CyberALS, a phenomenon that neurologists should worry about?

Objective: Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease that generates significant fear in both patients and the general population. In recent years, widespread access to artificial intelligence (AI)–driven health information tools—such as symptom checkers, large language models, and automated risk interpretation platforms—has transformed how individuals seek medical knowledge. While these tools offer educational benefits, they may also contribute to heightened ALS anxiety- CyberALS -the term we invented for this matter. Our objective is to explore the phenomenon of AI-driven anxiety related to ALS in non-ALS patients. Examining how AI-mediated health information influences symptom interpretation and what factors contribute to a higher risk of developing anxiety towards ALS. Methods: Between 2021 and 2025, 582 consecutive patients presenting with neuromuscular complaints were referred to the ALS clinics at the First University Clinic of TSMU and Medcenter Batumi with fear of having ALS. Following comprehensive neurological examination and appropriate longitudinal diagnostic investigations, 220 individuals were determined to have benign symptoms without evidence of neuromuscular disease and were included in the analysis. Participants were stratified according to self-reported use of AI-based symptom checkers or generative AI platforms: 143 patients reported repeated AI exposure, while 77 patients with comparable clinical presentations had not used AI tools. Demographic variables (age, sex, educational level) and personal experience with neurological illness were recorded. Anxiety severity was assessed in all participants using the Hamilton Anxiety Rating Scale (HAM-A), and anxiety levels were compared between AI users and non-users. Statistical analysis was performed using binary logistic regression models, separately for AI users and non-AI users. Statistical significance was defined as p < 0.05. Results: Among 220 patients evaluated (2021–2025), the majority presented with diffuse fasciculations and subjective weakness without objective neurological deficits. No patient fulfilled clinical or electrophysiological criteria for motor neuron disease. Anxiety assessment revealed elevated levels across the AI using cohort (N143), with mean HAM-A scores in the moderate-to-severe (24–30) range. Higher anxiety scores were significantly more frequent among younger patients (age 22–28) and those with higher educational attainment. Comparative analysis reveals a statistically significant increase in anxiety levels among patients who utilized AI platforms, relative to a control group (N77) that abstained from AI-assisted self-diagnosis. Personal or familial history of neurological illness further amplified anxiety severity and disease-related fear. Conclusion: This study demonstrates that exposure to AI chatbots may contribute to clinically significant health anxiety and persistent fear of ALS in patients presenting with benign neuromuscular symptoms. Despite the absence of objective evidence for motor neuron disease, elevated anxiety levels were universal and frequently disproportionate. Addressing cyberALS is essential to ensure that digital health technologies support, rather than undermine, psychological well-being.