Journal article
Capability of multimodal large language models to interpret pediatric radiological images
Pediatric radiology, Vol.54(10), pp.1729-1737
08/12/2024
DOI: 10.1007/s00247-024-06025-0
PMID: 39133401
Abstract
Background
There is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of large language models (LLMs) like ChatGPT can process image and video input in addition to text. They are thus theoretically capable of providing impressions of input radiological images.
Objective
To assess the ability of multimodal LLMs to interpret pediatric radiological images.
Materials and methods
Thirty medically significant cases were collected and submitted to GPT-4 (OpenAI, San Francisco, CA), Gemini 1.5 Pro (Google, Mountain View, CA), and Claude 3 Opus (Anthropic, San Francisco, CA) with a short history for a total of 90 images. AI responses were recorded and independently assessed for accuracy by a resident and attending physician. 95% confidence intervals were determined using the adjusted Wald method.
Results
Overall, the models correctly diagnosed 27.8% (25/90) of images (95% CI=19.5-37.8%), were partially correct for 13.3% (12/90) of images (95% CI=2.7-26.4%), and were incorrect for 58.9% (53/90) of images (95% CI=48.6-68.5%).
Conclusion
Multimodal LLMs are not yet capable of interpreting pediatric radiological images.
Details
- Title: Subtitle
- Capability of multimodal large language models to interpret pediatric radiological images
- Creators
- Thomas P. Reith - University of IowaDonna M. D'Alessandro - Univ Iowa Hosp & Clin, Dept Pediat, Iowa City, IA 52242 USAMichael P. D'Alessandro - Univ Iowa Hosp & Clin, Dept Radiol, Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Pediatric radiology, Vol.54(10), pp.1729-1737
- Publisher
- Springer Nature
- DOI
- 10.1007/s00247-024-06025-0
- PMID
- 39133401
- ISSN
- 0301-0449
- eISSN
- 1432-1998
- Number of pages
- 9
- Language
- English
- Electronic publication date
- 08/12/2024
- Academic Unit
- Radiology; Stead Family Department of Pediatrics; General Pediatrics and Adolescent Medicine
- Record Identifier
- 9984697659402771
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