The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

Area of Science:

• Gerontology
• Biomedical Engineering
• Public Health

Background:

• Falls represent a significant global public health issue, causing millions of injuries annually.
• Accurate fall risk assessment is crucial for effective prevention strategies.
• Artificial intelligence (AI) offers innovative data analysis capabilities for predictive modeling.

Purpose of the Study:

• To review and analyze existing research on AI applications in fall risk assessment.
• To examine AI's role in analyzing data related to postural control and fall prediction.

Main Methods:

• A systematic literature search was conducted across 6 databases.
• Inclusion criteria focused on AI methods, human sample data, and independent walking ability, published between 2018-2024.
• 22 relevant articles were selected from an initial pool of 3858.

Main Results:

• Data extraction primarily utilized functional assessments (82%) or medical records (18%).
• Various AI techniques were employed across the selected studies.
• AI-derived predictive models consistently achieved accuracy rates exceeding 70%.

Conclusions:

• AI is a powerful tool for developing accurate fall risk prediction models.
• The implementation of AI can lead to significant socioeconomic benefits through cost-effective, high-accuracy predictive solutions.
• AI facilitates advancements in fall prevention strategies.