PERSONALISED TREATMENT RECOMMENDATIONS USING INTERPRETABLE AI

Physical therapy for neurological rehabilitation is rapidly evolving due to the increased use of internet health resources, particularly in light of the growing need for at-home care following COVID-19. Travel expenses and the existence of musculoskeletal comorbidities are the main obstacles to in-person physical therapy sessions. The use of motion tracking, sensor-based mechanisms, and augmented feedback in more recent advanced treatment modalities, such as robotic therapy, virtual reality, non-invasive brain stimulation, and brain-computer interface, can be beneficial when combined with artificial intelligence. Physiotherapists' ability to identify issues, monitor patients remotely, and develop treatment plans for patients with neurological disorders like strokes, Parkinson's disease, multiple sclerosis, brain injuries, and spinal cord injuries can be greatly enhanced when computer intelligence is applied to neurological rehabilitation. This chapter examines the effective use of simple computer intelligence in neurological rehabilitation from a distance, particularly in identifying issues from a distance, evaluating patients, and creating personalised therapy regimens. Making challenging decisions in physical therapy for neurological rehabilitation requires considering a variety of data, including outcome metrics, patient statements, movement data from sensors, and observation of people's movements or recordings. This vast amount of data can be used by computer intelligence to identify hidden patterns, predict a patient's likelihood of recovery, and assist therapists in selecting the best course of action. However, these models must be straightforward to grasp, which means they must provide explicit justifications for their findings, in order for physicians to trust and utilise them. For instance, a therapist could use computer intelligence data to modify a stroke patient's programs and provide continuous feedback as they use a movement-tracking gadget and video workouts at home. Both the patient and the therapist can comprehend the rationale behind a suggested adjustment because to easily comprehensible computer intelligence, which fosters more trust and agreement. This chapter outlines a strategy for incorporating simple computer intelligence models, such as decision trees, network systems, and deep learning, with explanation components into systems for remote therapy. By utilising sensor data, remotely administered exams, and the patient's utilisation of online therapy programs, these models are able to assess a patient's progress in real time. Additionally discussed are explanation tools for use with more complex models. These tools assist physiotherapists in determining which factors most significantly influenced a computer-generated diagnosis or treatment recommendation. This chapter also discusses ethical, technological, and practical considerations, such as protecting patient privacy, standardising remote monitoring tools, and the significance of including physical therapists in the development of computer intelligence models to ensure their usefulness in clinical settings. It encourages a hybrid approach in which physiotherapists' thinking is aided by computer intelligence but not replaced. Lastly, customised, accessible, and widely used physical therapy for nerve issues can be enhanced through remote therapy using simple computer intelligence. Physical therapists may provide quality care even while they are far away by integrating real-time remote monitoring, intelligent examinations, and unambiguous decision support. In addition to continuing to provide care in areas that require it, this combination of technology and therapy represents a significant step towards comprehensive therapy that involves all patients, is beneficial in clinics, and is patient-centered. This chapter discusses how neurological physiotherapy employing remote therapy can be enhanced by simple AI. Artificial intelligence (AI) tools like virtual reality, robotics, and movement trackers can help with remote problem-solving, check-ups, and targeted treatments because more patients desire therapy at home following COVID and it might be challenging to meet in person. Simple AI technologies, such as decision-making processes and intelligible deep learning, make it easier for patients and physical therapists to comprehend and trust the information they get. For issues like stroke, Parkinson's disease, and spinal injuries, it can be beneficial to observe patients receiving treatment while it is administered and to modify the therapy as necessary. This chapter also discusses ethical issues, data privacy, and the significance of physician and therapist involvement. As a result, AI is viewed as a helpful tool rather than a replacement for easy-to-access brain and nerve recovery that is focused on the patient receiving treatment.