Artificial Intelligence in Podiatric Practice: From Gait Analysis to Diabetic Foot Risk Prediction

Artificial intelligence has expanded rapidly across podiatric care, from automated gait analysis to the prediction of diabetic foot complications. This narrative review synthesizes the evidence published between 2016 and 2025 on the applications, reported performance, and limitations of these tools. A documented search into five thematic axes: automated gait analysis and biomechanical assessment; diabetic foot risk prediction and ulcer assessment; diagnostic imaging and foot deformity classification; wearable sensing and remote monitoring; and validation, bias, and clinical translation. Deep learning reconstructs plantar pressure and joint kinetics from wearable signals and automates radiographic measurements of foot deformities. Prediction models for diabetic foot ulcers, amputation, and recurrence report areas under the curve between 0.89 and 0.97 in internal validation, although performance attenuates on external testing. Wearable insoles and sensor-enabled dressings extend monitoring beyond the clinic. Convergent evidence indicates that most models remain single-center proof-of-concept studies without prospective deployment, and that podiatrists favor supportive rather than autonomous roles for artificial intelligence. Future research should prioritize external validation, fairness, and implementation studies that link these tools to clinical outcomes.