Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) and machine learning (ML) research in neuroradiology is rapidly increasing, but most studies focus on development, not clinical use. Future work should prioritize external validation, bias, and explainability for practical AI integration.
Area Of Science
- Neuroradiology
- Artificial Intelligence (AI)
- Machine Learning (ML)
Background
- Growing interest in AI/ML within neuroradiology.
- Limited understanding of AI/ML research characteristics and evolution.
- Need to analyze trends and challenges in AI/ML publications.
Purpose Of The Study
- Characterize the emergence and evolution of AI/ML articles in neuroradiology.
- Provide an overview of trends, challenges, and future directions.
- Identify factors limiting clinical integration of AI/ML.
Main Methods
- Bibliometric analysis of the American Journal of Neuroradiology (1980-2022).
- Key terms: AI, ML, radiomics, deep learning, neural networks, GANs, object detection, NLP.
- Categorization: statistical modeling (type 1), AI/ML development (type 2), end-user application (type 3).
Main Results
- 182 articles identified; 79% non-integration (type 1 & 2), 21% end-user application (type 3).
- Publication growth ~5-fold in last 5 years, driven by non-integration articles.
- Minority of type 2 articles addressed bias (22%) and explainability (16%).
Conclusions
- Rapid increase in AI/ML publications, but limited end-user application.
- Areas for improvement: external validation, bias, and explainability in AI/ML development.
- Promote shift towards practical AI/ML integration in neuroradiology.
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