Artificial intelligence for pediatric neuroimaging

Area of Science:

• Artificial intelligence in medical imaging
• Pediatric neuroimaging applications
• Machine learning in healthcare

Background:

• AI is revolutionizing healthcare, with most FDA-cleared tools in radiology, particularly neuroradiology.
• Pediatric radiology lags due to data limitations, age variability, and ethical concerns.
• Bridging the research-to-clinical gap is challenging in pediatrics due to market size.

Purpose of the Study:

• To review AI principles, pitfalls, and applications in pediatric neuroimaging.
• To inform pediatric neuroradiology progress using adult neuroradiology insights.
• To discuss the potential of generative AI and human oversight in mitigating risks.

Main Methods:

• Review of AI technical principles and pitfalls.
• Analysis of current clinical AI tools in pediatric neuroimaging.
• Exploration of research advances and future directions.

Main Results:

• AI in radiology, especially neuroradiology, is rapidly advancing.
• Generative AI offers solutions for data limitations and complex pattern recognition.
• Human expert oversight is crucial for mitigating AI risks like bias and errors.

Conclusions:

• AI holds significant promise for advancing pediatric neuroimaging.
• Addressing challenges like data scarcity and ethical concerns is vital for AI adoption.
• Collaborative efforts and human oversight will ensure safe and effective AI integration.