Ann Rehabil Med.  2023 Dec;47(6):444-458. 10.5535/arm.23131.

AI in Rehabilitation Medicine: Opportunities and Challenges

• Lanotte F
• O’Brien MK
• Jayaraman A

• KMID: 2549726
• DOI: http://doi.org/10.5535/arm.23131

Abstract

Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient’s outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.

Keyword

Machine learning; Clinical outcomes; Wearable sensors; Computer vision; Precision medicine

Figure

• 

  Fig. 1. Data for rehabilitation medicine. (A) Traditionally, data is siloed in the different stages of medical care (e.g., community living, primary care physician, specialist physician, acute-care hospitals, rehabilitation facilities, etc.), with limited data mobility as patients transition between care settings. Artificial intelligence (AI) can integrate this information for a tailored and comprehensive evaluation of the health status of an individual. (B) Example data sources for AI applications in rehabilitation medicine.

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  Fig. 2. Framework for developing artificial intelligence (AI) models with clinically relevant attributes. Input data, such as EHR (electronic health record), imaging, sensor data, or video recordings (see Fig. 1B), are first selected and validated for their ability to capture the desired target output. Single/multimodal data streams are collected, processed, and passed to AI algorithms for inferential and/or predictive analytics. After establishing baseline model performance, streamlined datasets can be tested to determine the minimal data needed to achieve this performance, thereby supporting practical clinical implementation. The final model should be externally validated to determine its generalizability to an independent dataset. If performance is not satisfactory, the framework can be revisited in whole or in part.

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  Fig. 3. Model streamlining. (A) Hypothetical illustration of the minimal equivalent data needed for a model. When paired with appropriate AI (artificial intelligence) practices, increasing the data resolution (e.g., adding data sources, increasing measurement frequency, increasing data complexity) can decrease model error (black curve) at the expense of greater computational burden (purple line). The minimal equivalent dataset (grey star) reduces the data resolution without substantial increases in error, thereby streamlining the model for more practical real-world deployment. (B) Example of a streamlining process for wearable sensor data. Descriptions on the right include data input during model training and testing, and the stepwise methods of streamlining in bold. Min. Eqv., minimal equivalent.

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  Fig. 4. Data characterization for interpretable artificial intelligence (AI). The most meaningful data for interpretable AI models are both intuitive and relevant in clinical care (quadrant I, green). Data can be highly relevant to model performance but less intuitive, thus rendering it less actionable to a clinician during treatment (quadrant IV, yellow). Other data are highly intuitive and understandable to a treating clinician but less relevant, providing little-to-no value to the model (quadrant II, red). Data that are unintuitive and irrelevant should not be included in a model (quadrant III, grey).

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