Pays: Australia
ID NLM: 101469340
Informations de publication
Date de publication:
19 Jan 2024
19 Jan 2024
Historique:
received:
22
09
2023
accepted:
29
12
2023
medline:
20
1
2024
pubmed:
20
1
2024
entrez:
20
1
2024
Statut:
aheadofprint
Résumé
This study aimed to comprehensively evaluate the current utilization and future potential of ChatGPT, an AI-based chat model, in the field of radiology. The primary focus is on its role in enhancing decision-making processes, optimizing workflow efficiency, and fostering interdisciplinary collaboration and teaching within healthcare. A systematic search was conducted in PubMed, EMBASE and Web of Science databases. Key aspects, such as its impact on complex decision-making, workflow enhancement and collaboration, were assessed. Limitations and challenges associated with ChatGPT implementation were also examined. Overall, six studies met the inclusion criteria and were included in our analysis. All studies were prospective in nature. A total of 551 chatGPT (version 3.0 to 4.0) assessment events were included in our analysis. Considering the generation of academic papers, ChatGPT was found to output data inaccuracies 80% of the time. When ChatGPT was asked questions regarding common interventional radiology procedures, it contained entirely incorrect information 45% of the time. ChatGPT was seen to better answer US board-style questions when lower order thinking was required (P = 0.002). Improvements were seen between chatGPT 3.5 and 4.0 in regard to imaging questions with accuracy rates of 61 versus 85%(P = 0.009). ChatGPT was observed to have an average translational ability score of 4.27/5 on the Likert scale regarding CT and MRI findings. ChatGPT demonstrates substantial potential to augment decision-making and optimizing workflow. While ChatGPT's promise is evident, thorough evaluation and validation are imperative before widespread adoption in the field of radiology.
Identifiants
pubmed: 38243605
doi: 10.1111/1754-9485.13621
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 Royal Australian and New Zealand College of Radiologists.
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