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AI-Driven Adaptive Systems for Knee Rehabilitation: Leveraging Artificial Mental Models for Personalized Patient Support

Sabine Janzen; Prajvi Saxena; Cicy Agnes; Wolfgang Maaß
In: Proceedings of the Conference of the German AI Service Centers 2024. Konferenz der deutschen KI-Servicezentren (KonKIS-2024), September 18-19, Göttingen, Germany, 9/2024.

Abstract

In the evolving domain of prevention and rehabilitation, adaptive and personalized Artificial Intelligence (AI) systems are pivotal for delivering patient-centric care. This study investigates the application of Artificial Mental Models (AMM) within healthcare AI systems, specifically in knee rehabilitation, to address cognitive challenges that result in incomplete or biased data, impacting patient decision-making and communication. Leveraging Large Language Models (LLMs), we develop and fine-tune AMMs to accurately capture individual patients' mental models and enhance support during rehabilitation. Our research adopts a Design Science Research (DSR) methodology encompassing two phases: elicitation and individualization. In the elicitation phase, a domain-specific AMM is generated through a quantitative study involving 150 participants and indirect patient observations, ensuring a discrimination- and bias-free model. The individualization phase utilizes curated and non-curated patient data to fine-tune the AMM for individual patients. The effectiveness of these patient-specific AMMs is evaluated in real-world rehabilitation settings through A/B testing, comparing patient and AMM predictions of pain with actual pain assessments. The study's outcomes highlight the potential of AI systems to provide accurate, personalized, and bias-free patient care, significantly improving rehabilitation outcomes. The methodology and findings suggest broader applicability across various healthcare domains, enhancing the integration of AI in routine patient care and advancing the effectiveness of therapeutic interventions.

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