GEN-AI Multimodel Prediction and Prescriptive Care for Knee Osteoarthritis Progression

Abstract

Knee Osteoarthritis (KOA) is one of the leading sources of musculoskeletal disorders that results in a chronic pain experience for the elderly population, makes them lose their mobility and leads to a lower quality of life. Despite the high prevalence of KOA, clinical practice remains reactive and primarily focuses on symptom control. To close this gap, the project achieves an early detection generative AI-based multimodal prediction and a prescriptive care framework for personalized management of KOA progression. The introduced framework is a fusion of radiographic imaging (X-rays), Electronic Health Records (EHRs), laboratory data, and patient-reported outcomes to provide structural, clinical, and behavioural facets of the disease evolution. The paper leverages deep learning techniques from sophisticated architectures such as convolutional neural networks (CNNs) and transformer-based temporal models to capture spatial and sequential patterns for the heterogeneous data. The major novelty is the development of digital twin models serving as patient-specific, dynamic simulations for visualizing disease trajectories and testing the counterfactual “what-if” interventions. The system is equipped with explainable AI modules that deliver simplified information on risk sources and recommended lifestyle or clinical strategies. The fusion of multimodal, generative modelling, and prescriptive intelligence aims to make a radical change in the treatment of KOA by eliminating reactive symptom treatment and thus introducing proactive, preventive, and personalized care leading to the improvement of the patient's outcome and reducing socio-economic burden of osteoarthritis in the long term.

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