CyberALS, a phenomenon that neurologists should worry about?

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Mariam Kekenadze, Shorena Vashadze, Nana Kvirkvelia, Eka Kvaratskhelia, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8986902/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. AI ALS Fasciculations anxiety Figures Figure 1 Introduction The healthcare sector is undergoing a revolution, with artificial intelligence (AI) playing a pivotal role in transforming how we diagnose and treat diseases. AI’s ability to process vast amounts of data quickly and accurately is reshaping the medical landscape internationally and locally. Additionally, everything is smoothly and quietly integrating into the health system through symptom checkers, conversational agents, and decision-support tools used directly by the public. These technologies, at best, aim to improve access to medical knowledge and empower patients in health-related decision-making. However, emerging evidence suggests that AI-mediated health information may also have unintended psychological consequences, particularly when probabilistic or decontextualized outputs are interpreted without clinical guidance [ 1 , 2 ]. When asking ChatGPT “Why do I have fasciculations?” in the response, it is mentioned that “Fasciculations alone do not indicate ALS”, however even this output alone could be detrimental for the health-anxious person. One well-described phenomenon in this context is cyberchondria , defined as excessive or repetitive online searching for health information associated with increased anxiety and distress [ 3 ]. Studies have demonstrated that individuals with high health anxiety or intolerance of uncertainty are particularly vulnerable, often entering a self-reinforcing cycle in which reassurance-seeking paradoxically amplifies fear [ 4 ]. Although early cyberchondria research focused primarily on traditional internet searches, the rapid evolution of AI-driven symptom checkers and large language models introduces new dimensions, including perceived authority, personalization, and persuasive explanatory styles that may further intensify anxiety responses [ 5 , 6 ]. Recent evaluations of AI symptom checkers highlight substantial variability in diagnostic accuracy and risk communication, especially for rare or complex neurological conditions [ 7 , 8 ]. Trust in AI outputs has been associated with depending on framing, explanation, and presentation of uncertainty, factors that directly influence user anxiety and decision-making behavior [ 9 ]. While some digital tools may reduce anxiety by guiding appropriate care-seeking, for instance, research showed AI chatbots contributed to modest enhancements in life satisfaction and well-being [ 10 ] others may inadvertently reinforce catastrophic interpretations, particularly in users with limited clinical knowledge [ 11 ]. These concerns are especially relevant in the context of amyotrophic lateral sclerosis (ALS), a rare but universally fatal neurodegenerative disease with profound psychological impact not only on patients, but also on caregivers, and with no simple biomarker to prove or disprove diagnosis, only a combination of clinical/electrophysiological findings to support diagnosis through modern criteria. Raising awareness of ALS in public, combined with the nonspecific nature of early neuromuscular symptoms such as fasciculations, fatigue, or perceived weakness, creates fertile ground for misinterpretation [ 12 ]. In clinical practice, fear of ALS is a recognized driver of neurological consultation among individuals ultimately found to have benign conditions; [ 13 ] however, the role of AI-mediated information in shaping this fear has not been systematically examined. AI-driven anxiety surrounding ALS, given the name CyberALS by us, therefore, represents an emerging challenge at the intersection of neurology, mental health, and digital medicine globally. Material and Methods This study was designed and conducted at The First University Clinic of Tbilisi State Medical University (TSMU) and Medcenter Batumi ALS clinics . 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. Following comprehensive neurological examination and appropriate diagnostic investigations, 220 individuals were determined to have benign symptoms without evidence of neuromuscular disease and were included in the analysis; these were 120 female and 100 male patients, aged 18–72 years, applying for evaluation of neuromuscular symptoms accompanied by a persistent fear of amyotrophic lateral sclerosis (ALS). 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 using a structured questionnaire (see supplementary material 1). Anxiety severity was assessed in all participants using the Hamilton Anxiety Rating Scale (HAM-A) [ 14 ], and anxiety levels were compared between AI users and non-users. The study adhered to the principles of the Declaration of Helsinki and was approved by the ethics committee of Tbilisi State Medical University (Date: 8th June 2020, approval no. N3-2020/80). Informed consent was obtained from all participants. Eligible patients were presenting with complaints of weakness, muscle atrophy, fasciculations, for whom concern about ALS constituted a dominant reason for referral or repeated healthcare seeking. 143 included patients reported prior or ongoing exposure to AI-based symptom checkers, automated diagnostic platforms, or generative AI tools, which they identified as influential in shaping their belief or fear that they might have ALS and seeking medical advice. 77 Patients who were not exposed to AI tools were assessed for anxiety levels as well. Those who were diagnosed with neurological disorders during the investigation process were excluded. The flow of the study inclusion can be found in Fig. 1 . Clinical evaluation was performed by motor neuron disease neurologists and included a comprehensive medical history and a detailed neurological examination. Investigations, including electromyography (EMG) and nerve conduction studies(NCS), neuroimaging, and relevant laboratory tests, in accordance with established diagnostic standards and routine clinical practice, were performed. Multivariable logistic regression was performed to identify independent predictors of severe anxiety (HAM-A ≥ 25). Variables were selected a priori based on clinical relevance and univariable associations. Results are presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Statistical significance was set at p < 0.05. In parallel with the neurological assessment, the psychological and contextual dimensions of ALS-related fear were explored. Exposure to AI-driven health information was assessed through a structured clinical questionnaire (see supplementary material 1), documenting the types of AI tools used, frequency and duration of use, and the extent to which AI-generated outputs referenced ALS or motor neuron disease. Patients' anxiety levels were examined by using the Hamilton Anxiety Rating Scale (HAM-A) and compared in AI users and non-users. AI-using patients were asked to describe AI-using characteristics, type of AI platform used, frequency of AI use, primary symptoms searched, patient perception of AI information, typical AI-generated interpretations reported by patients and behavioral impact of AI use. Prior personal or familial experiences with neurodegenerative disease were explored, and education level was documented in AI and non AI-users. Results Among 220 individuals, 143 reported regular use of AI-based symptom checkers or generative AI platforms before referral and persistent fear of having amyotrophic lateral sclerosis (ALS). A substantial proportion of patients had higher educational attainment (see Table 1 ). Neurological examination showed no objective evidence of upper or lower motor neuron dysfunction. None of the patients met clinical or electrophysiological criteria for motor neuron disease; All patients underwent electromyography and nerve conduction studies, and they showed no signs of active or chronic denervation. In some cases, ENMG was performed multiple times for reassurance, with different specialists. In all patients, muscle bulk and strength were preserved on formal testing using the MRC motor grading system, hand grip test, and paper grip test (PGT) for the limbs, despite subjective reports of weakness and perceived muscle loss. Fasciculations were commonly reported and were most often diffuse, intermittent, and migratory in nature. These were frequently exacerbated by stress, fatigue, or heightened symptom monitoring. All patients demonstrated elevated anxiety levels on the Hamilton Anxiety Rating Scale (HAM-A). Elevated HAM-A scores were positively correlated with both younger age and higher educational attainment within the AI-user group. Non-users of AI platforms demonstrated lower anxiety indices, suggesting that the absence of AI-mediated health assessments correlates with reduced psychological distress in early adulthood. (Table 1 ). AI user -patients reporting personal or familial experiences with neurological or neurodegenerative disease exhibited particularly pronounced anxiety and devastating interpretation of benign symptoms.(Table 3 ) Table 1 Anxiety severity (HAM-A) stratified by demographic characteristics and AI use in patients with ALS-related health anxiety (AI users n = 143; non-AI users n = 77) Characteristic AI users (n = 143) Mild n (%) Moderate n (%) Severe n (%) Non-AI users (n = 77) Mild n (%) Moderate n (%) Severe n (%) p value† Age ≤ 30 years 3 (7.3) 13 (31.7) 25 (61.0) 9 (37.5) 11 (45.8) 4 (16.7) < 0.001 31–44 years 7 (16.3) 21 (48.8) 15 (34.9) 10 (38.5) 12 (46.2) 4 (15.3) 45–59 years 10 (29.4) 15 (44.1) 9 (26.5) 12 (44.4) 11 (40.7) 4 (14.8) ≥ 60 years 8 (32.0) 12 (48.0) 5 (20.0) 11 (52.4) 8 (38.1) 2 (9.5) Female 12 (14.6) 35 (42.7) 35 (42.7) 16 (39.0) 18 (43.9) 7 (17.1) 0.002 Male 16 (26.2) 26 (42.6) 19 (31.1) 20 (55.6) 14 (38.9) 2 (5.6) Secondary education 15 (31.3) 20 (41.7) 13 (27.0) 18 (60.0) 9 (30.0) 3 (10.0) 0.001 Higher education 13 (13.7) 41 (43.2) 41 (43.1) 18 (38.3) 23 (48.9) 6 (12.8) Total 28 (19.6) 61 (42.7) 54 (37.7) 36 (46.8) 32 (41.6) 9 (11.6) < 0.001 Table 1. HAM-A: Hamilton Anxiety Rating Scale. Mild anxiety: 8–17; moderate: 18–24; severe: ≥25. †Pearson χ² test used for categorical comparisons; χ² test for trend applied for ordered age groups. Statistical significance is defined as p < 0.05. Patients who reported AI-based symptom checker or generative AI use demonstrated significantly higher anxiety severity compared with non-AI users (p < 0.001). Severe anxiety was markedly more prevalent among AI users, particularly in younger patients (≤30 years), whereas non-AI users more frequently exhibited mild or moderate anxiety. These findings suggest that AI-mediated health information is associated with an increased anxiety burden in patients presenting with ALS-related health concerns. Patients who reported AI-based symptom checker or generative AI use demonstrated significantly higher anxiety severity compared with non-AI users (p < 0.001). Severe anxiety was markedly more prevalent among AI users, particularly in younger patients (≤ 30 years), whereas non-AI users more frequently exhibited mild or moderate anxiety. These findings suggest that AI-mediated health information is associated with an increased anxiety burden in patients presenting with ALS-related health concerns. Table 2 Patterns of AI-based symptom checker and generative AI use among patients with CyberALS (n = 143) AI use characteristic n (%) Type of AI platform used Symptom checker applications 98 (68.5) Generative AI platforms (chatbots, LLMs) 121 (84.6) Both symptom checkers and generative AI 76 (53.1) Frequency of AI use Occasional (≤ 1 time/week) 32 (22.4) Regular (2–4 times/week) 49 (34.3) Frequent (≥ 5 times/week or daily) 62 (43.4) Primary symptoms searched Fasciculations 118 (82.5) Muscle weakness 104 (72.7) Muscle atrophy 79 (55.2) Bulbar symptoms (speech/swallowing) 41 (28.7) Typical AI-generated interpretations reported by patients ALS is listed as a possible or likely diagnosis 109 (76.2) Emphasis on progressive or fatal outcome 94 (65.7) Discussion of benign alternatives 87 (60.8) Patient perception of AI information Considered AI output authoritative or highly reliable 101 (70.6) Considered AI output more reliable than clinician reassurance 64 (44.8) Somewhat reliable 30 (21.0) Not reliable 12 (8.4) Behavioral impact of AI use Increased symptom monitoring or self-examination 112 (78.3) Repeated reassurance-seeking (medical consultations) 96 (67.1) Discuss symptoms with family/friends 83 (58.0) Search for the same symptoms again online, and continue AI use 107 (74.8) Did not affect my behaviour 21 ( 14.7) Table 2. Table is representing results of the AI questionnaire in AI users. Type of AI platform used, frequency of AI use, primary symptoms searched, typical AI-generated interpretations reported by patients, patient perception of AI information, and behavioral impact of AI use. Percentages may exceed 100% where multiple responses were permitted. AI: artificial intelligence; ALS: amyotrophic lateral sclerosis. Table 3 Multivariable logistic regression analysis of factors associated with severe anxiety (HAM-A ≥ 25), stratified by AI use Variable AI users (n = 143) Adjusted OR (95% CI) p value Non-AI users (n = 77) Adjusted OR (95% CI) p value Age ≤ 30 years 3.21 (1.54–6.71) 0.002 1.42 (0.48–4.18) 0.52 Female sex 1.88 (1.02–3.47) 0.043 1.21 (0.39–3.78) 0.74 Higher education 2.63 (1.32–5.25) 0.006 1.34 (0.44–4.06) 0.60 Personal/familial neurological disease history 1.91 (0.96–3.79) 0.067 1.18 (0.36–3.85) 0.78 Frequent AI use (≥ 5 times/week)* 3.07 (1.49–6.31) 0.002 — — Table 3. Binary logistic regression models were performed separately for AI users and non-AI users. Outcome variable: severe anxiety (HAM-A ≥25). Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) are shown. Statistical significance is defined as p < 0.05. Frequent AI use is included only in the AI-user model. In stratified multivariable analysis, younger age, higher educational attainment, and frequent AI engagement remained independently associated with severe anxiety among AI users, whereas no demographic variable independently predicted severe anxiety in non-AI users(Table 3 ). These findings suggest that the anxiety gradient observed in the overall cohort may be largely driven by patterns of AI exposure rather than demographic vulnerability alone. Discussion This study provides evidence that exposure to AI-driven health information is associated with increased anxiety related to amyotrophic lateral sclerosis (ALS), even in the absence of objective neurological disease. Within our cohort of 220 patients, ALS-related fear was significantly more pronounced among individuals who reported using AI-based symptom checkers or generative AI platforms compared with non-AI users, despite normal clinical examinations and investigative findings. These results extend the established concept of cyberchondria to the context of generative artificial intelligence, highlighting a novel form of disease-specific anxiety within neurological practice. Health anxiety disproportionate to clinical findings is a well-recognized phenomenon in neurological practice, particularly in patients presenting with benign neuromuscular symptoms such as fasciculations or subjective weakness [ 13 ]. Our findings suggest that AI-based symptom checkers and generative platforms may intensify this anxiety by presenting rare but severe diagnoses alongside common, nonspecific symptoms, without adequate contextualization of probability or prevalence. This aligns with prior work demonstrating that digital symptom tools often overemphasize serious conditions to avoid false reassurance, inadvertently increasing user distress [ 7 , 8 ]. Fasciculations and subjective weakness were the predominant presenting symptoms, consistent with prior literature describing benign fasciculation syndrome and anxiety-related neuromuscular complaints as common reasons for neurological referral [ 15 , 16 ]. Importantly, none of the patients demonstrated clinical progression, objective weakness, muscle wasting, or electrophysiological evidence of denervation—features that are crucial to the diagnosis of ALS [ 17 ]. This further supports existing evidence that fasciculations in isolation, particularly when widespread and fluctuating, are rarely indicative of motor neuron disease [ 18 ]. Anxiety levels were universally elevated in our cohort, with HAM-A scores frequently reaching moderate-to-severe ranges in AI-users. Younger age and higher educational attainment were associated with greater anxiety severity, a finding consistent with prior studies suggesting that individuals with higher health literacy may paradoxically be more vulnerable to health anxiety due to increased exposure to medical information and overinterpretation of risk [ 19 ]. The amplification of anxiety among patients with personal or familial experiences of neurological disease is seen. Our findings suggest that repeated exposure to AI-generated health information may act as a reinforcing mechanism, sustaining catastrophic interpretations and confirmation bias. This is particularly problematic in ALS, where the emotional salience of the diagnosis amplifies fear of irreversible disability and death [ 12 ]. At a broader level, our findings raise ethical and design considerations for AI-driven health technologies. Responsible AI development in medicine must prioritize contextualized risk communication, transparent uncertainty, and safeguards against harm amplification, particularly for rare and fatal diseases [ 1 , 20 ]. The present study is subject to several methodological constraints, most notably a restricted cohort size; a more expansive and multicultural sample would be requisite to enhance statistical power and generalizability. Furthermore, the cross-sectional nature of the data precludes the establishment of definitive causal inferences. A primary concern remains the directionality of the observed association: it is unclear whether engagement with AI-based symptom checkers precipitates heightened anxiety or if individuals predisposed to health-related distress are more inclined to utilize these platforms. These factors probably operate within a bidirectional feedback loop, necessitating longitudinal investigations to elucidate the temporal dynamics between AI exposure and psychological outcomes. In conclusion, CyberAls-AI-driven anxiety surrounding ALS represents an emerging and underrecognized challenge at the intersection of neurology, mental health, and digital medicine. Clinicians must adapt to the evolving informational environment in which patients form illness beliefs and be ready for overexposition of benign symptoms. Furthermore, AI developers must consider psychological safety as a core outcome. Abbreviations AI – Artificial Intelligence ALS – Amyotrophic Lateral Sclerosis CIs – Confidence intervals HAM-A – Hamilton Anxiety Rating Scale MRC – Medical Research Council ORs – Adjusted odds ratios PGT – Paper grip test TSMU – Tbilisi State Medical University Declarations ● Ethics approval and consent to participate The study adhered to the principles of the Declaration of Helsinki and was approved by the ethics committee of Tbilisi State Medical University (Date: 8th June 2020, approval no. N3-2020/80). Informed consent was obtained from all participants. ● Consent for publication All Authors' Consent for publication ● Availability of data and materials Supporting data is available from the corresponding author upon a reasonable request ● Competing Interests The authors declare that there are no conflicts of interest relevant to this work. ● Funding This research was supported by the Shota Rustaveli National Science Foundation of Georgia( SRNSFG) under Grant number: YS-24-4131 ● Authors' contributions MK contributed to the conception and design of the study Sh.V, N.K, MB, MK participated in the recruitment of patients and data collection. RK, MB, EK, MK contributed to the analysis and interpretation of data. MB, HH, Sh. V and RK have been involved in drafting the manuscript, revising it and all authors have given final approval for the version to be published. ● Acknowledgements We would like to express our gratitude to the patients for their willingness to participate in this study. Biographical notes: Mariam Kekenadze - MD, PhD, has defended her PhD researching ALS in Georgia, founder of ALS Association Georgia, Neurologist, Tbilisi State Medical University, Clinical Neurophysiologist, Specializes at Neuromuscular disorders, works at University clinic of Tbilisi State Medical University. Has completed a Fellowship at UCL Institute of Neurology Neurogenetics Lab under the supervision of Prof.Henry Houlden. At this moment, finalizes Msc in clinical neurology at UCL. Rauan Kaiyrzhanov is a research fellow from Kazakhstan who is interested in the genetics of rare neurological disorders in Central Asian and Transcaucasian countries. He instigated a scientific collaboration between IoN UCL and several medical universities as well as medical research centres from Kazakhstan, Tajikistan, Azerbaijan, Armenia, and Georgia. He has recruited many patients with rare and clinically interesting phenotypes of neurogenetic conditions from these countries to the Synapse project. Nana Kvirkvelia - Professor of Neurology, Ivane Javakhishvili Tbilisi State University, works as a clinical neurophysiologist at Petre Sarajishvili Institute of Neurology, specializes at MG, MND Shorena Vashadz e- Professor of Neurology, Batumi Shota Rustaveli state University, clinical practice for more than 30 year, works at Medcenter, Batumi , Georgia. Eka Kvaratskhelia - Ass. Professor of Department of Molecular and Medical Genetics at Tbilisi State Medical University, the Research Laboratory of Molecular Genetics and Epigenetics was established in 2014 by the effort of Prof. E. Abzianidze and Associate Prof. E. Kvaratskhelia in the Department. Several Scientific and PhD programs are being carried out in the lab. Maia Beridze- the Head of Neurological Department, The First University Clinic of Tbilisi State Medical University, Neurologist, Scientific degree: the Doctor of Medicine. Professor Henry Houlden - Professor of Neurology, head of genetics lab at Queen Square Institute of Neurology,has clinical expertise in inherited neurological disorders, movement disorders such as multiple system atrophy, ataxia, leukodystrophy, epilepsy, and paroxysmal conditions, spastic paraplegia, and neuromuscular conditions. He undertakes research laboratory work on neurogenetics and movement disorders with a particular interest in rare diseases that are adult or childhood-onset, such as multiple system atrophy (MSA), spinocerebellar ataxia and other movement disorders, inherited neuromuscular conditions and difficult-to-diagnose disorders. Particularly in diverse and underrepresented populations. . References Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–8. Starcevic V, Berle D. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8986902","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619039160,"identity":"5f246045-890c-4709-afcb-753688165a24","order_by":0,"name":"Mariam Kekenadze","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYJCCA0BswMADYrIxMPCD6IQCUrRINoC0GBC2CaHF4ACEjxPotp99eOBnDoMxP8/hYw9+lNXJGZ9fnfjhgQGDPL/YAaxazM6kGxzs3cZgJtnblm7Yc47N2OzG280SQIcZzpydgF3LgTSGA7zbGGwMzvOYSfC28SRuu3F2A0hLgsFtHFrOP2M4+Beoxf48/zfJv20S9ZtnnN38A6+WG2kMh4G2mBnw9rBJ87YZJBjw927Db8uNZwyHZbdJGEucOWYmLXMuwXDGDd5tFgkGErj9cj6N+ePbbTaG/T3JzyTflNXJ8/ef3XzzR4WNPL80di1QIIHMTkAXIQj4D5CiehSMglEwCkYAAACQHl8Co2E/FgAAAABJRU5ErkJggg==","orcid":"","institution":"Tbilisi State Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mariam","middleName":"","lastName":"Kekenadze","suffix":""},{"id":619039161,"identity":"63e30471-78d4-4509-a6d6-2f7873fa63c1","order_by":1,"name":"Shorena Vashadze","email":"","orcid":"","institution":"Batumi Shota Rustaveli State University","correspondingAuthor":false,"prefix":"","firstName":"Shorena","middleName":"","lastName":"Vashadze","suffix":""},{"id":619039162,"identity":"3898a223-2512-491a-a9ad-e8cb89c931b4","order_by":2,"name":"Nana Kvirkvelia","email":"","orcid":"","institution":"Tbilisi State University","correspondingAuthor":false,"prefix":"","firstName":"Nana","middleName":"","lastName":"Kvirkvelia","suffix":""},{"id":619039163,"identity":"9a9fcae1-b927-4a31-9ad8-2551f15160f0","order_by":3,"name":"Eka Kvaratskhelia","email":"","orcid":"","institution":"Tbilisi State Medical University","correspondingAuthor":false,"prefix":"","firstName":"Eka","middleName":"","lastName":"Kvaratskhelia","suffix":""},{"id":619039164,"identity":"baa4f3cf-5135-4f55-b69b-c73ff5ced701","order_by":4,"name":"Maia Beridze","email":"","orcid":"","institution":"Tbilisi State Medical University","correspondingAuthor":false,"prefix":"","firstName":"Maia","middleName":"","lastName":"Beridze","suffix":""},{"id":619039165,"identity":"2069995f-b230-4382-bdec-d5cb08abec71","order_by":5,"name":"Henry Houlden","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"","lastName":"Houlden","suffix":""},{"id":619039166,"identity":"374206af-0ded-405c-b5ee-342d4e6f0ab1","order_by":6,"name":"Rauan Kaiyrzhanov","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Rauan","middleName":"","lastName":"Kaiyrzhanov","suffix":""}],"badges":[],"createdAt":"2026-02-27 10:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8986902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8986902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106534949,"identity":"48b780a8-494c-4671-af67-19c609224a66","added_by":"auto","created_at":"2026-04-09 15:07:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238418,"visible":true,"origin":"","legend":"\u003cp\u003eChart flow demonstrating the inclusion process of the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8986902/v1/2cb4b33ba8b855316df0aca0.png"},{"id":107480312,"identity":"07127714-48e0-4508-a568-f0ccf92fb75f","added_by":"auto","created_at":"2026-04-22 02:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":777792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8986902/v1/4d93491c-2364-4c3b-9886-b974329a0941.pdf"},{"id":106726423,"identity":"d7aaced1-a8f9-4b9e-82b4-5afe0ed8ad24","added_by":"auto","created_at":"2026-04-12 18:36:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10188,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8986902/v1/da6904acb3aba2cbc8d837a6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"CyberALS, a phenomenon that neurologists should worry about?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe healthcare sector is undergoing a revolution, with artificial intelligence (AI) playing a pivotal role in transforming how we diagnose and treat diseases. AI\u0026rsquo;s ability to process vast amounts of data quickly and accurately is reshaping the medical landscape internationally and locally. Additionally, everything is smoothly and quietly integrating into the health system through symptom checkers, conversational agents, and decision-support tools used directly by the public. These technologies, at best, aim to improve access to medical knowledge and empower patients in health-related decision-making. However, emerging evidence suggests that AI-mediated health information may also have unintended psychological consequences, particularly when probabilistic or decontextualized outputs are interpreted without clinical guidance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. When asking ChatGPT \u0026ldquo;Why do I have fasciculations?\u0026rdquo; in the response, it is mentioned that \u0026ldquo;Fasciculations alone do not indicate ALS\u0026rdquo;, however even this output alone could be detrimental for the health-anxious person.\u003c/p\u003e \u003cp\u003eOne well-described phenomenon in this context is \u003cem\u003ecyberchondria\u003c/em\u003e, defined as excessive or repetitive online searching for health information associated with increased anxiety and distress [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies have demonstrated that individuals with high health anxiety or intolerance of uncertainty are particularly vulnerable, often entering a self-reinforcing cycle in which reassurance-seeking paradoxically amplifies fear [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although early cyberchondria research focused primarily on traditional internet searches, the rapid evolution of AI-driven symptom checkers and large language models introduces new dimensions, including perceived authority, personalization, and persuasive explanatory styles that may further intensify anxiety responses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent evaluations of AI symptom checkers highlight substantial variability in diagnostic accuracy and risk communication, especially for rare or complex neurological conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Trust in AI outputs has been associated with depending on framing, explanation, and presentation of uncertainty, factors that directly influence user anxiety and decision-making behavior [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While some digital tools may reduce anxiety by guiding appropriate care-seeking, for instance, research showed AI chatbots contributed to modest enhancements in life satisfaction and well-being [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] others may inadvertently reinforce catastrophic interpretations, particularly in users with limited clinical knowledge [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese concerns are especially relevant in the context of amyotrophic lateral sclerosis (ALS), a rare but universally fatal neurodegenerative disease with profound psychological impact not only on patients, but also on caregivers, and with no simple biomarker to prove or disprove diagnosis, only a combination of clinical/electrophysiological findings to support diagnosis through modern criteria. Raising awareness of ALS in public, combined with the nonspecific nature of early neuromuscular symptoms such as fasciculations, fatigue, or perceived weakness, creates fertile ground for misinterpretation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In clinical practice, fear of ALS is a recognized driver of neurological consultation among individuals ultimately found to have benign conditions; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] however, the role of AI-mediated information in shaping this fear has not been systematically examined.\u003c/p\u003e \u003cp\u003eAI-driven anxiety surrounding ALS, given the name CyberALS by us, therefore, represents an emerging challenge at the intersection of neurology, mental health, and digital medicine globally.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eThis study was designed and conducted at \u003cb\u003eThe First University Clinic of Tbilisi State Medical University (TSMU) and Medcenter Batumi ALS clinics\u003c/b\u003e. 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. Following comprehensive neurological examination and appropriate diagnostic investigations, 220 individuals were determined to have benign symptoms without evidence of neuromuscular disease and were included in the analysis; these were 120 female and 100 male patients, aged 18\u0026ndash;72 years, applying for evaluation of neuromuscular symptoms accompanied by a persistent fear of amyotrophic lateral sclerosis (ALS). 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 using a structured questionnaire (see supplementary material 1). Anxiety severity was assessed in all participants using the Hamilton Anxiety Rating Scale (HAM-A) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and anxiety levels were compared between AI users and non-users.\u003c/p\u003e \u003cp\u003e The study adhered to the principles of the Declaration of Helsinki and was approved by the ethics committee of Tbilisi State Medical University (Date: 8th June 2020, approval no. N3-2020/80). Informed consent was obtained from all participants. Eligible patients were presenting with complaints of weakness, muscle atrophy, fasciculations, for whom concern about ALS constituted a dominant reason for referral or repeated healthcare seeking. 143 included patients reported prior or ongoing exposure to AI-based symptom checkers, automated diagnostic platforms, or generative AI tools, which they identified as influential in shaping their belief or fear that they might have ALS and seeking medical advice. 77 Patients who were not exposed to AI tools were assessed for anxiety levels as well. Those who were diagnosed with neurological disorders during the investigation process were excluded. The flow of the study inclusion can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinical evaluation was performed by motor neuron disease neurologists and included a comprehensive medical history and a detailed neurological examination. Investigations, including electromyography (EMG) and nerve conduction studies(NCS), neuroimaging, and relevant laboratory tests, in accordance with established diagnostic standards and routine clinical practice, were performed.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression was performed to identify independent predictors of severe anxiety (HAM-A\u0026thinsp;\u0026ge;\u0026thinsp;25). Variables were selected a priori based on clinical relevance and univariable associations. Results are presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eIn parallel with the neurological assessment, the psychological and contextual dimensions of ALS-related fear were explored. Exposure to AI-driven health information was assessed through a structured clinical questionnaire (see supplementary material 1), documenting the types of AI tools used, frequency and duration of use, and the extent to which AI-generated outputs referenced ALS or motor neuron disease. Patients' anxiety levels were examined by using the Hamilton Anxiety Rating Scale (HAM-A) and compared in AI users and non-users. AI-using patients were asked to describe AI-using characteristics, type of AI platform used, frequency of AI use, primary symptoms searched, patient perception of AI information, typical AI-generated interpretations reported by patients and behavioral impact of AI use. Prior personal or familial experiences with neurodegenerative disease were explored, and education level was documented in AI and non AI-users.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong 220 individuals, 143 reported regular use of AI-based symptom checkers or generative AI platforms before referral and persistent fear of having amyotrophic lateral sclerosis (ALS). A substantial proportion of patients had higher educational attainment (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNeurological examination showed no objective evidence of upper or lower motor neuron dysfunction. None of the patients met clinical or electrophysiological criteria for motor neuron disease; All patients underwent electromyography and nerve conduction studies, and they showed no signs of active or chronic denervation. In some cases, ENMG was performed multiple times for reassurance, with different specialists. In all patients, muscle bulk and strength were preserved on formal testing using the MRC motor grading system, hand grip test, and paper grip test (PGT) for the limbs, despite subjective reports of weakness and perceived muscle loss. Fasciculations were commonly reported and were most often diffuse, intermittent, and migratory in nature. These were frequently exacerbated by stress, fatigue, or heightened symptom monitoring. All patients demonstrated elevated anxiety levels on the Hamilton Anxiety Rating Scale (HAM-A). Elevated HAM-A scores were positively correlated with both younger age and higher educational attainment within the AI-user group. Non-users of AI platforms demonstrated lower anxiety indices, suggesting that the absence of AI-mediated health assessments correlates with reduced psychological distress in early adulthood. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). AI user -patients reporting personal or familial experiences with neurological or neurodegenerative disease exhibited particularly pronounced anxiety and devastating interpretation of benign symptoms.(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAnxiety severity (HAM-A) stratified by demographic characteristics and AI use in patients with ALS-related health anxiety (AI users n\u0026thinsp;=\u0026thinsp;143; non-AI users n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAI users (n\u0026thinsp;=\u0026thinsp;143) Mild n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerate n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSevere n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNon-AI users (n\u0026thinsp;=\u0026thinsp;77) Mild n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerate n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSevere n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u0026dagger;\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;30 years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3 (7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13 (31.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e25 (61.0)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9 (37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11 (45.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4 (16.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u0026ndash;44 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7 (16.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21 (48.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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(40.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4 (14.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;60 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8 (32.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12 (48.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5 (20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11 (52.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8 (38.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2 (9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12 (14.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35 (42.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35 (42.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16 (39.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18 (43.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7 (17.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16 (26.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26 (42.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19 (31.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20 (55.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14 (38.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15 (31.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20 (41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13 (27.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18 (60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9 (30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3 (10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHigher education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13 (13.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41 (43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41 (43.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18 (38.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23 (48.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6 (12.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e28 (19.6)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e61 (42.7)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e54 (37.7)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e36 (46.8)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e32 (41.6)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e9 (11.6)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 1. \u003cem\u003eHAM-A: Hamilton Anxiety Rating Scale. Mild anxiety: 8\u0026ndash;17; moderate: 18\u0026ndash;24; severe: \u0026ge;25. \u003c/em\u003e\u003cem\u003e\u0026dagger;Pearson \u0026chi;\u0026sup2; test used for categorical comparisons; \u0026chi;\u0026sup2; test for trend applied for ordered age groups. Statistical significance is defined as p \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatients who reported AI-based symptom checker or generative AI use demonstrated significantly higher anxiety severity compared with non-AI users (p \u0026lt; 0.001). Severe anxiety was markedly more prevalent among AI users, particularly in younger patients (\u0026le;30 years), whereas non-AI users more frequently exhibited mild or moderate anxiety. These findings suggest that AI-mediated health information is associated with an increased anxiety burden in patients presenting with ALS-related health concerns.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatients who reported AI-based symptom checker or generative AI use demonstrated significantly higher anxiety severity compared with non-AI users (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Severe anxiety was markedly more prevalent among AI users, particularly in younger patients (\u0026le;\u0026thinsp;30 years), whereas non-AI users more frequently exhibited mild or moderate anxiety. These findings suggest that AI-mediated health information is associated with an increased anxiety burden in patients presenting with ALS-related health concerns.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePatterns of AI-based symptom checker and generative AI use among patients with CyberALS (n\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAI use characteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eType of AI platform used\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSymptom checker applications\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98 (68.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGenerative AI platforms (chatbots, LLMs)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e121 (84.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoth symptom checkers and generative AI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e76 (53.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequency of AI use\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional (\u0026le;\u0026thinsp;1 time/week)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32 (22.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegular (2\u0026ndash;4 times/week)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49 (34.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent (\u0026ge;\u0026thinsp;5 times/week or daily)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62 (43.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary symptoms searched\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFasciculations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e118 (82.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuscle weakness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e104 (72.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuscle atrophy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79 (55.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBulbar symptoms (speech/swallowing)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41 (28.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTypical AI-generated interpretations reported by patients\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALS is listed as a possible or likely diagnosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e109 (76.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmphasis on progressive or fatal outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94 (65.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiscussion of benign alternatives\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e87 (60.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient perception of AI information\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConsidered AI output authoritative or highly reliable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101 (70.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConsidered AI output more reliable than clinician reassurance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64 (44.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSomewhat reliable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30 (21.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot reliable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12 (8.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral impact of AI use\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased symptom monitoring or self-examination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e112 (78.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRepeated reassurance-seeking (medical consultations)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96 (67.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiscuss symptoms with family/friends\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83 (58.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSearch for the same symptoms again online, and continue AI use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107 (74.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDid not affect my behaviour\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21 ( 14.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\n\u003cp\u003eTable 2. \u003cem\u003eTable is representing results of the AI questionnaire in AI users. Type of AI platform used, frequency of AI use, primary symptoms searched, typical AI-generated interpretations reported by patients, patient perception of AI information, and behavioral impact of AI use. Percentages may exceed 100% where multiple responses were permitted. AI: artificial intelligence; ALS: amyotrophic lateral sclerosis.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable logistic regression analysis of factors associated with severe anxiety (HAM-A\u0026thinsp;\u0026ge;\u0026thinsp;25), stratified by AI use\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAI users (n\u0026thinsp;=\u0026thinsp;143) Adjusted OR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNon-AI users (n\u0026thinsp;=\u0026thinsp;77) Adjusted OR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;30 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.21 (1.54\u0026ndash;6.71)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.42 (0.48\u0026ndash;4.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.88 (1.02\u0026ndash;3.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.21 (0.39\u0026ndash;3.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigher education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.63 (1.32\u0026ndash;5.25)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34 (0.44\u0026ndash;4.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePersonal/familial neurological disease history\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.91 (0.96\u0026ndash;3.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 (0.36\u0026ndash;3.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent AI use (\u0026ge;\u0026thinsp;5 times/week)*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.07 (1.49\u0026ndash;6.31)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eTable 3. Binary logistic regression models were performed separately for AI users and non-AI users. Outcome variable: severe anxiety (HAM-A \u0026ge;25). Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) are shown. Statistical significance is defined as p \u0026lt; 0.05. Frequent AI use is included only in the AI-user model.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn stratified multivariable analysis, younger age, higher educational attainment, and frequent AI engagement remained independently associated with severe anxiety among AI users, whereas no demographic variable independently predicted severe anxiety in non-AI users(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings suggest that the anxiety gradient observed in the overall cohort may be largely driven by patterns of AI exposure rather than demographic vulnerability alone.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides evidence that exposure to AI-driven health information is associated with increased anxiety related to amyotrophic lateral sclerosis (ALS), even in the absence of objective neurological disease. Within our cohort of 220 patients, ALS-related fear was significantly more pronounced among individuals who reported using AI-based symptom checkers or generative AI platforms compared with non-AI users, despite normal clinical examinations and investigative findings. These results extend the established concept of cyberchondria to the context of generative artificial intelligence, highlighting a novel form of disease-specific anxiety within neurological practice.\u003c/p\u003e \u003cp\u003eHealth anxiety disproportionate to clinical findings is a well-recognized phenomenon in neurological practice, particularly in patients presenting with benign neuromuscular symptoms such as fasciculations or subjective weakness [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our findings suggest that AI-based symptom checkers and generative platforms may intensify this anxiety by presenting rare but severe diagnoses alongside common, nonspecific symptoms, without adequate contextualization of probability or prevalence. This aligns with prior work demonstrating that digital symptom tools often overemphasize serious conditions to avoid false reassurance, inadvertently increasing user distress [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFasciculations and subjective weakness were the predominant presenting symptoms, consistent with prior literature describing benign fasciculation syndrome and anxiety-related neuromuscular complaints as common reasons for neurological referral [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Importantly, none of the patients demonstrated clinical progression, objective weakness, muscle wasting, or electrophysiological evidence of denervation\u0026mdash;features that are crucial to the diagnosis of ALS [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This further supports existing evidence that fasciculations in isolation, particularly when widespread and fluctuating, are rarely indicative of motor neuron disease [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnxiety levels were universally elevated in our cohort, with HAM-A scores frequently reaching moderate-to-severe ranges in AI-users. Younger age and higher educational attainment were associated with greater anxiety severity, a finding consistent with prior studies suggesting that individuals with higher health literacy may paradoxically be more vulnerable to health anxiety due to increased exposure to medical information and overinterpretation of risk [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The amplification of anxiety among patients with personal or familial experiences of neurological disease is seen.\u003c/p\u003e \u003cp\u003eOur findings suggest that repeated exposure to AI-generated health information may act as a reinforcing mechanism, sustaining catastrophic interpretations and confirmation bias. This is particularly problematic in ALS, where the emotional salience of the diagnosis amplifies fear of irreversible disability and death [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt a broader level, our findings raise ethical and design considerations for AI-driven health technologies. Responsible AI development in medicine must prioritize contextualized risk communication, transparent uncertainty, and safeguards against harm amplification, particularly for rare and fatal diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study is subject to several methodological constraints, most notably a restricted cohort size; a more expansive and multicultural sample would be requisite to enhance statistical power and generalizability. Furthermore, the cross-sectional nature of the data precludes the establishment of definitive causal inferences. A primary concern remains the directionality of the observed association: it is unclear whether engagement with AI-based symptom checkers precipitates heightened anxiety or if individuals predisposed to health-related distress are more inclined to utilize these platforms. These factors probably operate within a bidirectional feedback loop, necessitating longitudinal investigations to elucidate the temporal dynamics between AI exposure and psychological outcomes.\u003c/p\u003e \u003cp\u003eIn conclusion, CyberAls-AI-driven anxiety surrounding ALS represents an emerging and underrecognized challenge at the intersection of neurology, mental health, and digital medicine. Clinicians must adapt to the evolving informational environment in which patients form illness beliefs and be ready for overexposition of benign symptoms. Furthermore, AI developers must consider psychological safety as a core outcome.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAI – Artificial Intelligence\u003c/p\u003e\n\u003cp\u003eALS – Amyotrophic Lateral Sclerosis\u003c/p\u003e\n\u003cp\u003eCIs – Confidence intervals\u003c/p\u003e\n\u003cp\u003eHAM-A – Hamilton Anxiety Rating Scale\u003c/p\u003e\n\u003cp\u003eMRC – Medical Research Council\u003c/p\u003e\n\u003cp\u003eORs – Adjusted odds ratios\u003c/p\u003e\n\u003cp\u003ePGT – Paper grip test\u003c/p\u003e\n\u003cp\u003eTSMU – Tbilisi State Medical University\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e● Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study adhered to the principles of the Declaration of Helsinki and was approved by the ethics committee of Tbilisi State Medical University (Date: 8th June 2020, approval no. N3-2020/80). Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e● Consent for publication\u003c/p\u003e\n\u003cp\u003eAll Authors' Consent for publication\u003c/p\u003e\n\u003cp\u003e● Availability of data and materials\u003c/p\u003e\n\u003cp\u003eSupporting data is available from the corresponding author upon a reasonable request\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e● Competing Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest relevant to this work.\u003c/p\u003e\n\u003cp\u003e● Funding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Shota Rustaveli National Science Foundation of Georgia( SRNSFG) under Grant number: YS-24-4131\u003c/p\u003e\n\u003cp\u003e● Authors' contributions\u003c/p\u003e\n\u003cp\u003eMK contributed to the conception and design of the study Sh.V, N.K, MB, MK participated in the recruitment of patients and data collection. RK, MB, EK, MK contributed to the analysis and interpretation of data. MB, HH, Sh. V and RK \u0026nbsp;have been involved in drafting the manuscript, revising it and all authors have given final approval for the version to be published.\u003c/p\u003e\n\u003cp\u003e● Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the patients for their willingness to participate in this study.\u003c/p\u003e\n\u003cp\u003eBiographical notes:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMariam Kekenadze\u003c/strong\u003e- \u0026nbsp;MD, PhD, \u0026nbsp; has defended her PhD researching ALS in Georgia, founder of ALS Association Georgia, Neurologist, Tbilisi State Medical University, Clinical Neurophysiologist, Specializes at Neuromuscular disorders, works at University clinic of Tbilisi State Medical University. Has completed a Fellowship at UCL Institute of Neurology Neurogenetics Lab under the supervision of Prof.Henry Houlden. At this moment, finalizes Msc in clinical neurology at UCL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRauan Kaiyrzhanov\u003c/strong\u003e is a research fellow from Kazakhstan who is interested in the genetics of rare neurological disorders in Central Asian and Transcaucasian countries. He instigated a scientific collaboration between IoN UCL and several medical universities as well as medical research centres from Kazakhstan, Tajikistan, Azerbaijan, Armenia, and Georgia. He has recruited many patients with rare and clinically interesting phenotypes of neurogenetic conditions from these countries to the Synapse project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNana Kvirkvelia\u003c/strong\u003e- Professor of Neurology, Ivane Javakhishvili Tbilisi State University, works as a clinical neurophysiologist at Petre Sarajishvili Institute of Neurology, specializes at MG, MND\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShorena Vashadz\u003c/strong\u003ee- Professor of Neurology, Batumi Shota Rustaveli state University, clinical practice for more than 30 year, works at Medcenter, Batumi , Georgia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEka Kvaratskhelia\u003c/strong\u003e- Ass. Professor of Department of Molecular and Medical Genetics at Tbilisi State Medical University, the Research Laboratory of Molecular Genetics and Epigenetics was established in 2014 by the effort of Prof. E. Abzianidze and Associate Prof. E. Kvaratskhelia in the Department. Several Scientific and PhD programs are being carried out in the lab.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaia Beridze- the\u0026nbsp;\u003c/strong\u003eHead of Neurological Department, The First University Clinic of Tbilisi State Medical University, Neurologist, \u0026nbsp;Scientific degree: the Doctor of Medicine. Professor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHenry Houlden -\u0026nbsp;\u003c/strong\u003eProfessor of Neurology, head of genetics lab at Queen Square Institute of Neurology,has clinical expertise in inherited neurological disorders, movement disorders such as multiple system atrophy, ataxia, leukodystrophy, epilepsy, and paroxysmal conditions, spastic paraplegia, and neuromuscular conditions. He undertakes research laboratory work on neurogenetics and movement disorders with a particular interest in rare diseases that are adult or childhood-onset, such as multiple system atrophy (MSA), spinocerebellar ataxia and other movement disorders, inherited neuromuscular conditions and difficult-to-diagnose disorders. Particularly in diverse and underrepresented populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTopol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStarcevic V, Berle D. Cyberchondria: towards a better understanding of excessive health-related Internet use. Expert Rev Neurother. 2013;13(2):205\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFergus TA. Cyberchondria and intolerance of uncertainty: examining when individuals experience health anxiety in response to Internet searches for medical information. Cyberpsychol Behav Soc Netw. 2013;16(10):735\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlease C, Kaptchuk TJ, Bernstein MH, Mandl KD, Halamka JD, DesRoches CM. Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners\u0026rsquo; views. J Med Internet Res. 2019;21(3):e12802.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNadarzynski T, Miles O, Cowie A, Ridge D. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study. Digit Health. 2019;5:2055207619871808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSemigran HL, Linder JA, Gidengil C, Mehrotra A. Evaluation of symptom checkers for self diagnosis and triage. BMJ. 2015;351:h3480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser H, Coiera E, Wong D. Safety of patient-facing digital symptom checkers. Lancet. 2018;392(10161):2263\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBu\u0026ccedil;inca Z, Lin PH, Gajos KZ, Glassman EL. Proxy tasks and trust calibration in human-AI collaboration. Proc ACM Hum-Comput Interact. 2020;4(CSCW2):1\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng X, Tian L, Ho GWK, Yorke J, Hui V. The Effectiveness of AI Chatbots in Alleviating Mental Distress and Promoting Health Behaviors Among Adolescents and Young Adults: Systematic Review and Meta-Analysis. J Med Internet Res. 2025;27:e79850. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/79850\u003c/span\u003e\u003cspan address=\"10.2196/79850\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2025 Nov 26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray E, Lo B, Pollack L, et al. The impact of health information on health anxiety: a systematic review. J Psychosom Res. 2003;54(6):529\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijesekera LC, Leigh PN. Amyotrophic lateral sclerosis. Orphanet J Rare Dis. 2009;4:3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStone J, Carson A, Duncan R, et al. Who is referred to neurology clinics?\u0026mdash;the diagnoses made in 3781 new patients. Clin Neurol Neurosurg. 2010;112(9):747\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton M. The Assessment of Anxiety States by Rating. 32. Br J Med Psychol 50\u0026ndash;5. 1959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLayzer RB. The origin of muscle fasciculations and cramps. Muscle Nerve. 1994;17(11):1243\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Carvalho M, Swash M. Fasciculation potentials: a study of amyotrophic lateral sclerosis and other neurogenic disorders. Muscle Nerve. 1998;21(3):336\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooks BR, Miller RG, Swash M, Munsat TL, World Federation of Neurology Research Group on Motor Neuron Diseases. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. 2000;1(5):293\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho MD, Swash M. Benign fasciculation syndrome: a follow-up study. Muscle Nerve. 2004;29(3):357\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuse K, McManus F, Leung C, Meghreblian B, Williams JM. Cyberchondriasis: fact or fiction? A preliminary examination of the relationship between health anxiety and searching for health information on the internet. J Anxiety Disord. 2012;26(1):189\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFloridi L, Cowls J. A unified framework of five principles for AI in society. Harv Data Sci Rev. 2019;1(1).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI, ALS, Fasciculations, anxiety","lastPublishedDoi":"10.21203/rs.3.rs-8986902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8986902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eAmyotrophic 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)\u0026ndash;driven health information tools\u0026mdash;such as symptom checkers, large language models, and automated risk interpretation platforms\u0026mdash;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.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eBetween 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAmong 220 patients evaluated (2021\u0026ndash;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\u0026ndash;30) range. Higher anxiety scores were significantly more frequent among younger patients (age 22\u0026ndash;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.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis 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.\u003c/p\u003e","manuscriptTitle":"CyberALS, a phenomenon that neurologists should worry about?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 15:07:18","doi":"10.21203/rs.3.rs-8986902/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7194dda6-dfc3-4dc1-a526-fb62de65b160","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T08:57:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 15:07:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8986902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8986902","identity":"rs-8986902","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}