Are We Missing the Environmental Factors in AI-Based Fall Risk Models?: A Systematic Review.

Background: Falls commonly occur in home environments where environmental conditions can contribute to fall risk. Identification and mitigation of environmental hazards are critical components of fall prevention. However, artificial intelligence (AI)-based fall prediction models have largely focused on individual-level predictors, with limited attention to home environmental hazards despite their modifiable role in fall risk. Objective: To systematically review how environmental factors are incorporated into existing AI-based fall risk prediction models and summarize reported AI approaches and model performance among community-dwelling older adults. Methods: This systematic review followed PRISMA guidelines. Six electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Web of Science, and Scopus) were searched from inception through December 2025. Eligible studies applied AI-based models to predict falls among older adults in community settings and incorporated environmental factors as model inputs. Results: Of more than 10,000 records identified, nine studies met final inclusion criteria. Six used supervised machine learning with structured data, while three employed computer vision or robotics-based approaches. Environmental factors were heterogeneously represented, ranging from checklist-based indicators to sensor- and vision-derived measures. When included, environmental features contributed meaningful information by improving discrimination or identifying actionable home hazards (AUC-ROC ranged from 0.67 to 0.76). Conclusion: Environmental factors remain underemphasized in AI-based fall prediction models. Greater integration of standardized and context-aware environmental information may enhance the relevance and preventive utility of AI-based fall risk prediction in community settings.