AI-Driven Programs for Non-specific Chronic Neck Pain

Overview

Non-specific chronic neck pain represents a major global health burden, affecting 30% to 50% of the general population. It is particularly prevalent among office workers, computer users, and women, with a notable rise in young adults aged 20 to 24. While acute episodes of neck pain may initially resolve, underlying functional impairments often persist, causing over a third of patients to develop chronic symptoms lasting three months or longer. Typically localized in the lateral and posterior neck regions without radicular signs, this condition is frequently driven by poor posture and improper ergonomics. The resulting abnormal stress on the cervical spine and musculature severely limits daily activities, lowers overall quality of life, and places a heavy socioeconomic strain on patients and their communities. Traditional conservative management typically relies on a combination of pharmacotherapy and standard physical therapy modalities, including manual techniques, exercise programs, electrotherapy, and thermal agents. However, these conventional interventions demand frequent, in-person clinical visits, creating a significant financial and logistical barrier for many families, particularly under current economic challenges in Egypt. Consequently, there is an urgent need for cost-effective, highly accessible, and novel rehabilitation models that can streamline care and optimize clinical outcomes. Artificial intelligence (AI) and machine learning offer a promising solution to these challenges by providing automated, data-driven remote care. Through mobile apps and smart rehabilitation platforms, AI can analyze complex clinical datasets-including patient demographics, pain intensity, and radiographic alignment-to predict treatment timelines and automate routine clinical tasks. Crucially, AI solves the problem of standardized, non-individualized home exercise plans by adjusting to a patient's daily symptom presentation, mimics a therapist's tailored approach, and offers real-time feedback. However, as these technologies advance, a clear gap remains in the physical therapy profession. Many clinicians lack a foundational understanding of AI fundamentals and harbor concerns about automation, highlighting an urgent need to evaluate physical therapists' perceptions and preparation to foster clinical trust and seamless integration.

The prevalence of chronic non-specific neck pain (CNNP) is on the rise among the young adult population. Non-specific neck pain stands as the fourth leading cause of chronic disability, with an annual prevalence rate exceeding 30%. Chronic non-specific neck pain (CNNP), is projected to affect 48%-67% of individuals at some point in their lifetime. The economic repercussions of neck pain extend to both individuals and society, encompassing costs related to healthcare, insurance, loss of productivity, and sick leave. In young adults, neck pain has been identified as a risk factor for reduced general work productivity. ( Zhang , Y ,et al,2024) Chronic nonspecific neck pain is the most frequent form of neck pain. It is commonly associated with biomechanical, functional, proprioceptive, and postural impairments. When symptoms persist for more than 12 weeks, the condition acquires the value of chronicity and is denominated chronic nonspecific neck pain (CNSNP). (Mendes-Fernandes, T, et al, 2021) Nonspecific neck pain commonly arises insidiously and is generally multifactorial in origin, including one or more of the following: poor posture, anxiety, depression, neck strain, and sporting or occupational activities. (Wilhelm, M, et al, 2023) Since proprioceptive deficit has frequently been observed following chronic neck pain, regaining neck proprioception is a critical part of neck pain rehabilitation to decrease the extra reliance and postural dependency on visual and vestibular systems while performing functional tasks. Several exercise programs have been prescribed for chronic neck pain patients to relieve pain and improve proprioceptive acuity. It hypothesized that deep flexor training can improve neck proprioception via reducing pain and strengthening the muscles which stabilize the cervical spine. ( Rahnama, L., et al,2023) Conservative care for patients with neck pain often includes pharmacologic therapies. While practice patterns may favor the use of specific agents, such as nonsteroidal anti-inflammatory drugs, corticosteroids, and opioid analgesics, providing short-term pain relief, there is limited evidence supporting their long-term use in most patients with CNNP. Exercise is a crucial component of treatment programs for patients with CNNP. Several trials have concluded that 1- to 6-month neck stretching exercises can decrease neck pain and improve neck function. However, stretching exercises may be insufficient in improving muscle strength. ( Zhang , Y ,et al,2024). Deep cervical flexors play a key role in maintaining cervical lordosis and head posture. Research in neck pain sufferers consistently shows these muscles are both under activated and fatigued compared to healthy controls. Clinically, restoring DCF endurance and activation is recommended for managing neck pain. (Iqbal, Z. A.,et al, 2021). The clinical practice guidelines suggest that manual therapy plus exercise should be the first line of defense for individuals with nonspecific neck pain without red flags. (Wilhelm, M, et al,2023) By evaluating AI-driven treatment planning, this research contributes to the emerging evidence on how technology can augment traditional care. Specifically, it explores whether AI can offer more accurate, personalized, and adaptive treatment plans, thereby enhancing treatment outcomes, improving patient engagement, and potentially reducing the burden on healthcare systems. The results of this study may support the adoption of AI tools in clinical settings, leading to more efficient and accessible rehabilitation models, especially in underserved or remote areas (Rashid \& Sharma, 2025). The integration of artificial intelligence (AI) in patient pain medicine education has the potential to revolutionize pain management. By harnessing the power of AI, patient education becomes more personalized, interactive, and supportive, empowering patients to understand their pain, make informed decisions, and actively participate in their pain management journey. AI tailors the educational content to individual patients' needs, providing personalized recommendations. It introduces interactive elements through chatbots and virtual assistants, enhancing engagement and motivation. AI-powered platforms improve accessibility by providing easy access to educational resources and adapting content to diverse patient populations. Future AI applications in pain management include explaining pain mechanisms, treatment options, predicting outcomes based on individualized patient-specific factors, and supporting monitoring and adherence. (Robinson et al, 2024). Expected Advantages of AI-Based Rehabilitation; Personalized plans adjusted weekly based on real-time patient input, Cost-effective model minimizes the need for frequent clinic visits, Shorter treatment duration adaptive protocols reduce unnecessary sessions, Predictive modeling uses patient data to estimate likely improvement Scalability and accessibility especially beneficial for remote or underserved populations. (Khalid et al, 2024). By searching the literature there is no study based on our knowledge compare between AI driven exercise and traditional program in pain intensity level , neck ROM, neck proprioception, neck functional ability level in patients with Non-Specific Chronic Neck Pain so, the aim of this study is to investigate the efficacy of AI-driven program \& traditional physical therapy programs on pain intensity level , neck ROM, neck proprioception , neck functional ability level in patients with Non-Specific Chronic Neck Pain .