Behavioral Intention to Use Artificial Intelligence Enabled Health Screening among Urban Adults in India

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However, limited population‑level evidence exists on public acceptance of such technologies in low‑ and middle‑income countries (LMICs), where concerns regarding trust, privacy, and regulatory oversight may hinder adoption. Objective To quantify public acceptance of AI‑based health screening tools and to identify socio‑demographic, technological, and perceptual determinants of behavioral intention to use such tools among adults in Hyderabad, India. Methods A community‑based cross‑sectional study was conducted among 1,512 adults using a structured questionnaire grounded in the Technology Acceptance Model and an extended trust–risk framework. The primary outcome was behavioral intention to use AI‑based screening. Multivariable logistic regression was used to identify independent predictors after adjusting for demographic and digital‑access confounders. Results Overall, 981 participants (64.9%) expressed willingness to use AI‑based screening. Perceived usefulness (adjusted odds ratio [aOR] 2.36, 95% CI 2.05–2.72) and trust in AI and healthcare institutions (aOR 1.89, 95% CI 1.63–2.19) were strong positive predictors, whereas privacy concerns (aOR 0.66, 95% CI 0.58–0.75) and perceived diagnostic risk (aOR 0.72, 95% CI 0.63–0.83) were independently associated with lower acceptance. Government certification (82.4%) and mandatory physician confirmation (75.6%) were the most frequently endorsed trust‑building interventions. The median willingness‑to‑pay for AI screening was ₹50–100. Conclusion Public acceptance of AI‑enabled screening in urban India is moderate and is strongly shaped by trust, perceived utility, and data‑protection concerns. Regulatory oversight, clinical integration, and user‑centered design are essential prerequisites for ethical and effective deployment of AI‑based population screening programs in LMIC settings. artificial intelligence digital health population screening technology acceptance health policy India Introduction Artificial intelligence (AI) has rapidly transitioned from experimental clinical applications to large‑scale deployment in diagnostic imaging, clinical decision support, and population‑level risk prediction (Topol 2023; Rajpurkar et al. 2022). AI‑enabled screening tools—delivered through mobile applications, kiosks, or wearable devices—are increasingly proposed as cost‑effective strategies to expand early detection of non‑communicable diseases (NCDs), optimize limited healthcare workforces, and reduce outpatient congestion in urban health systems [ 2 ]. India faces a dual challenge of a rapidly rising NCD burden, accounting for more than 60% of total mortality, and persistent shortages of trained healthcare professionals. National initiatives such as the Ayushman Bharat Digital Mission explicitly promote algorithm‑driven risk stratification and telehealth‑enabled screening pathways to address these constraints (Gopalakrishnan and Ganeshkumar 2022). However, technological feasibility alone does not guarantee real‑world effectiveness. Public trust, perceived usefulness, usability, and confidence in data governance play decisive roles in determining whether individuals are willing to engage with algorithmic health services (Choudhury and Asan 2023). Evidence from high‑income countries suggests that perceived usefulness, transparency, and institutional trust are central drivers of acceptance of AI in healthcare, whereas privacy concerns and fear of diagnostic error act as major deterrents (Alowais et al. 2022; Gerke et al. 2022). Extrapolation of these findings to LMIC settings remains limited, as digital literacy, regulatory maturity, and historical experiences with healthcare quality vary substantially. Urban India represents a particularly important context: high smartphone penetration coexists with substantial socioeconomic inequality, fragmented healthcare delivery, and heterogeneous exposure to digital health services. Without population‑level evidence on public attitudes, large‑scale AI‑based screening initiatives risk poor uptake, inequitable utilization, and erosion of trust in health systems. This study therefore aimed to assess public acceptance of AI‑enabled health screening tools in a large urban Indian population and to identify modifiable socio‑demographic and perceptual determinants of behavioral intention to use such technologies. Conceptual framework and hypotheses The study was guided by an extended Technology Acceptance Model (TAM), incorporating constructs of perceived usefulness, perceived ease of use, institutional trust, privacy concern, and perceived diagnostic risk (Li et al. 2022; Dwivedi et al. 2023). We hypothesized that: H1. Higher perceived usefulness is positively associated with willingness to use AI‑based screening. H2. Higher perceived ease of use is positively associated with willingness to use AI‑based screening. H3. Greater trust in AI systems and healthcare institutions is positively associated with willingness to use AI‑based screening. H4. Higher privacy concern is negatively associated with willingness to use AI‑based screening. H5. Higher perceived diagnostic risk is negatively associated with willingness to use AI‑based screening. H6. Prior exposure to digital health services (telemedicine or health‑app use) increases acceptance of AI‑based screening. Methods Study design and setting A community‑based cross‑sectional study was conducted in Hyderabad, Telangana, India, between March and June 2026. Study population Adults aged 18 years or older who had resided in Hyderabad for at least six months were eligible to participate. Sample size estimation Assuming a conservative prevalence of acceptance of 50%, with 95% confidence level and 3% absolute precision, the minimum required sample size was calculated as 1,067. To improve statistical power for multivariable modeling and subgroup analyses and to account for incomplete responses, the target sample size was increased to approximately 1,500 participants. Sampling technique Multisite convenience sampling was used, including recruitment from outpatient departments of tertiary hospitals, residential communities, public venues, and online survey dissemination. Recruitment was stratified by age group and sex to reduce sampling imbalance. Inclusion and exclusion criteria Inclusion criteria age ≥ 18 years; residence in Hyderabad for ≥ 6 months; ability to provide informed consent. Exclusion criteria cognitive impairment precluding consent; questionnaires with more than 20% missing responses. Data collection instrument A structured questionnaire was developed using validated constructs from the Technology Acceptance Model and recent AI‑acceptance literature (Alowais et al. 2022; Choudhury and Asan 2023). The instrument comprised sections on socio‑demographic characteristics, digital‑health exposure, perceived usefulness, perceived ease of use, trust in AI and institutions, privacy concerns, perceived diagnostic risk, cost sensitivity, behavioral intention, and solution‑oriented policy and design preferences. All construct items were rated on five‑point Likert scales. Outcome variable The primary outcome was behavioral intention to use AI‑based health screening, dichotomized as willing (Likert score ≥ 4) or not willing/unsure (Likert score ≤ 3). Statistical analysis Descriptive statistics were computed as mean ± standard deviation or median (interquartile range) for continuous variables and as frequencies with percentages for categorical variables. Internal consistency of construct scales was assessed using Cronbach’s alpha. Bivariate analyses were conducted using chi‑square tests, independent‑samples t‑tests, or Mann–Whitney U tests as appropriate. Multivariable logistic regression was performed to identify independent predictors of acceptance, adjusting for age, sex, education, income, smartphone ownership, and prior telemedicine use. Model diagnostics included variance inflation factors, Hosmer–Lemeshow goodness‑of‑fit test, and receiver operating characteristic (ROC) curve analysis. Statistical significance was set at p < 0.05. Analyses were conducted using SPSS version 29.0. Bias control A standardized definition of AI‑based screening was provided to all participants. Multivariable adjustment was used to control for confounding, and incomplete questionnaires were excluded from analysis. Clinical trial number: not applicable. Ethical considerations The study protocol was reviewed and approved by the Institutional Ethics Committee of Malla Reddy Vishwavidyapeeth, Hyderabad, Telangana, India , in accordance with the ethical standards laid down in the Declaration of Helsinki and relevant national guidelines. Results Sample characteristics Of 1,578 individuals approached, 66 questionnaires were excluded because of incomplete responses, resulting in a final analytical sample of 1,512 participants. The mean age was 36.9 ± 12.4 years; 52.3% were male, and 95.6% owned a smartphone. More than half (55.1%) had previously used telemedicine services. Digital‑health exposure Frequent use of health‑related mobile applications was reported by 41.7% of participants, and 61.3% indicated moderate or high familiarity with AI in healthcare. Only 46.2% had heard of AI‑based screening tools before participating in the study. Acceptance of AI‑based screening Overall, 981 participants (64.9%) reported willingness to use AI‑based screening within the next six months. Construct reliability and scores All construct scales demonstrated good internal consistency (Cronbach’s alpha range 0.78–0.87). Mean perceived‑usefulness score was 3.92 ± 0.69, trust score 3.43 ± 0.81, and privacy‑concern score 3.86 ± 0.73. Solution‑oriented preferences Government certification of AI screening systems was endorsed by 82.4% of respondents, and mandatory physician confirmation by 75.6%. A majority favored regional‑language interfaces (87.1%), voice assistance for elderly users (80.2%), and deployment in government hospitals (88.9%). Barriers and willingness‑to‑pay The most frequently reported barriers were privacy concerns (30.4%), fear of incorrect diagnosis (24.1%), and lack of trust (18.7%). The median willingness‑to‑pay for AI screening was ₹50–100. Multivariable predictors of acceptance In adjusted analyses, perceived usefulness (aOR 2.36, 95% CI 2.05–2.72), trust (aOR 1.89, 95% CI 1.63–2.19), perceived ease of use (aOR 1.31, 95% CI 1.15–1.49), prior telemedicine use (aOR 1.54, 95% CI 1.26–1.88), and higher education (aOR 1.27, 95% CI 1.05–1.54) were positively associated with acceptance. Privacy concern (aOR 0.66, 95% CI 0.58–0.75), perceived diagnostic risk (aOR 0.72, 95% CI 0.63–0.83), and age ≥ 60 years (aOR 0.69, 95% CI 0.52–0.92) were negatively associated with acceptance. The model demonstrated good calibration (Hosmer–Lemeshow p = 0.58) and discrimination (ROC‑AUC = 0.82). Discussion This large community‑based study provides robust evidence on public acceptance of AI‑enabled health screening tools in an urban Indian population. Approximately two‑thirds of respondents expressed willingness to use AI‑based screening, indicating moderate readiness for adoption but also highlighting substantial residual hesitancy. Determinants of acceptance Perceived usefulness emerged as the strongest predictor of acceptance, reinforcing prior findings that tangible health‑system benefits—such as early detection and reduced waiting times—are central to public adoption of digital health technologies (Alowais et al. 2022). Trust in AI systems and healthcare institutions was another critical determinant, consistent with systematic reviews emphasizing the centrality of institutional credibility in shaping technology acceptance (Choudhury and Asan 2023). Privacy concerns and perceived diagnostic risk were powerful deterrents. These findings align with ethical analyses highlighting data governance and algorithmic transparency as core challenges in AI‑driven healthcare (Gerke et al. 2022; WHO 2022). Notably, older adults were less likely to accept AI‑based screening, suggesting that age‑specific design and education strategies may be required to prevent digital exclusion. Policy and implementation implications Participants overwhelmingly supported government certification, clinical oversight, and hospital‑based deployment as prerequisites for trust. This suggests that purely commercial, app‑based screening models may face limited uptake unless embedded within formal healthcare systems and regulatory frameworks. Strong support for regional‑language interfaces and voice assistance further underscores the importance of inclusive design to reduce digital inequities. The observed median willingness‑to‑pay of ₹50–100 indicates substantial price sensitivity, implying that large‑scale implementation may require public financing or cross‑subsidization to ensure equitable access. Collectively, these findings support a hybrid model in which AI‑based screening tools are regulated by public authorities, clinically supervised, and integrated into existing healthcare infrastructure. Comparison with previous studies Acceptance levels in this study are comparable to those reported in other LMIC contexts but remain lower than those documented in several high‑income countries, where digital trust and regulatory maturity are greater (Kaur et al. 2023; Alowais et al. 2022). The relative importance of trust and privacy mirrors global patterns, suggesting that these constructs represent universal determinants of AI adoption in healthcare. Strengths and limitations Strengths of this study include the large sample size, theory‑driven design, incorporation of solution‑oriented policy questions, and rigorous multivariable analysis. Limitations include the use of convenience sampling, which may limit generalizability, and reliance on self‑reported intention rather than observed behavior. Longitudinal studies are needed to assess how stated intentions translate into actual utilization. Conclusion Public acceptance of AI‑enabled health screening in urban India is moderate and is strongly influenced by perceived usefulness, institutional trust, and concerns regarding privacy and diagnostic accuracy. Regulatory certification, clinical integration, transparent communication, and inclusive design are essential for ethical and effective deployment. Policymakers should view public trust as a critical infrastructure component of digital‑health strategies, alongside technological development. Declarations Ethical approval The study protocol was approved by the Institutional Ethics Committee of Malla Reddy Vishwavidyapeeth, Hyderabad, Telangana, India, and was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. Consent to participate Written or digital informed consent was obtained from all participants prior to participation in the study. Consent to publish Informed consent for participation and publication of anonymized data was obtained from all participants. Data availability statement The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Clinical trial number : not applicable. Funding statement No external funding was received for this study. Conflict of interest statement The authors declare no competing interests. References Topol EJ. High–performance medicine: the convergence of human and artificial intelligence. Nat Med. 2023;29(1):44–56. World Health Organization. Ethics and governance of artificial intelligence for health. Geneva: WHO; 2022. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–8. Gopalakrishnan S, Ganeshkumar P. Digital health in India: opportunities and challenges. J Fam Med Prim Care. 2022;11(4):1442–8. Choudhury A, Asan O. Trust in artificial intelligence in healthcare: a systematic review. JMIR Med Inf. 2023;11:e46985. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Acceptance of AI applications in healthcare: a systematic review. Int J Med Inf. 2022;165:104828. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence–driven healthcare. Camb Q Healthc Ethics. 2022;31(2):191–202. Li J, Dey A, Forlizzi J. Modeling acceptance of AI–based systems: a literature review. ACM Comput Surv. 2022;55(4):1–36. Dwivedi YK, Kshetri N, Hughes L, et al. So what if ChatGPT wrote it? Multidisciplinary perspectives on AI technologies. Int J Inf Manage. 2023;71:102642. Kaur S, Kumar R, Sengupta S. Public perception of artificial intelligence in healthcare in India. J Med Syst. 2023;47(2):15. Tables Table 1 Socio‑demographic characteristics of participants (n = 1,512) Characteristic Category n (%) Age group 18–29 502 (33.2) 30–44 580 (38.4) 45–59 348 (23.0) ≥ 60 82 (5.4) Sex Male 791 (52.3) Female 708 (46.8) Other 13 (0.9) Education ≤ 12th 372 (24.6) Graduate 684 (45.2) Postgraduate+ 456 (30.2) Smartphone ownership Yes 1446 (95.6) Prior telemedicine use Yes 833 (55.1) Table 2 Digital health exposure and AI familiarity Variable n (%) Frequent use of health apps 631 (41.7) Occasional use of health apps 487 (32.2) Familiar with AI in healthcare (moderate/high) 927 (61.3) Previously aware of AI screening tools 699 (46.2) Table 3 Acceptance and solution‑oriented preferences Item n (%) agreeing Willing to use AI screening 981 (64.9) Government certification required 1246 (82.4) Mandatory doctor confirmation 1143 (75.6) Regional language interface 1317 (87.1) Voice assistance for elderly 1213 (80.2) Deployment in government hospitals 1344 (88.9) Table 4 Barriers to adoption (single best response) Barrier n (%) Privacy concerns 460 (30.4) Fear of incorrect diagnosis 364 (24.1) Lack of trust in AI 283 (18.7) Low digital skills 221 (14.6) Cost 142 (9.4) Prefer doctor only 42 (2.8) Table 5 Multivariable logistic regression for predictors of acceptance Predictor aOR 95% CI p value Perceived usefulness (per unit) 2.36 2.05–2.72 < 0.001 Trust score 1.89 1.63–2.19 < 0.001 Ease of use score 1.31 1.15–1.49 < 0.001 Telemedicine use 1.54 1.26–1.88 < 0.001 Higher education 1.27 1.05–1.54 0.013 Privacy concern score 0.66 0.58–0.75 < 0.001 Diagnostic risk score 0.72 0.63–0.83 < 0.001 Age ≥ 60 years 0.69 0.52–0.92 0.011 Additional Declarations No competing interests reported. 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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-8692393","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595316448,"identity":"6da3d1c6-7616-40aa-8b7c-5a36c01ed1bd","order_by":0,"name":"Nishanth Muppa","email":"data:image/png;base64,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","orcid":"","institution":"Malla Reddy Vishwavidyapeeth","correspondingAuthor":true,"prefix":"","firstName":"Nishanth","middleName":"","lastName":"Muppa","suffix":""},{"id":595316449,"identity":"82fca078-1e89-46c9-9978-29917f0471a5","order_by":1,"name":"Sreeja Namilakonda","email":"","orcid":"","institution":"Malla Reddy Vishwavidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Sreeja","middleName":"","lastName":"Namilakonda","suffix":""},{"id":595316450,"identity":"cdc32c70-1e46-4d7e-8e31-1baf54fd1ba9","order_by":2,"name":"Sowmika Busireddy","email":"","orcid":"","institution":"Malla Reddy Vishwavidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Sowmika","middleName":"","lastName":"Busireddy","suffix":""},{"id":595316451,"identity":"90ecb8be-0840-49e3-9a1c-d8d591d27d90","order_by":3,"name":"Ojal Maddipati","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ojal","middleName":"","lastName":"Maddipati","suffix":""},{"id":595316452,"identity":"36319d60-7380-41bb-b4a7-1e6162ceef75","order_by":4,"name":"Srinivas Y","email":"","orcid":"","institution":"Malla Reddy Vishwavidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Srinivas","middleName":"","lastName":"Y","suffix":""}],"badges":[],"createdAt":"2026-01-25 12:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8692393/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8692393/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106254030,"identity":"b30e1ec1-d13f-48f7-b143-72a24478183e","added_by":"auto","created_at":"2026-04-06 18:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":820765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8692393/v1/a512213f-6b89-4500-b54b-8ab91b964b14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Behavioral Intention to Use Artificial Intelligence Enabled Health Screening among Urban Adults in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) has rapidly transitioned from experimental clinical applications to large‑scale deployment in diagnostic imaging, clinical decision support, and population‑level risk prediction (Topol 2023; Rajpurkar et al. 2022). AI‑enabled screening tools\u0026mdash;delivered through mobile applications, kiosks, or wearable devices\u0026mdash;are increasingly proposed as cost‑effective strategies to expand early detection of non‑communicable diseases (NCDs), optimize limited healthcare workforces, and reduce outpatient congestion in urban health systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndia faces a dual challenge of a rapidly rising NCD burden, accounting for more than 60% of total mortality, and persistent shortages of trained healthcare professionals. National initiatives such as the Ayushman Bharat Digital Mission explicitly promote algorithm‑driven risk stratification and telehealth‑enabled screening pathways to address these constraints (Gopalakrishnan and Ganeshkumar 2022). However, technological feasibility alone does not guarantee real‑world effectiveness. Public trust, perceived usefulness, usability, and confidence in data governance play decisive roles in determining whether individuals are willing to engage with algorithmic health services (Choudhury and Asan 2023).\u003c/p\u003e \u003cp\u003eEvidence from high‑income countries suggests that perceived usefulness, transparency, and institutional trust are central drivers of acceptance of AI in healthcare, whereas privacy concerns and fear of diagnostic error act as major deterrents (Alowais et al. 2022; Gerke et al. 2022). Extrapolation of these findings to LMIC settings remains limited, as digital literacy, regulatory maturity, and historical experiences with healthcare quality vary substantially.\u003c/p\u003e \u003cp\u003eUrban India represents a particularly important context: high smartphone penetration coexists with substantial socioeconomic inequality, fragmented healthcare delivery, and heterogeneous exposure to digital health services. Without population‑level evidence on public attitudes, large‑scale AI‑based screening initiatives risk poor uptake, inequitable utilization, and erosion of trust in health systems.\u003c/p\u003e \u003cp\u003eThis study therefore aimed to assess public acceptance of AI‑enabled health screening tools in a large urban Indian population and to identify modifiable socio‑demographic and perceptual determinants of behavioral intention to use such technologies.\u003c/p\u003e"},{"header":"Conceptual framework and hypotheses","content":"\u003cp\u003eThe study was guided by an extended Technology Acceptance Model (TAM), incorporating constructs of perceived usefulness, perceived ease of use, institutional trust, privacy concern, and perceived diagnostic risk (Li et al. 2022; Dwivedi et al. 2023).\u003c/p\u003e \u003cp\u003eWe hypothesized that:\u003c/p\u003e \u003cp\u003eH1. Higher perceived usefulness is positively associated with willingness to use AI‑based screening.\u003c/p\u003e \u003cp\u003eH2. Higher perceived ease of use is positively associated with willingness to use AI‑based screening.\u003c/p\u003e \u003cp\u003eH3. Greater trust in AI systems and healthcare institutions is positively associated with willingness to use AI‑based screening.\u003c/p\u003e \u003cp\u003eH4. Higher privacy concern is negatively associated with willingness to use AI‑based screening.\u003c/p\u003e \u003cp\u003eH5. Higher perceived diagnostic risk is negatively associated with willingness to use AI‑based screening.\u003c/p\u003e \u003cp\u003eH6. Prior exposure to digital health services (telemedicine or health‑app use) increases acceptance of AI‑based screening.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eA community‑based cross‑sectional study was conducted in Hyderabad, Telangana, India, between March and June 2026.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eAdults aged 18 years or older who had resided in Hyderabad for at least six months were eligible to participate.\u003c/p\u003e\n\u003ch3\u003eSample size estimation\u003c/h3\u003e\n\u003cp\u003eAssuming a conservative prevalence of acceptance of 50%, with 95% confidence level and 3% absolute precision, the minimum required sample size was calculated as 1,067. To improve statistical power for multivariable modeling and subgroup analyses and to account for incomplete responses, the target sample size was increased to approximately 1,500 participants.\u003c/p\u003e\n\u003ch3\u003eSampling technique\u003c/h3\u003e\n\u003cp\u003eMultisite convenience sampling was used, including recruitment from outpatient departments of tertiary hospitals, residential communities, public venues, and online survey dissemination. Recruitment was stratified by age group and sex to reduce sampling imbalance.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eInclusion criteria\u003c/strong\u003e \u003cp\u003eage\u0026thinsp;\u0026ge;\u0026thinsp;18 years; residence in Hyderabad for \u0026ge;\u0026thinsp;6 months; ability to provide informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion criteria\u003c/strong\u003e \u003cp\u003ecognitive impairment precluding consent; questionnaires with more than 20% missing responses.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection instrument\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire was developed using validated constructs from the Technology Acceptance Model and recent AI‑acceptance literature (Alowais et al. 2022; Choudhury and Asan 2023). The instrument comprised sections on socio‑demographic characteristics, digital‑health exposure, perceived usefulness, perceived ease of use, trust in AI and institutions, privacy concerns, perceived diagnostic risk, cost sensitivity, behavioral intention, and solution‑oriented policy and design preferences. All construct items were rated on five‑point Likert scales.\u003c/p\u003e\n\u003ch3\u003eOutcome variable\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was behavioral intention to use AI‑based health screening, dichotomized as willing (Likert score\u0026thinsp;\u0026ge;\u0026thinsp;4) or not willing/unsure (Likert score\u0026thinsp;\u0026le;\u0026thinsp;3).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were computed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) for continuous variables and as frequencies with percentages for categorical variables. Internal consistency of construct scales was assessed using Cronbach\u0026rsquo;s alpha. Bivariate analyses were conducted using chi‑square tests, independent‑samples t‑tests, or Mann\u0026ndash;Whitney U tests as appropriate. Multivariable logistic regression was performed to identify independent predictors of acceptance, adjusting for age, sex, education, income, smartphone ownership, and prior telemedicine use. Model diagnostics included variance inflation factors, Hosmer\u0026ndash;Lemeshow goodness‑of‑fit test, and receiver operating characteristic (ROC) curve analysis. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses were conducted using SPSS version 29.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBias control\u003c/h2\u003e \u003cp\u003eA standardized definition of AI‑based screening was provided to all participants. Multivariable adjustment was used to control for confounding, and incomplete questionnaires were excluded from analysis.\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eThe study protocol was reviewed and approved by the \u003cb\u003eInstitutional Ethics Committee of Malla Reddy Vishwavidyapeeth, Hyderabad, Telangana, India\u003c/b\u003e, in accordance with the ethical standards laid down in the Declaration of Helsinki and relevant national guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eOf 1,578 individuals approached, 66 questionnaires were excluded because of incomplete responses, resulting in a final analytical sample of 1,512 participants. The mean age was 36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4 years; 52.3% were male, and 95.6% owned a smartphone. More than half (55.1%) had previously used telemedicine services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDigital‑health exposure\u003c/h2\u003e \u003cp\u003eFrequent use of health‑related mobile applications was reported by 41.7% of participants, and 61.3% indicated moderate or high familiarity with AI in healthcare. Only 46.2% had heard of AI‑based screening tools before participating in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAcceptance of AI‑based screening\u003c/h2\u003e \u003cp\u003eOverall, 981 participants (64.9%) reported willingness to use AI‑based screening within the next six months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eConstruct reliability and scores\u003c/h2\u003e \u003cp\u003eAll construct scales demonstrated good internal consistency (Cronbach\u0026rsquo;s alpha range 0.78\u0026ndash;0.87). Mean perceived‑usefulness score was 3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69, trust score 3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81, and privacy‑concern score 3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSolution‑oriented preferences\u003c/h2\u003e \u003cp\u003eGovernment certification of AI screening systems was endorsed by 82.4% of respondents, and mandatory physician confirmation by 75.6%. A majority favored regional‑language interfaces (87.1%), voice assistance for elderly users (80.2%), and deployment in government hospitals (88.9%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBarriers and willingness‑to‑pay\u003c/h2\u003e \u003cp\u003eThe most frequently reported barriers were privacy concerns (30.4%), fear of incorrect diagnosis (24.1%), and lack of trust (18.7%). The median willingness‑to‑pay for AI screening was ₹50\u0026ndash;100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable predictors of acceptance\u003c/h2\u003e \u003cp\u003eIn adjusted analyses, perceived usefulness (aOR 2.36, 95% CI 2.05\u0026ndash;2.72), trust (aOR 1.89, 95% CI 1.63\u0026ndash;2.19), perceived ease of use (aOR 1.31, 95% CI 1.15\u0026ndash;1.49), prior telemedicine use (aOR 1.54, 95% CI 1.26\u0026ndash;1.88), and higher education (aOR 1.27, 95% CI 1.05\u0026ndash;1.54) were positively associated with acceptance. Privacy concern (aOR 0.66, 95% CI 0.58\u0026ndash;0.75), perceived diagnostic risk (aOR 0.72, 95% CI 0.63\u0026ndash;0.83), and age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (aOR 0.69, 95% CI 0.52\u0026ndash;0.92) were negatively associated with acceptance. The model demonstrated good calibration (Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.58) and discrimination (ROC‑AUC\u0026thinsp;=\u0026thinsp;0.82).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis large community‑based study provides robust evidence on public acceptance of AI‑enabled health screening tools in an urban Indian population. Approximately two‑thirds of respondents expressed willingness to use AI‑based screening, indicating moderate readiness for adoption but also highlighting substantial residual hesitancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeterminants of acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceived usefulness emerged as the strongest predictor of acceptance, reinforcing prior findings that tangible health‑system benefits\u0026mdash;such as early detection and reduced waiting times\u0026mdash;are central to public adoption of digital health technologies (Alowais et al. 2022). Trust in AI systems and healthcare institutions was another critical determinant, consistent with systematic reviews emphasizing the centrality of institutional credibility in shaping technology acceptance (Choudhury and Asan 2023).\u003c/p\u003e\n\u003cp\u003ePrivacy concerns and perceived diagnostic risk were powerful deterrents. These findings align with ethical analyses highlighting data governance and algorithmic transparency as core challenges in AI‑driven healthcare (Gerke et al. 2022; WHO 2022). Notably, older adults were less likely to accept AI‑based screening, suggesting that age‑specific design and education strategies may be required to prevent digital exclusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy and implementation implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants overwhelmingly supported government certification, clinical oversight, and hospital‑based deployment as prerequisites for trust. This suggests that purely commercial, app‑based screening models may face limited uptake unless embedded within formal healthcare systems and regulatory frameworks. Strong support for regional‑language interfaces and voice assistance further underscores the importance of inclusive design to reduce digital inequities.\u003c/p\u003e\n\u003cp\u003eThe observed median willingness‑to‑pay of ₹50\u0026ndash;100 indicates substantial price sensitivity, implying that large‑scale implementation may require public financing or cross‑subsidization to ensure equitable access. Collectively, these findings support a hybrid model in which AI‑based screening tools are regulated by public authorities, clinically supervised, and integrated into existing healthcare infrastructure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with previous studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcceptance levels in this study are comparable to those reported in other LMIC contexts but remain lower than those documented in several high‑income countries, where digital trust and regulatory maturity are greater (Kaur et al. 2023; Alowais et al. 2022). The relative importance of trust and privacy mirrors global patterns, suggesting that these constructs represent universal determinants of AI adoption in healthcare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStrengths of this study include the large sample size, theory‑driven design, incorporation of solution‑oriented policy questions, and rigorous multivariable analysis. Limitations include the use of convenience sampling, which may limit generalizability, and reliance on self‑reported intention rather than observed behavior. Longitudinal studies are needed to assess how stated intentions translate into actual utilization.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePublic acceptance of AI‑enabled health screening in urban India is moderate and is strongly influenced by perceived usefulness, institutional trust, and concerns regarding privacy and diagnostic accuracy. Regulatory certification, clinical integration, transparent communication, and inclusive design are essential for ethical and effective deployment. Policymakers should view public trust as a critical infrastructure component of digital‑health strategies, alongside technological development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Ethics Committee of Malla Reddy Vishwavidyapeeth, Hyderabad, Telangana, India, and was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten or digital informed consent was obtained from all participants prior to participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for participation and publication of anonymized data was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTopol EJ. High\u0026ndash;performance medicine: the convergence of human and artificial intelligence. Nat Med. 2023;29(1):44\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Ethics and governance of artificial intelligence for health. Geneva: WHO; 2022.\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\u003eGopalakrishnan S, Ganeshkumar P. Digital health in India: opportunities and challenges. J Fam Med Prim Care. 2022;11(4):1442\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhury A, Asan O. Trust in artificial intelligence in healthcare: a systematic review. JMIR Med Inf. 2023;11:e46985.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlowais SA, Alghamdi SS, Alsuhebany N, et al. Acceptance of AI applications in healthcare: a systematic review. Int J Med Inf. 2022;165:104828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence\u0026ndash;driven healthcare. Camb Q Healthc Ethics. 2022;31(2):191\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Dey A, Forlizzi J. Modeling acceptance of AI\u0026ndash;based systems: a literature review. ACM Comput Surv. 2022;55(4):1\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwivedi YK, Kshetri N, Hughes L, et al. So what if ChatGPT wrote it? Multidisciplinary perspectives on AI technologies. Int J Inf Manage. 2023;71:102642.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur S, Kumar R, Sengupta S. Public perception of artificial intelligence in healthcare in India. J Med Syst. 2023;47(2):15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio‑demographic characteristics of participants (n\u0026thinsp;=\u0026thinsp;1,512)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e502 (33.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e580 (38.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e348 (23.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e791 (52.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e708 (46.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e372 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e684 (45.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e456 (30.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmartphone ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1446 (95.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior telemedicine use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e833 (55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital health exposure and AI familiarity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequent use of health apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e631 (41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional use of health apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e487 (32.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamiliar with AI in healthcare (moderate/high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e927 (61.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreviously aware of AI screening tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e699 (46.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcceptance and solution‑oriented preferences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%) agreeing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilling to use AI screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e981 (64.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment certification required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1246 (82.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandatory doctor confirmation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1143 (75.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional language interface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1317 (87.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoice assistance for elderly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1213 (80.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeployment in government hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1344 (88.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBarriers to adoption (single best response)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarrier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivacy concerns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e460 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of incorrect diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364 (24.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of trust in AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e283 (18.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow digital skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e221 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer doctor only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable logistic regression for predictors of acceptance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep 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\u003ePerceived usefulness (per unit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u0026ndash;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"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\u003eTrust score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u0026ndash;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"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\u003eEase of use score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u0026ndash;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"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\u003eTelemedicine use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u0026ndash;1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"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\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u0026ndash;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivacy concern score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u0026ndash;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"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\u003eDiagnostic risk score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u0026ndash;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"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\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u0026ndash;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"artificial intelligence, digital health, population screening, technology acceptance, health policy, India","lastPublishedDoi":"10.21203/rs.3.rs-8692393/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8692393/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eArtificial intelligence (AI)\u0026ndash;enabled health screening tools are increasingly promoted as scalable solutions for early detection of non‑communicable diseases and for reducing congestion in overstretched health systems. However, limited population‑level evidence exists on public acceptance of such technologies in low‑ and middle‑income countries (LMICs), where concerns regarding trust, privacy, and regulatory oversight may hinder adoption.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo quantify public acceptance of AI‑based health screening tools and to identify socio‑demographic, technological, and perceptual determinants of behavioral intention to use such tools among adults in Hyderabad, India.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA community‑based cross‑sectional study was conducted among 1,512 adults using a structured questionnaire grounded in the Technology Acceptance Model and an extended trust\u0026ndash;risk framework. The primary outcome was behavioral intention to use AI‑based screening. Multivariable logistic regression was used to identify independent predictors after adjusting for demographic and digital‑access confounders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 981 participants (64.9%) expressed willingness to use AI‑based screening. Perceived usefulness (adjusted odds ratio [aOR] 2.36, 95% CI 2.05\u0026ndash;2.72) and trust in AI and healthcare institutions (aOR 1.89, 95% CI 1.63\u0026ndash;2.19) were strong positive predictors, whereas privacy concerns (aOR 0.66, 95% CI 0.58\u0026ndash;0.75) and perceived diagnostic risk (aOR 0.72, 95% CI 0.63\u0026ndash;0.83) were independently associated with lower acceptance. Government certification (82.4%) and mandatory physician confirmation (75.6%) were the most frequently endorsed trust‑building interventions. The median willingness‑to‑pay for AI screening was ₹50\u0026ndash;100.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePublic acceptance of AI‑enabled screening in urban India is moderate and is strongly shaped by trust, perceived utility, and data‑protection concerns. Regulatory oversight, clinical integration, and user‑centered design are essential prerequisites for ethical and effective deployment of AI‑based population screening programs in LMIC settings.\u003c/p\u003e","manuscriptTitle":"Behavioral Intention to Use Artificial Intelligence Enabled Health Screening among Urban Adults in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 09:17:06","doi":"10.21203/rs.3.rs-8692393/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":"7d54932a-4ae9-4edc-9804-75c17ceaa402","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T18:09:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 09:17:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8692393","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8692393","identity":"rs-8692393","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}