Publication
Adaptive AI Systems in Knee Rehabilitation: Integrating Artificial Mental Models for Personalized Patient Support
Sabine Janzen; Prajvi Saxena; Cicy Agnes; Wolfgang Maaß
In: 13. Jahreskongress der Deutschen Kniegesellschaft. Jahreskongress der Deutschen Kniegesellschaft (DKG-2024), November 8-9, Hamburg, Germany, DKG, 11/2024.
Abstract
In the field of prevention and rehabilitation, adaptive and personalized Artificial Intelligence (AI) systems play a vital role in enhancing patient-centric care. These AI systems analyze extensive data on user behavior and situational context to provide tailored support that meets unique needs of individuals, such as patients recovering from knee surgeries. Such patients often face cognitive challenges that interfere with their ability to process complex medical information, make informed decisions, and communicate effectively about their symptoms. These challenges may lead to the generation of incomplete, inaccurate, or biased data, significantly impacting the effectiveness of their treatment. This research introduces the concept of artificial mental models (AMM) integrated into healthcare AI systems. AMMs are cognitive frameworks that encapsulate patient's perceptions and expectations about their therapy and recovery journey. They are beneficial in scenarios requiring nuanced understanding and adaptation to patient's changing conditions, e.g., to assist an amateur soccer player recovering from knee surgery. Here, the AMM acts as a liaison between patient and therapist, helping to devise a personalized exercise regimen that adapts to patient's pain and progress. This work explores the generation of AMMs using Large Language Models instrumental in both eliciting and fine-tuning AMMs to individual patient needs.
The research encompasses a prospective study with two phases: elicitation and individualization. The elicitation phase involves creating a bias-free, domain-specific basis AMM through extensive data collection from both quantitative studies and indirect observations, including data on personality traits and expected pain during specific exercises. This basis AMM is evaluated in a technical experiment to ensure it is free from bias and discrimination. In the individualization phase, the basis AMM is refined using direct observations of specific patients, incorporating curated data such as medication details, rehabilitation plans, and patient-reported outcomes, as well as non-curated data like movement patterns and fitness status.
The final AMM tailored for individual patients is assessed through action research, involving real-world application to evaluate its impact on rehabilitation outcomes. The research questions focus on whether the predictions made by the AMM about pain are consistent with the patients' experiences and the assessments made by therapists.
Results validate the effectiveness of AMMs in real-time clinical settings, demonstrating their potential to significantly enhance the personalization and effectiveness of patient care in knee rehabilitation. Broader implications suggest that AI-driven approaches could enhance patient care across various areas of healthcare, supporting the ability of systems to predict patient needs and improve overall outcomes by more targeted and effective interventions.