Enhancing Rehabilitation Assessment: Human-AI Collaboration for Clinical Decision Making

Table of Contents:

1. Introduction
2. Background
3. Challenges in adopting decision support systems
4. Human-AI collaboration in rehabilitation assessment
5. The proposed approach: integrating machine learning and rule-based model
6. User studies and analysis of therapist's experiences
7. Results and benefits of the proposed system
8. Conclusion
9. Resources
10. FAQ

Human-AI Collaboration in Rehabilitation Assessment: Improving Decision-Making

The field of Healthcare has seen a growing interest in utilizing AI systems for decision-making support. In the domain of rehabilitation assessment, clinicians can greatly benefit from the computational information provided by AI to enhance their clinical decision-making processes. However, there are challenges in adopting these systems due to factors such as lack of user-centered designs and the opaqueness of AI systems.

1. Introduction

Rehabilitation assessment plays a crucial role in determining the progress of patients recovering from stroke. The adoption of AI systems can provide therapists with valuable insights and analysis on the patient's status, leading to more informed decision-making. This article delves into the development and evaluation of an interactive AI system that supports collaborative decision-making with therapists on rehabilitation assessment.

2. Background

With advancements in machine learning, algorithmic-assisted decision making has gained attention in various domains. In the health domain, clinical decision support systems have emerged as a promising way to improve the quality of decision-making. However, the successful implementation of these systems faces challenges such as lack of user-centered designs and the non-robustness of AI systems to new data.

3. Challenges in Adopting Decision Support Systems

The adoption of decision support systems in clinical practice is not without hurdles. Lack of user-centered designs hinders the seamless integration of AI technology into therapists' workflow. Additionally, the opaqueness of AI systems poses challenges in understanding and trusting the outputs generated by these systems. Moreover, the inability of AI systems to adapt to new data further complicates their implementation.

4. Human-AI Collaboration in Rehabilitation Assessment

To overcome the challenges Mentioned above, the focus of this work is on developing a decision support system for stroke rehabilitation assessment that encourages collaboration between human therapists and AI. By leveraging the expertise of both humans and AI, this collaborative approach aims to improve decision-making and ultimately enhance patient outcomes.

5. The Proposed Approach: Integrating Machine Learning and Rule-Based Model

The proposed approach integrates machine learning techniques with a rule-based model based on therapists' knowledge. Through iterative engagement with therapists and post-stroke patients, this approach aims to create a human-AI collaborative framework for rehabilitation assessment. Reinforcement learning is applied to dynamically identify Salient features of assessment and predict the quality of motion. These salient features are then used to generate patient-specific analysis for the therapists.

6. User Studies and Analysis of Therapist's Experiences

In order to evaluate the effectiveness of the proposed system, user studies were conducted with therapists. Therapists were presented with three variants of the system: a traditional system that only shows a video of the patient's exercises, a system that shows videos with only the predicted score of AI, and the proposed system that shows videos with predicted scores and patient-specific analysis. Feedback from therapists was collected to analyze the impact of AI analysis on their decision-making process.

7. Results and Benefits of the Proposed System

The proposed system achieved higher scores on various perspectives as compared to the traditional and AI-only variants. Therapists expressed higher usage intent for the proposed system, as it provided richer information and reduced their effort on assessment while significantly improving their agreement level. Both rule-based and hybrid models, incorporating therapist feedback, showed significant performance improvement in replicating therapist's assessments. The interactive hybrid model performed significantly better than the non-interactive neural network, and the agreement level among therapists was also enhanced.

8. Conclusion

This work emphasizes the value of incorporating user-centered explanations from AI to facilitate effective human-AI collaboration in rehabilitation assessment. The results highlight the benefits of the proposed approach, where experts and AI systems learn from each other's strengths to improve decision-making. The user-centered analysis provided by AI offers new insights to experts, facilitating consistent assessment with less effort. The interaction between experts and AI enables a better understanding of the capabilities and limitations of AI and allows for more accurate predictions.

9. Resources

• Resource 1: AI in Healthcare - Challenges and Opportunities
• Resource 2: Human-Centered Design in AI Systems
• Resource 3: Reinforcement Learning in Decision Support Systems

10. FAQ

Q: How does the proposed system benefit therapists in rehabilitation assessment? A: The proposed system provides therapists with computational information and analysis on a patient's status, enabling them to make more informed decisions. It reduces their effort on assessment and improves agreement among therapists.

Q: What are the challenges in adopting decision support systems in healthcare? A: Challenges include lack of user-centered designs, the opaqueness of AI systems, and the non-robustness of AI systems to new data. Addressing these challenges is crucial for successful implementation.

Q: How does the interactive hybrid model compare to the neural network in the proposed system? A: The interactive hybrid model outperforms the non-interactive neural network in terms of replicating therapist's assessments and agreement level among therapists. It showcases the benefits of human-AI collaboration.

Q: Are there any resources available for further reading on ai in healthcare and human-centered design? A: Yes, please refer to the following resources:

• Resource 1: AI in Healthcare - Challenges and Opportunities
• Resource 2: Human-Centered Design in AI Systems