Clinical bottom line
AI-driven fall risk prediction tools offer promising adjuncts to traditional assessment methods in older adult rehabilitation, enabling more personalized and timely interventions. Early evidence suggests these technologies can improve identification of high-risk individuals and tailor multifactorial prevention strategies, potentially reducing fall incidence and associated morbidity. However, integration into clinical workflows requires validation of predictive accuracy, user acceptability, and demonstration of meaningful impact on patient outcomes.
What the evidence shows
Falls are a leading cause of injury and functional decline in older adults, with multifactorial risk factors including balance impairment, muscle weakness, sensory deficits, and environmental hazards (Tiedemann et al., 2021; Lamb et al., 2021). Conventional fall risk screening and assessment rely on clinical judgment and standardized tools, but these may lack sensitivity or fail to capture dynamic risk changes (Tiedemann et al., 2021).
Recent advances in artificial intelligence (AI) have enabled development of personalized fall risk prediction models that integrate diverse data sources such as gait parameters, sensor-derived movement patterns, medical history, and environmental factors (Xu et al., 2025). Xu et al. (2025) demonstrated that AI algorithms could stratify older adults by fall risk with improved accuracy compared to traditional methods, facilitating targeted intervention delivery. These models can continuously update risk profiles, allowing dynamic monitoring and adjustment of prevention strategies.
Multifactorial fall prevention programs combining exercise, home hazard modification, vision correction, and medication review remain the cornerstone of fall risk reduction (Lamb et al., 2021; Hill et al., 2021). AI-driven risk stratification may enhance these programs by identifying individuals most likely to benefit and optimizing resource allocation (Xu et al., 2025). Additionally, AI can support adherence by providing personalized feedback and reminders, addressing barriers to uptake highlighted by Tiedemann et al. (2021).
Despite these advances, evidence on the clinical effectiveness of AI-augmented fall prevention interventions remains limited. Most studies to date focus on algorithm development and validation in controlled settings rather than pragmatic trials assessing impact on fall rates or functional outcomes. Moreover, populations studied often exclude those with cognitive impairment or multimorbidity, limiting generalizability.
Caveats and uncertainty
AI-driven fall risk prediction is an emerging field with several limitations. Algorithms require large, high-quality datasets representative of diverse older adult populations to avoid bias and ensure external validity. Many models have yet to be externally validated or tested in real-world clinical environments (Xu et al., 2025).
Integration into clinical practice raises challenges including clinician training, workflow adaptation, data privacy concerns, and patient acceptance. The interpretability of AI outputs is critical to facilitate clinician trust and informed decision-making. Additionally, the cost-effectiveness of AI-enhanced fall prevention compared to standard care remains to be established.
Current evidence does not yet support replacing established fall risk assessments with AI tools but suggests their role as complementary aids. The heterogeneity of fall risk factors and individual patient contexts necessitates cautious interpretation of AI predictions within comprehensive clinical evaluation.
How this may change practice
Physical medicine and rehabilitation clinicians may soon incorporate AI-driven fall risk prediction tools as adjuncts to standard assessments, enabling more precise identification of high-risk older adults. This could facilitate earlier and more personalized multifactorial interventions, potentially improving functional outcomes and reducing fall-related injuries.
Clinicians should remain informed about emerging AI technologies and critically appraise their validation status and applicability to their patient populations. Collaboration with multidisciplinary teams including data scientists and informaticians will be essential to implement AI tools effectively and ethically.
Future practice may also leverage AI to enhance patient engagement and adherence through tailored education and remote monitoring, addressing common barriers to fall prevention program uptake. However, adoption should proceed alongside robust evaluation of clinical impact, safety, and cost-effectiveness.