**5.4 Clinical decision-making**

AI has been used as a decision support tool [25]. For example, in the field of rehabilitation, a DL algorithm has been developed to recommend to patients with low back pain, and according to clinical aspects whether they should go to their primary care physician, a physical therapist, or whether they can perform selfmanagement [26].

DL has also been used to develop pain phenotypes based on resonance imaging findings. However, due to the complexity of pain, the role of this classification in daily care is unclear [27].

One aspect that AI could enhance would be biomarkers. In many cases, certain biomarkers cannot be used because they are too costly to obtain in terms of time or money. For example, in frailty, DL has been used to analyze body composition (bone mass, muscle mass, and fat distribution) in a CT slice at third lumbar vertebra (L3)

to assess frailty and sarcopenia [28]. This would allow obtaining important data that would allow prescribing rehabilitation programs more appropriately.

AI has also been used to classify fractures. There are algorithms that have shown 72% accuracy in calcaneal fracture classification using CT [29], with similar or superior effectiveness to orthopedic surgeons for classification of proximal humerus fractures [30], good performance in hip fractures [31], femur [32], and ankle [33].

AI systems that combine clinical data in rib fractures with imaging test results have been published to improve sensitivity and reduce diagnostic time compared with expert radiologists [34].

DL has also been used to discover hidden fractures by combining clinical and radiological data. For example, an algorithm could predict the likelihood of posterior malleolar fracture in patients with tibial shaft fractures by analyzing the image along with other clinical, demographic, and injury data of the patient such as age, mechanism of injury, and fracture type [35].

#### **5.5 Prediction and risk of musculoskeletal injuries**

A growing field for the use of AI is sports medicine, although not only for the purpose of predicting whether an athlete is going to suffer an injury during a match or training but also about measuring the risk of injury to the athlete by analyzing all intrinsic and extrinsic factors and their relationship to each other, since injuries occur because of these. For example, in basketball, extrinsic factors could include the ball, the type of floor, the playing field, the temperature, or the time at which the game is played. Within intrinsic factors, we would have previous injuries, age, or gender [36].

In addition to the sport, the predictive factors are probably related to biological variables of the athletes, although no clear relationship has been established. Static traits such as flexibility, strength, or balance have usually been considered to predict injury. However, the dynamic and changing aspect of these characteristics, as well as their mutual influence, have not been taken into account [37]. AI could help manage this data.

#### **5.6 Application to improve health literacy**

Literacy is a heterogeneous and multidimensional concept that implies the ability to understand, evaluate, use, and interact with written texts in order to participate in society, achieve one's goals and develop one's potential. Health literacy involves the ability to enable individuals to obtain, understand, appreciate, and use information to make decisions and take actions that have a significant impact on their health status [38].

To improve health literacy, one tool that could be used within AI would be the use of chatbots. A chatbot is a computer system that mimics a human conversation by text or voice. Despite its potential, users of these systems often abandon them after the first or second encounter with it [39]. AI has been incorporated to achieve more empathetic and human interfaces that more realistically simulate user interaction [40].

Chatbots could facilitate health literacy, improve disease self-management, stimulate treatment adherence, or improve administrative services, such as medical appointment management [41].

These systems are also used to facilitate adherence to a home rehabilitation exercise program at hospital discharge. The role of algorithms here would be to enhance exercise adherence, achieving improved patient motivation and involvement [42]. In addition, AI could solve the fact that resources to assist patients in home-based Rehabilitation are often generic and not well adapted to individual needs and preferences [43]. For that reason, AI has been used to improve the performance of home exercise programs [44].

## **5.7 Data management and wearable devices**

A fundamental aspect of data management today is big data. Big data involves a set of tools that analyzes data too large or too complex to be processed by traditional statistical systems. This complexity has led to the use of AI systems to analyze Big Data. For example, it has been successfully employed to coordinate the results of massive multicenter studies in the field of drug discovery [45].

In the field of musculoskeletal diseases, a large volume of data are being recorded through imaging, electronic medical records, sensors in wearable devices, and in genome sequencing. Major advances are also being made in analysis and processing systems. Thus, analyzing in detail the multidimensional information in a patient's electronic health record would provide a powerful tool to facilitate individualized health management [46].

Fuzzy logic-based AI systems that are capable of analyzing questionable, incomplete, or inconsistent clinical information have been employed and still facilitate the diagnostic management of certain pathologies [47].

Wearable devices are ubiquitous today. These devices are equipped with different sensors (accelerometers, global positioning system, gyroscopes. ..) that can record a large number of biological parameters and also have permanent connectivity. Internet of Things (IoT) refers to the set of physical objects with sensors and programs connected to other devices and systems through a network. One of the practical applications of this type of technology would be to extract a high volume of data from lifestyles, training, and sport events [48]. AI could use all this data and integrate it with other sources of information to generate algorithms to make clinical decisions or predict adverse events.

In addition to sports activity, wearables are used by users to record sleep quality, general physical activity, and walking (speed, distance traveled, and number of steps). However, it has not yet been possible to leverage this information to optimize healthcare or decrease healthcare costs [49]. It is thought that AI may be the solution to harness the performance of all this data and improve patient health.
