Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Bone age estimation (BAE) is crucial for diagnosing pediatric growth disorders and has medicolegal importance in identification and criminal responsibility.
• Artificial Intelligence (AI) offers potential to improve diagnostic accuracy and efficiency in medical imaging analysis.

Purpose of the Study:

• To review AI-based algorithms for radiologic bone age assessment.
• To conduct a novel Strength, Weakness, Opportunities, and Challenges (SWOC) analysis of AI in BAE.
• To propose strategies for AI implementation in forensic and medicolegal BAE.

Main Methods:

• Review of existing AI algorithms for radiologic BAE.
• Qualitative SWOC analysis of AI application in BAE.
• Development of implementation strategies based on SWOC findings.

Main Results:

• AI algorithms show promise in enhancing BAE by reducing observer variability and analytical time.
• SWOC analysis identified key factors influencing AI adoption in BAE.
• Specific strategies were formulated for successful AI integration in forensic and medicolegal contexts.

Conclusions:

• AI holds significant potential to revolutionize bone age estimation, offering improved accuracy and efficiency.
• The SWOC analysis provides a framework for understanding and overcoming barriers to AI implementation in BAE.
• Strategic implementation is key for leveraging AI in clinical and medicolegal BAE settings.