You must have noticed the recent surge of applications that take advantage of artificial intelligence (AI) and machine learning (ML) algorithms. From self-driving cars and automated control systems to human-like personal assistants, AI seems to be the hottest commodity in today’s changing world. Although the terms AI and ML are thrown around synonymously to indicate improving machines’ capability to make smart decisions, ML is considered only a branch of AI that strives to make technologies smarter based on previously collected data.
AI has already achieved tremendous headways in areas such as natural language processing, computer vision, autonomous driving, and speech recognition. More recently, AI is stepping into healthcare applications such as computer-aided diagnosis, prediction of clinical outcomes, biomechanics, and motion analysis. The applications of human motion, which is also known as biomechanics, is getting significant traction in recent years. Exploring and investigating human movement can help athletes excel in sports. Understanding the structure of the human body can also reveal many health-related information. An AI-based system can help understand the complex relation of various body parts, identify abnormal body motion, and subsequently reveal the risk of recurring injuries.
Just like other life forms on earth, human being is constantly subjected to the gravitational pull from all directions. These complex interactions of the forces can be studied, and the resulting knowledge can be used to improve the quality of life. Human movement is the result of a complex interaction of the components of the musculoskeletal system comprising of bones, muscles, ligaments, and joints. Human movement can become degraded and destabilized because of injury or lesion in any individual component of the musculoskeletal system. Proper modification and control of the human movement can prevent injury, rectify motion abnormalities, and speed up the rehabilitation process. Chiropractors, physical therapists, occupational therapists and other health professionals around the globe are actively seeking to get a deeper understanding of the skeletal structure of the human body. Knowledge of which can then be directed at solving various medical conditions resulting from abnormal body movement. Machine learning algorithms can dig into the wealth of information embedded in our body through observation of normal daily activities and produce insights that can be used to make smart clinical decisions.
Observing the standard deviations in terms of kinematics, and EMG patterns of the human body can be used to evaluate the neuromusculoskeletal system, generate treatment plans, and to assess the efficacy of treatment for patients who have Cerebral Palsy (CP), Knee Osteoarthritis (OA), and Spinal Cord Injury (SCI). Children with CP require correct assessments of their posture to enhance both fine and gross motor control. Recently, a group of scientists came up with a deep learning model that automatically identifies postural points in the trunk and the arm region with an error rate of less than 7 degrees from the reference and classification accuracy of more than 80% in clinical settings. The researchers were able to measure the compensatory movements of the hands and the arms to maintain an upright posture to mitigate poor trunk/head control. Such automation would invariably increase the objective decision-making ability of the physiotherapists and other medical practitioners.
Biomechanical research can elevate the current rehabilitation process of neurological diseases. According to the United Nations, more than 1 billion people around the world suffer from neurological disorders. Amyotrophic lateral sclerosis (ALS) and Multiple sclerosis (MS) are the most common neurological disorders. ALS is a fatal disease, causing the degeneration of motor neurons. In contrast, MS is a non-fatal disease affecting the central nervous system, causing pain in movement and loss of motor functions. As ALS and MS are incurable, finding the disabling symptoms are very crucial. Rehabilitation exercises can prolong the life expectancy of ALS patients. Rehabilitation can potentially regain some of the lost motor functions and thereby enhance the quality of life. More research on human biomechanics and using advanced AI technologies can open pathways to a host of possibilities to mitigate the harmful impact of these diseases.
We have seen only a glimpse of what AI can bring to the healthcare industry. However, the critical question is, are we ready to take the full advantage of AI? Unlike other industries, AI adoption in healthcare is not an easy process. Although it may be easy to gather data needed to train machine learning algorithms, it is complicated to bypass regulations on the healthcare data. For example, ownership of ultrasound and X-Ray images is an issue that restricts access to the sheer volume of image data necessary to develop, design, train, and validate safe and effective AI-enabled clinical applications.
Healthcare organizations have produced a large amount of data. Some of these data are not stored in a digitized format. Moreover, doctor and patient privacy regulations hinder machine learning engineers’ access to adequate useful data sources. Optimizing the integrity of the insights is particularly tricky in healthcare compared to other industries. AI integrity comprises of four qualities of AI algorithms: health, explainability, security, and reproducibility. Firstly, trained ML models should provide results within the norms specified by the data scientists. Secondly, ML algorithms should be able to explain the factors leading to their predictive performance. Thirdly, ML algorithms should be immune to malicious attacks changing its expected behaviour. Lastly, predictions made by ML algorithms should be reproducible. Healthcare industries need to be prepared to tackle these challenges.
ML algorithms can benefit a broad spectrum of healthcare applications. From AI-controlled surgery robots to assistive patient care applications such as monitoring and assisting patient recovery. Just because someone has built an AI application, does not mean healthcare professionals can quickly adopt it. One must remember that there are many factors at play here. As people’s lives depend on proper procedures, lots of tests and trials need to be conducted before mass adoption. Healthcare industries need to go through a transition to comply with the regulations of AI. Now is the right time for companies to join the AI revolution by making necessary modifications into their business processes, adopting standards for compliance, and becoming pioneers for the future of healthcare.