Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

Area of Science:

• Medical Informatics
• Artificial Intelligence in Medicine
• Paediatric Healthcare

Background:

• Paediatricians rely on extensive data (symptoms, labs, images) and experience for clinical decisions.
• Daily generation of large datasets from electronic health records and medical imaging necessitates advanced analytical tools.
• Computer-assisted intelligent drug design is a growing field.

Purpose of the Study:

• To review recent advancements in artificial intelligence (AI) research and clinical applications within paediatrics.
• To explore the value of AI in paediatric diagnosis, treatment, and health management.
• To summarize AI's role in intelligent medical image recognition and drug development.

Main Methods:

• Literature review of recent research on AI in paediatrics.
• Analysis of AI applications in clinical decision support systems.
• Examination of AI's impact on paediatric drug discovery and development.

Main Results:

• AI demonstrates significant value in intelligent diagnosis and treatment planning for paediatric patients.
• AI-powered tools are effective in paediatric medical image recognition.
• AI contributes to intelligent drug research and development and health management.

Conclusions:

• AI-aided decision support systems are increasingly valuable in paediatrics.
• AI integration in paediatrics aids in managing large datasets for improved clinical outcomes.
• Future research should focus on further clinical validation and implementation of AI in paediatric care.