Artificial intelligence applications in pediatric oncology diagnosis.

Area of Science:

• Medical technology
• Computer science
• Oncology

Background:

• Artificial intelligence (AI) algorithms demonstrate high accuracy and efficiency in various medical tasks.
• AI assists physicians in improving diagnostic efficiency for personalized treatment and surveillance.
• Machine learning and deep learning are core principles enabling AI to capture data, identify patterns, and make predictions.

Purpose of the Study:

• To provide insights into emerging AI applications for diagnosing pediatric oncology.
• To systematically review the current literature on AI in pediatric cancer diagnosis.
• To highlight AI's potential to overcome challenges in pediatric oncology research.

Main Methods:

• Systematic review of up-to-date literature on AI applications in pediatric oncology.
• Analysis of AI's working principles, including machine learning and deep learning.
• Exploration of solutions like convolutional neural networks (CNNs) for data scarcity.

Main Results:

• AI algorithms can provide timely diagnostic references using clinical and imaging data.
• AI advancements are reducing the dependence on large datasets and improving computational efficiency.
• Sharing CNN algorithms across institutions is a feasible approach to address sample size issues.

Conclusions:

• AI holds significant promise for advancing the diagnosis of pediatric oncology.
• Technical advancements in AI are poised to solve the sample size challenge in pediatric oncology.
• AI integration can lead to improved personalized treatment and surveillance for pediatric cancer patients.