Abstract
Background
This study investigated self-reported awareness, demand, and willingness to pay for artificial intelligence (AI)-supported healthcare services among Vietnamese adults.
Methods
A cross-sectional online survey conducted across multiple regions of Vietnam was carried out from April to May 2025 using convenience and snowball sampling through social media, institutional mailing lists, and professional networks. A total of 3,550 valid responses were analyzed. Data on sociodemographic characteristics, chronic health conditions, health literacy, eHealth literacy, and perceptions of AI-supported healthcare services were collected using a structured questionnaire. Multivariable ordered logistic regression, binary logistic regression, and Tobit regression models were applied to identify factors associated with AI awareness, demand, and willingness to pay.
Results
Among participants, 49.1% reported being somewhat aware of AI applications in healthcare, while 58.6% were willing or strongly willing to use AI-supported healthcare services. Emergency medical support (80.6%) and telehealth (55.0%) were the most preferred AI applications. However, 65.6% of respondents were unwilling to pay additional costs for AI-enhanced healthcare services. Among those willing to pay, the most commonly accepted additional cost ranged from 10% to 19%. Higher income, larger household size, and greater health literacy and eHealth literacy were associated with higher levels of AI awareness and demand. Cancer and respiratory disease were associated with higher reported awareness and demand for AI-supported healthcare, while cardiovascular disease and musculoskeletal disorders were associated with greater willingness to pay additional costs. AI awareness was also positively associated with AI demand.
Conclusion
Participants in this survey demonstrated heterogeneous levels of AI awareness and relatively high self-reported willingness to use selected AI-assisted healthcare services, particularly for emergency support and telehealth. However, willingness to pay additional costs remained limited. These findings suggest potential public interest in AI-supported healthcare while highlighting the importance of affordability, health literacy, and equitable access in future implementation efforts.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12913-026-15244-0.
Keywords: Artificial intelligence, Healthcare, Public perception, eHealth literacy, Willingness to pay, Digital health
Introduction
Artificial Intelligence (AI) is increasingly being integrated into healthcare systems across the globe, revolutionizing how care is delivered, optimized, and accessed. Applications of AI span diagnostic imaging, predictive analytics, drug discovery, personalized medicine, and clinical decision support systems [1, 2]. The global AI healthcare market has seen exponential growth, with North America holding nearly 45% of the market share, followed closely by Europe and Asia-Pacific regions, notably China, South Korea, and Japan [2, 3]. Studies have shown that AI can improve diagnostic accuracy, reduce human error, increase operational efficiency, and cut healthcare costs, positioning it as a strategic solution to meet rising global healthcare demands [4, 5]. However, the uptake of AI depends not only on technological capabilities but also on public acceptance, understanding, and readiness to engage with AI-driven services.
Public awareness, demand, and willingness to pay (WTP) for AI in healthcare have become central to successful digital health transformation. Existing evidence suggests that public attitudes toward AI-assisted healthcare are generally favorable, although acceptance remains conditional and context-dependent. Systematic reviews have reported that many individuals perceive AI as having the potential to improve healthcare efficiency, accuracy, and accessibility while simultaneously expressing concerns regarding privacy, transparency, accountability, and the preservation of human involvement in healthcare decision-making [6–8]. Despite growing interest in AI-assisted healthcare, awareness of specific healthcare applications remains limited in many populations. A prior survey in the United States found that approximately half of respondents reported little or no awareness of AI applications in supporting disease diagnosis, while only 18.9% reported substantial awareness of such applications [9]. Similarly, attitudes toward AI-assisted healthcare have generally been positive but cautious across countries. In a multinational survey involving 13,806 patients, 57.6% reported positive views toward AI in healthcare, yet most respondents preferred physician-led decision-making (72.9%) and explainable AI systems (70.2%), highlighting the continuing importance of human oversight in healthcare delivery [10]. These findings suggest that acceptance of AI in healthcare extends beyond technological performance and is closely linked to trust, transparency, and the role of healthcare professionals in decision-making [11–13].
Demand for AI-supported healthcare services has also been reported in several settings, although willingness to pay appears more variable. A large national survey in China found that approximately 99% of respondents accepted the use of medical AI and nearly 80% expressed demand for AI-assisted healthcare services, suggesting substantial public interest in AI-supported care [14]. However, evidence from a representative survey in Hungary indicated that approximately two-thirds of respondents reported willingness to pay for AI-assisted diagnostic services or robot-assisted surgery, and higher income was associated with greater willingness to pay for technology-supported healthcare options [15]. Collectively, these findings suggest that awareness, demand, and willingness to pay represent distinct dimensions of public acceptance that may not necessarily align with one another. Nevertheless, important knowledge gaps remain. Most available evidence originates from high-income countries or a limited number of upper-middle-income settings, whereas relatively little is known about public awareness, demand, and willingness to pay for AI-supported healthcare services in low- and middle-income countries, particularly in Southeast Asia. Evidence from Vietnam remains especially scarce despite increasing national investment in artificial intelligence and digital health infrastructure. Understanding public perceptions in this context is important because healthcare systems, affordability, digital readiness, and cultural expectations may shape acceptance, demand, and willingness to pay for AI-assisted healthcare differently from those observed elsewhere.
Vietnam has increasingly embraced the integration of artificial intelligence in healthcare, propelled by a national vision to modernize the health system and address systemic challenges. Under Decision No. 127/QĐ-TTg, the Vietnamese government established a strategic roadmap for AI development through 2030, highlighting healthcare as a key application domain. Practical adoption has followed suit, with major hospitals and private healthcare entities implementing AI-driven technologies across diverse clinical areas. AI is currently being used to enhance diagnostic accuracy in radiology, streamline patient triage, support treatment planning, and monitor public health trends [16, 17]. For example, hospitals have piloted AI systems to analyze X-rays and CT scans, support prediction of disease risk, and optimize workflows for chronic disease management. However, several structural barriers still impede widespread implementation. These include disparities in digital infrastructure—particularly in rural areas—insufficient data security mechanisms, and an underdeveloped regulatory framework for evaluating and approving AI medical technologies [17, 18]. Ethical concerns also remain under-addressed, such as the potential for algorithmic bias, informed consent in AI-assisted diagnosis, and the risk of deskilling the clinical workforce [16]. While Vietnam’s AI healthcare ecosystem is expanding, the pace and equity of its adoption hinge on overcoming these regulatory, infrastructural, and ethical constraints.
In light of global digital health trends and Vietnam’s strategic orientation toward AI development, generating context-specific evidence on public attitudes and expectations is both timely and necessary. Understanding how Vietnamese people perceive AI, what factors shape their demand, and whether they are willing to pay extra for AI-supported services will provide actionable insights for policymakers, healthcare providers, and digital health designers. Therefore, this study aims to (1) assess the level of awareness, demand, and willingness to pay for AI-supported healthcare services among the general Vietnamese population, and (2) examine how these outcomes relate to sociodemographic characteristics, health conditions, and levels of health and digital literacy.
Materials and methods
Study design and participants
This study was a cross-sectional online survey conducted across multiple regions of Vietnam from April to May 2025 to assess self-reported awareness, demand, and willingness to pay (WTP) for AI-supported healthcare services. These outcomes were selected to reflect complementary dimensions of AI acceptance, including familiarity with AI applications (awareness), stated intention to use AI-supported services (demand), and hypothetical economic acceptance (WTP). The study evaluated perceived acceptance and stated intention rather than actual adoption or real-world utilization of AI technologies.
Participants were recruited using convenience and snowball sampling methods through digital platforms, including social media (Facebook, Zalo), institutional mailing lists, and professional networks. A total of 3,720 individuals accessed the survey, and 3,550 valid responses were included after excluding incomplete or duplicate questionnaires. Eligible participants were Vietnamese adults aged ≥ 18 years who were able to read and understand Vietnamese and resided in Northern, Central, or Southern Vietnam. Exclusion criteria included incomplete questionnaires, missing responses for key outcome variables, and suspected duplicate submissions identified during data cleaning.
The study population represented a digitally connected sample recruited primarily through online and institutional communication channels. Occupation was included as a covariate in multivariable analyses because occupational status may influence exposure to digital technologies, access to healthcare services, economic resources, and perceptions of AI-supported healthcare [11, 19, 20], thereby potentially confounding associations between health literacy, eHealth literacy, and study outcomes.
Data collection procedure
Data were collected using an anonymous structured questionnaire administered through Google Forms. The survey link was distributed through social media, healthcare institutional mailing lists, and community or professional groups. Participants were encouraged to share the survey with peers and colleagues to increase population coverage. Electronic informed consent was obtained before participation, and the survey required approximately 10–15 min to complete.
The questionnaire was developed in Vietnamese based on literature review and expert discussion within the research team. The AI-related items were designed as single-item measures to assess distinct constructs, including self-reported familiarity with AI in healthcare (awareness), overall willingness to use AI-supported healthcare services (demand), and hypothetical willingness to pay additional costs for AI-enhanced healthcare services. The questionnaire was pilot-tested in 30 participants to evaluate clarity, relevance, and technical functionality, and revisions were made accordingly. Content validity was established through literature review, expert review, and pilot testing. Because the AI-related outcomes were measured using single-item questions rather than multi-item scales, internal consistency reliability was not applicable. For validated instruments originally developed in English, forward translation and expert review were conducted to ensure semantic equivalence and contextual appropriateness.
To minimize missing or duplicate responses, mandatory response settings and submission restrictions were applied within Google Forms. Duplicate responses were additionally screened using submission timestamps and response patterns before analysis. The complete questionnaire, including the exact wording of the AI-related questions, HLS19-Q12, and eHEALS instruments, is provided in Supplementary File S1.
Variables
Sociodemographic and health characteristics
Collected variables included age, gender, education level, occupation, household income, household size, and geographic region. Participants also self-reported chronic health conditions, including diabetes, cardiovascular disease, cancer, respiratory disease, musculoskeletal disorders, metabolic disorders, endocrine disorders, and other chronic conditions. Chronic diseases were coded as binary variables (yes/no).
Health literacy and eHealth literacy
General health literacy was assessed using the 12-item European Health Literacy Survey Questionnaire (HLS19-Q12), a validated instrument widely used in public health research [21, 22]. The scale evaluates individuals’ perceived ability to access, understand, appraise, and apply health information using a 4-point Likert scale ranging from “very difficult” to “very easy.” Scores were calculated using the Type P scoring method, generating values from 0 to 100 and categorized as inadequate (≤ 50), problematic (> 50 to ≤ 66.67), sufficient (> 66.67 to ≤ 83.33), or excellent (> 83.33). Digital health literacy was assessed using the 8-item eHealth Literacy Scale (eHEALS), which measures perceived ability to seek, evaluate, and apply electronic health information [23, 24]. Each item was scored on a 5-point Likert scale, producing total scores ranging from 8 to 40, categorized as inadequate (8–20), problematic (21–26), or sufficient (27–40).
AI-related outcomes
AI awareness was assessed using a four-level item (“never heard,” “heard but not clear,” “somewhat aware,” and “well-informed”). This variable reflected self-reported familiarity or exposure to AI in healthcare rather than objective knowledge or technical understanding.
Overall demand for AI-supported healthcare was assessed using a five-point willingness scale ranging from “strongly unwilling” to “strongly willing.” This measure represented participants’ general willingness to use AI-supported healthcare services rather than acceptance of any specific AI application. Participants were also asked separately which healthcare services they considered appropriate for AI support, including telemedicine, home-based elderly care, wearable monitoring, personalized health advice, diagnostic imaging, clinical decision support, and automated reminders. Responses to these service-specific items were analyzed descriptively only.
Willingness to pay was assessed using a two-step approach. Participants were first asked whether they would be willing to pay additional costs if the same healthcare service was enhanced with AI-supported functions while maintaining the same standard of medical care (“not willing to pay extra” vs. “willing to pay extra”). Those who indicated willingness to pay were subsequently asked to report the maximum additional percentage they would be willing to pay compared with the cost of the same healthcare service without AI support using an open-ended numeric response. This measure represented hypothetical economic acceptance rather than actual payment behavior.
Statistical analysis
Data were analyzed using STATA version 17 (StataCorp, College Station, TX, USA). Descriptive statistics were used to summarize participant characteristics and survey responses. Analyses were conducted using a complete-case approach, and questionnaires with missing data in key outcome variables were excluded without imputation.
Internal consistency reliability was assessed using Cronbach’s alpha, with α ≥ 0.70 considered acceptable. Factor analysis using principal component analysis with varimax rotation was performed to evaluate construct validity. Reliability and construct validity results for the HLS19-Q12 and eHEALS are presented in Supplementary Table S1 and Table S2.
Multivariable ordered logistic regression models were used to identify factors associated with AI awareness and AI demand. The proportional odds assumption was assessed using the Brant test and was not violated for either model (all p-values > 0.05). Binary logistic regression was applied to evaluate factors associated with willingness to pay additional costs for AI-supported healthcare services, while Tobit regression was used to assess factors associated with the additional percentage participants were willing to pay because the outcome was left- and right-censored between 0% and 100%.
Candidate covariates were selected a priori based on theoretical relevance and previous literature and included age, gender, education, occupation, geographic region, household size, monthly income, chronic health conditions, health literacy, and eHealth literacy. AI awareness was additionally included in the demand model, whereas AI demand was included in the willingness-to-pay models. Forward stepwise selection (retention threshold p < 0.20) was subsequently applied to derive parsimonious models while preserving theoretically relevant covariates. Full multivariable models including all candidate covariates are presented in Supplementary Tables S3–S6.
Variance inflation factors ranged from 1.01 to 1.41, with a mean VIF of 1.12, well below commonly accepted thresholds. Model fit was evaluated using likelihood ratio statistics and pseudo-R² values for all regression models. Results were reported as odds ratios (ORs), regression coefficients (β), and 95% confidence intervals (CIs). Statistical significance was defined as p < 0.05.
Ethical consideration
The study protocol was reviewed and approved by the Institutional Review Board of the Thang Long Institute of Medicine and Pharmacy (Code: 03.25/GCN-HĐĐĐNCYSH-IMP). All participants provided electronic informed consent prior to participation. Data were collected anonymously and stored securely, and participants were informed of their right to withdraw at any time without penalty.
Results
Table 1 presents the sociodemographic and health-related characteristics of the study participants. The table shows that males accounted for 71.2% of the sample, and most respondents had vocational, college, or university education (78.8%). The majority were employed (92.7%) and resided in Central Vietnam (45.2%). Nearly half of participants reported a monthly household income of 5–<10 million VND (43.8%), while 56.4% were early middle-aged adults aged 30–44 years. The mean age was 39.58 ± 9.64 years, mean monthly income was 10.39 ± 5.85 million VND, mean health literacy score was 65.92 ± 13.65, and mean eHealth literacy score was 30.63 ± 5.32. Cardiovascular disease (4.7%) and diabetes (3.6%) were the most commonly reported chronic conditions.
Table 1.
Sociodemographic characteristics of the study participants
| Factor | Category | Frequency (n) | Percent (%) |
|---|---|---|---|
| Gender | Male | 2,527 | 71.2 |
| Female | 1,023 | 28.8 | |
| Education | Secondary or below | 54 | 1.5 |
| High school | 251 | 7.1 | |
| Vocational/College/University | 2,796 | 78.8 | |
| Postgraduate | 449 | 12.7 | |
| Occupation | Employed | 3,289 | 92.7 |
| Unemployed | 42 | 1.2 | |
| Student | 139 | 3.9 | |
| Retired | 80 | 2.3 | |
| Region | Northern Vietnam | 1,131 | 31.9 |
| Central Vietnam | 1,605 | 45.2 | |
| Southern Vietnam | 814 | 22.9 | |
| Income group | < 5 million VND/month | 221 | 6.2 |
| 5–<10 million VND/month | 1,553 | 43.8 | |
| 10–<15 million VND/month | 1,220 | 34.4 | |
| ≥ 15 million VND/month | 556 | 15.7 | |
| Age group | Young adults (18–29 years) | 496 | 14.0 |
| Early middle-aged adults (30–44 years) | 2,001 | 56.4 | |
| Late middle-aged adults (45–59 years) | 973 | 27.4 | |
| Older adults (≥ 60 years) | 80 | 2.3 | |
| Morbidities | Diabetes | 128 | 3.6 |
| Hypertension | 15 | 0.4 | |
| Cardiovascular disease | 168 | 4.7 | |
| Cancer | 98 | 2.8 | |
| Respiratory disease | 70 | 2.0 | |
| Musculoskeletal disorders | 81 | 2.3 | |
| ENT disorders | 33 | 0.9 | |
| Digestive disorders | 65 | 1.8 | |
| Neurological disorders | 13 | 0.4 | |
| Metabolic disorders | 37 | 1.0 | |
| Endocrine disorders (other than diabetes) | 42 | 1.2 | |
| Other chronic conditions | 30 | 0.8 | |
| Mean ± SD | |||
| Age (years) | 39.58 ± 9.64 | ||
| Household size (persons) | 4.44 ± 1.49 | ||
| Monthly income (million VND) | 10.39 ± 5.85 | ||
| Health Literacy | 65.92 ± 13.65 | ||
| eHealth Literacy | 30.63 ± 5.32 | ||
Table 2 presents participants’ awareness, willingness to use, and willingness to pay for AI-supported healthcare services. Among respondents, 49.1% reported being somewhat aware of AI applications in healthcare, whereas only 9.2% considered themselves well-informed. Regarding willingness to use AI-supported healthcare services, 58.6% reported being willing or strongly willing to use such services. Emergency medical AI support was the most preferred AI application (80.6%), followed by telehealth services (55.0%) and elderly home care support (50.3%). Most participants (65.6%) indicated that they were unwilling to pay additional costs for AI-enhanced healthcare services. Among those willing to pay, the most frequently reported additional cost ranged from 10% to 19% (12.0% of the total sample). The mean additional percentage participants were willing to pay was 5.71% across the full sample, including respondents unwilling to pay additional costs who were assigned a value of 0%. Among respondents willing to pay additional costs, the mean maximum additional amount was 16.56% ± 17.96%.
Table 2.
Public awareness, willingness, and acceptance of AI applications in healthcare
| Group | Item / Category | Frequency (n) | Percent (%) |
|---|---|---|---|
| AI Awareness | Never heard | 354 | 10.1 |
| Heard but not clear | 1,105 | 31.6 | |
| Somewhat aware | 1,715 | 49.1 | |
| Well-informed | 322 | 9.2 | |
| Willingness to Use AI | Strongly unwilling | 371 | 10.6 |
| Unwilling | 206 | 5.9 | |
| No opinion | 870 | 24.9 | |
| Willing | 1,543 | 44.1 | |
| Strongly willing | 506 | 14.5 | |
| Preferred AI Application in Healthcare | Telehealth | 1,951 | 55.0 |
| Elderly care at home | 1,787 | 50.3 | |
| Health alerts via wearables | 1,489 | 41.9 | |
| Personalized health advice via AI app | 1,714 | 48.3 | |
| Emergency medical AI support | 2,860 | 80.6 | |
| Chronic disease risk prediction | 1,224 | 34.5 | |
| AI image diagnosis support | 1,090 | 30.7 | |
| AI for scheduling and medical records | 1,223 | 34.4 | |
| AI warnings for inpatient complications | 1,061 | 29.9 | |
| AI reminders and treatment suggestions | 1,288 | 36.3 | |
| AI-supported clinical decision systems | 901 | 25.4 | |
| Willingness to Pay for AI Add-on Service | Not willing to pay extra cost | 2245 | 65.6 |
| < 5% extra cost | 151 | 4.4 | |
| 5–9% extra cost | 206 | 6.0 | |
| 10–19% extra cost | 411 | 12.0 | |
| 20–49% extra cost | 297 | 8.7 | |
| 50–100% extra cost | 115 | 3.4 | |
| Mean ± SD (%), overall sample† | 5.71 ± 13.15 | ||
| Mean ± SD (%), among respondents willing to pay extra cost‡ | 16.56 ± 17.96 | ||
† Calculated using original open-ended percentage responses; respondents unwilling to pay additional costs were assigned a value of 0%; ‡ Calculated only among respondents who reported willingness to pay additional costs (n = 1,180)
Table 3 presents the multivariable ordered logistic regression analysis of factors associated with AI awareness in healthcare. The table shows that higher monthly income (OR = 1.12, 95% CI: 1.10–1.14, p < 0.001), larger household size (OR = 1.05, 95% CI: 1.00–1.10, p = 0.030), female gender (OR = 1.28, 95% CI: 1.10–1.49, p = 0.001), higher health literacy (OR = 1.01, 95% CI: 1.01–1.02, p < 0.001), and higher eHealth literacy (OR = 1.04, 95% CI: 1.03–1.06, p < 0.001) were significantly associated with higher levels of AI awareness. Participants with cancer (OR = 1.91, 95% CI: 1.21–3.03, p = 0.005) and those with respiratory disease (OR = 1.72, 95% CI: 1.01–2.93, p = 0.047) demonstrated significantly higher AI awareness, whereas participants with diabetes (OR = 0.68, 95% CI: 0.46–1.00, p = 0.049) showed lower awareness levels. Increasing age (OR = 0.99, 95% CI: 0.98–0.99, p = 0.001) was also associated with lower awareness levels.
Table 3.
Multivariate ordered logistic regression to identify factors associated with AI awareness and demand in healthcare
| Factor | Categories | Levels of AI Awareness | Levels of AI Demand |
|---|---|---|---|
| OR (95% CI) | OR (95% CI) | ||
| Socioeconomic characteristics | Monthly income (per 1 million VND increase) | 1.12 (1.10; 1.14)* | 1.05 (1.04; 1.06)* |
| Household size (per 1-person increase) | 1.05 (1.00; 1.10)* | 1.09 (1.04; 1.14)* | |
| Female gender (vs. Male) | 1.28 (1.10; 1.49)* | – | |
| Age (per 1-year increase) | 0.99 (0.98; 0.99)* | – | |
| Education level | Secondary or below | Reference | Reference |
| High school | 1.44 (0.75; 2.78) | 0.40 (0.22; 0.74)* | |
| Vocational/College/University | 0.58 (0.32; 1.04) | 0.31 (0.18; 0.55)* | |
| Postgraduate | 0.57 (0.31; 1.05) | 0.25 (0.14; 0.45)* | |
| Occupation | Employed | Reference | – |
| Unemployed/Student | 2.08 (1.50; 2.89)* | – | |
| Retired | 1.54 (0.90; 2.63) | – | |
| Geographic region | Northern Vietnam | Reference | Reference |
| Central Vietnam | 0.99 (0.84; 1.15) | 0.83 (0.71; 0.96)* | |
| Southern Vietnam | 1.24 (1.03; 1.49)* | 0.78 (0.66; 0.93)* | |
| Health literacy and eHealth literacy | Health literacy score (per 1-point increase) | 1.01 (1.01; 1.02)* | 1.01 (1.00; 1.01)* |
| eHealth literacy (per 1-point increase) | 1.04 (1.03; 1.06)* | 1.05 (1.04; 1.07)* | |
| Chronic health conditions | Cardiovascular disease (vs. No) | – | 0.49 (0.37; 0.66)* |
| Cancer (vs. No) | 1.91 (1.21; 3.03)* | 2.31 (1.46; 3.65)* | |
| Respiratory disease (vs. No) | 1.72 (1.01; 2.93)* | 1.91 (1.14; 3.19)* | |
| Diabetes (vs. No) | 0.68 (0.46; 1.00)* | 0.73 (0.51; 1.03) | |
| Metabolic disorders (vs. No) | – | 0.40 (0.23; 0.69)* | |
| Endocrine disorders (vs. No) | – | 1.79 (1.02; 3.14)* | |
| Digestive disorders (vs. No) | – | 1.35 (0.86; 2.14) | |
| Hypertension (vs. No) | – | 0.44 (0.13; 1.47) | |
| Other chronic conditions (vs. No) | 0.57 (0.30; 1.09) | – | |
| AI awareness level | Per 1-level increase | – | 2.41 (2.20; 2.64)* |
Abbreviations: OR, odds ratio; CI, confidence interval
Note: Results were obtained from multivariable ordered logistic regression models. “–” indicates variables not retained in the final model; * Statistically significant at p < 0.05
Table 3 also depicts the multivariable ordered logistic regression analysis of factors associated with AI demand in healthcare. The table reveals that higher monthly income (OR = 1.05, 95% CI: 1.04–1.06, p < 0.001), larger household size (OR = 1.09, 95% CI: 1.04–1.14, p < 0.001), higher health literacy (OR = 1.01, 95% CI: 1.00–1.01, p = 0.017), higher eHealth literacy (OR = 1.05, 95% CI: 1.04–1.07, p < 0.001), and higher AI awareness levels (OR = 2.41, 95% CI: 2.20–2.64, p < 0.001) were significantly associated with greater AI demand. Participants with cancer (OR = 2.31, 95% CI: 1.46–3.65, p < 0.001), those with respiratory disease (OR = 1.91, 95% CI: 1.14–3.19, p = 0.014), and those with endocrine disorders (OR = 1.79, 95% CI: 1.02–3.14, p = 0.043) demonstrated significantly higher AI demand. In contrast, participants with cardiovascular disease and metabolic disorders showed significantly lower AI demand.
Table 4 presents the multivariable logistic regression analysis of factors associated with willingness to pay additional costs for AI-supported healthcare services. The table shows that higher AI demand levels (OR = 1.52, 95% CI: 1.41–1.64, p < 0.001), female gender (OR = 1.25, 95% CI: 1.06–1.48, p = 0.008), higher monthly income (OR = 1.05, 95% CI: 1.03–1.06, p < 0.001), unemployment/student status (OR = 2.06, 95% CI: 1.45–2.92, p < 0.001), and retirement status (OR = 2.92, 95% CI: 1.61–5.29, p < 0.001) were significantly associated with willingness to pay additional costs for AI-supported healthcare services. Participants with cardiovascular disease (OR = 2.07, 95% CI: 1.44–2.98, p < 0.001) and those with musculoskeletal disorders (OR = 2.33, 95% CI: 1.46–3.73, p < 0.001) were also more likely to report willingness to pay additional costs. Conversely, participants with vocational, college, or university education (OR = 0.44, 95% CI: 0.24–0.81, p = 0.009) and those with ENT disorders (OR = 0.12, 95% CI: 0.03–0.51, p = 0.004) were significantly less likely to pay additional costs.
Table 4.
Multivariate logistic and tobit regression to identify factors associated with willingness to use and willingness to pay for AI in healthcare
| Factor | Categories | Willingness to Pay Additional Cost | Additional Percentage Willing to Pay |
|---|---|---|---|
| OR (95% CI) | β (95% CI) | ||
| Socioeconomic characteristics | Female gender (vs. Male) | 1.25 (1.06; 1.48)* | 3.21 (0.61; 5.81)* |
| Monthly income (per 1 million VND increase) | 1.05 (1.03; 1.06)* | 0.27 (0.07; 0.46)* | |
| Household size (per 1-person increase) | 1.04 (0.99; 1.10) | – | |
| Education level | Secondary or below | Reference | Reference |
| High school | 0.87 (0.44; 1.72) | 4.27 (− 5.04; 13.57) | |
| Vocational/College/University | 0.44 (0.24; 0.81)* | −7.33 (− 15.78; 1.12) | |
| Postgraduate | 0.54 (0.28; 1.04) | −4.19 (− 13.03; 4.64) | |
| Occupation | Employed | Reference | Reference |
| Unemployed/Student | 2.06 (1.45; 2.92)* | 6.25 (1.09; 11.42)* | |
| Retired | 2.92 (1.61; 5.29)* | 9.24 (1.95; 16.53)* | |
| Geographic region | Northern Vietnam | – | Reference |
| Central Vietnam | – | 1.81 (− 1.02; 4.65) | |
| Southern Vietnam | – | 3.12 (− 0.18; 6.42) | |
| Chronic health conditions | Cardiovascular disease (vs. No) | 2.07 (1.44; 2.98)* | 9.30 (4.00; 14.60)* |
| Musculoskeletal disorders (vs. No) | 2.33 (1.46; 3.73)* | 11.17 (4.18; 18.16)* | |
| ENT disorders (vs. No) | 0.12 (0.03; 0.51)* | −33.45 (− 53.48; −13.42)* | |
| Cancer (vs. No) | 1.64 (0.97; 2.76) | – | |
| Metabolic disorders (vs. No) | 0.49 (0.22; 1.10) | – | |
| Diabetes (vs. No) | 1.41 (0.89; 2.24) | – | |
| AI demand level | Per 1-level increase | 1.52 (1.41; 1.64)* | 5.06 (3.96; 6.16)* |
| Model performance | LR χ² (p-value) | 429.54 (< 0.001) | 249.59 (< 0.001) |
| Log likelihood | −1990.90 | −6653.10 | |
| Pseudo R² | 0.1974 | 0.1184 | |
| Hosmer–Lemeshow χ² (p-value) | 18.45 (0.118) | – | |
| Area under ROC curve (AUC) | 0.686 | – | |
| AIC | 4021.80 | 13344.71 | |
| BIC | 4113.88 | 13436.79 |
* Statistically significant at p < 0.05
Abbreviations: OR, odds ratio; β, regression coefficient; CI, confidence interval; AUC, area under the receiver operating characteristic curve; AIC, Akaike information criterion; BIC, Bayesian information criterion
Table 4 also illustrates the Tobit regression analysis of factors associated with the additional percentage participants were willing to pay for AI-supported healthcare services. The table reveals that higher AI demand levels (β = 5.06, 95% CI: 3.96–6.16, p < 0.001), female gender (β = 3.21, 95% CI: 0.61–5.81, p = 0.016), higher monthly income (β = 0.27, 95% CI: 0.07–0.46, p = 0.008), unemployment/student status (β = 6.25, 95% CI: 1.09–11.42, p = 0.018), and retirement status (β = 9.24, 95% CI: 1.95–16.53, p = 0.013) were significantly associated with higher additional payment percentages. Participants with cardiovascular disease (β = 9.30, 95% CI: 4.00–14.60, p = 0.001) and those with musculoskeletal disorders (β = 11.17, 95% CI: 4.18–18.16, p = 0.002) were also willing to pay higher additional percentages. In contrast, participants with ENT disorders were significantly associated with lower additional payment percentages (β = −33.45, 95% CI: −53.48 to − 13.42, p = 0.001).
Discussion
This study examined self-reported awareness, demand, and willingness to pay for AI-supported healthcare services among Vietnamese adults. Overall, participants reported varying levels of familiarity with AI in healthcare, relatively high willingness to use selected AI-supported healthcare services, and limited willingness to pay additional costs. Higher income, larger household size, greater health literacy, higher eHealth literacy, and several chronic health conditions were associated with higher reported awareness, demand, or willingness to pay. These findings should be interpreted with the nature of the study measures in mind. Specifically, the awareness outcome reflects participants’ self-reported familiarity with AI applications in healthcare rather than objective knowledge or technical understanding of AI systems, whereas the demand and willingness-to-pay outcomes represent stated intentions rather than actual use of or payment for AI-supported healthcare services.
Public awareness of AI in healthcare appears to vary substantially across settings, particularly regarding the depth of understanding and familiarity with practical applications. In the present study, 49.1% of participants reported being “somewhat aware” of AI applications in healthcare, whereas only 9.2% considered themselves “well-informed.” Similar patterns have been reported internationally. In the United States, although awareness of AI was widespread, approximately 48% of respondents reported little or no awareness of AI applications in disease diagnosis, and only around 19% felt very familiar with such applications [25]. Likewise, studies from Germany found that despite broad recognition of AI, most respondents lacked direct experience with AI-assisted healthcare systems [26]. In Saudi Arabia, 45.8% of participants believed that AI could improve healthcare professionals’ performance, although relatively few considered AI capable of replacing clinicians [27]. Together, these findings suggest that public awareness often reflects general familiarity or exposure to AI rather than technical understanding or AI literacy. Accordingly, the awareness measure used in this study should be interpreted as self-reported familiarity with AI in healthcare rather than objective knowledge of AI systems or their clinical functions.
The reported demand for AI-assisted healthcare services in this study was broadly consistent with findings from other countries. In the current sample, 44.1% of participants reported being willing and 14.5% strongly willing to use AI-supported healthcare services, particularly for emergency support, telehealth, and elderly home care. Similar preferences have been observed internationally. In Poland, more than 60% of respondents supported AI applications in medical diagnostics and expressed relatively high levels of trust in AI-assisted systems [28]. Studies among older adults in Canada also reported favorable attitudes toward AI monitoring and decision-support technologies [29]. However, findings from Japan suggested more cautious attitudes, with 44.7% of respondents expressing willingness to use AI-driven medicine while simultaneously reporting concerns regarding regulation and accountability [30]. These findings indicate that public acceptance of AI-assisted healthcare is often conditional and context-dependent. In particular, the relatively high reported willingness observed in this study may reflect perceived usefulness of AI for supportive healthcare functions rather than unconditional acceptance of fully autonomous AI systems. Moreover, willingness expressed in survey settings may not necessarily translate into actual utilization in clinical practice, as real-world adoption is likely influenced by factors such as trust, affordability, infrastructure, clinician recommendation, and regulatory oversight. Because acceptance varied substantially across individual AI applications, the demand model should be interpreted as reflecting overall willingness to use AI-supported healthcare rather than preferences for any specific AI service. Future studies should investigate determinants of acceptance separately for different AI applications.
Despite moderate-to-high reported willingness to use AI-assisted healthcare services, willingness to pay additional costs remained relatively limited. In this study, 65.6% of respondents were unwilling to pay extra for AI-enhanced healthcare services, and the mean additional percentage participants were willing to pay was 5.71%. Similar patterns have been observed internationally. In Germany, acceptance of AI-related healthcare costs was generally restrained despite favorable attitudes toward AI-assisted disease monitoring and drug development [26]. In India, willingness to use AI-supported mental health services was moderated by concerns regarding privacy and the risk of misdiagnosis [31]. Studies from the United States and Japan have similarly suggested that willingness to share health data or pay for AI-assisted healthcare depends heavily on perceptions of trustworthiness, reliability, and safety [25, 30]. One possible explanation for the relatively low willingness-to-pay estimates observed in this study is that respondents may expect AI-assisted healthcare services to reduce costs through automation and operational efficiency rather than increase service prices. Such expectations may partly influence public perceptions regarding the economic value of AI in healthcare.
Socioeconomic and geographic characteristics were associated with variations in AI awareness and demand. Higher income and larger household size were associated with greater awareness and demand, which aligns with previous studies suggesting that individuals with greater socioeconomic resources are more likely to engage with digital health technologies [32, 33]. At the same time, respondents with higher educational attainment demonstrated lower reported demand for AI-assisted healthcare services and were less likely to report willingness to pay additional costs. This finding differs from several previous studies. For example, a representative survey in Hungary found that individuals with higher educational attainment and greater eHealth literacy were more likely to choose AI-supported healthcare options and demonstrated higher willingness to pay for preferred digital health technologies [15]. Similarly, evidence from telemedicine research suggests that greater willingness to pay is associated with stronger acceptance of AI-enabled healthcare services [34]. One possible interpretation is that educational attainment may influence expectations regarding the value and implementation of AI-supported healthcare services. However, the reasons underlying this association could not be determined because trust in AI, privacy concerns, perceived usefulness, and perceived risks were not directly measured. The finding may suggest that acceptance of AI and willingness to pay for AI are distinct constructs. Further research is needed to explore the mechanisms underlying this association.
Regional differences were also observed in the present study. Respondents from Southern Vietnam demonstrated higher awareness levels, whereas participants from Central and Southern Vietnam showed lower demand for AI-assisted healthcare compared with those from Northern Vietnam. These differences may reflect variations in digital access, healthcare infrastructure, urbanization, and exposure to digital technologies across regions. Southern Vietnam, particularly large urban centers such as Ho Chi Minh City, has experienced rapid expansion of digital infrastructure and private healthcare services, potentially increasing public exposure to AI-related technologies. Similar digital and regional disparities have been described in studies from Europe and other middle-income settings, where access to eHealth services was lower among underserved populations and regions with limited infrastructure [35, 36]. Although these differences were statistically significant, the underlying reasons remain unclear. Because information on urban-rural residence, internet accessibility, digital infrastructure, and previous exposure to AI technologies was not collected, caution is warranted when interpreting regional variations. Additional studies incorporating these contextual factors are needed before drawing conclusions regarding geographic disparities in AI acceptance.
Health literacy and eHealth literacy were positively associated with both AI awareness and AI demand. Similar findings have been reported in Jordan and Nigeria, where individuals with stronger health-related information skills demonstrated more favorable attitudes toward digital health technologies and AI-supported services [37, 38]. Nevertheless, these findings should not be interpreted as evidence that participants possessed deeper technical understanding of AI systems. Rather, individuals with higher literacy levels may feel more confident navigating health information and engaging with digital technologies in general. These findings suggest that clear and accessible communication strategies may remain important when introducing AI-assisted healthcare services, particularly among populations with lower levels of health or digital literacy.
Several chronic health conditions were also associated with AI-related perceptions. Respondents with cancer and respiratory disease demonstrated higher awareness and demand for AI-assisted healthcare, while respondents with cardiovascular disease and musculoskeletal disorders were associated with greater willingness to pay additional costs. Similar findings have been reported in previous studies, where individuals with chronic diseases expressed greater interest in AI-assisted monitoring, medication adherence support, and early warning systems because of their ongoing healthcare needs [39, 40]. However, acceptance varied across disease groups, as participants with ENT disorders demonstrated lower willingness to pay. This variation suggests that perceived usefulness and economic value of AI-assisted healthcare may differ according to disease characteristics, prior healthcare experiences, and patient expectations.
Several limitations should be considered when interpreting the findings of this study. First, the cross-sectional design precludes causal inference between sociodemographic characteristics, literacy, morbidity profiles, and AI-related perceptions. Second, the study relied on self-reported responses, which may be affected by recall bias and social desirability bias. Third, the use of convenience and snowball sampling through online platforms may have introduced selection bias. The study population primarily represented a digitally connected sample and was characterized by a predominance of male participants, relatively high educational attainment, a large proportion of respondents from Central Vietnam, and limited representation of older adults aged ≥ 60 years. Consequently, awareness, demand, and willingness-to-pay estimates may differ from those of the broader Vietnamese population, and the findings should not be considered nationally representative. Fourth, willingness to pay was assessed using hypothetical survey questions rather than actual payment behavior, which may introduce hypothetical response bias. Therefore, reported willingness-to-pay estimates should be interpreted as stated preferences rather than evidence of actual purchasing behavior or market demand. Fifth, willingness to use AI-supported healthcare services may not reflect actual utilization because real-world adoption is likely to depend on multiple contextual factors, including affordability, trust, infrastructure, regulation, and healthcare provider acceptance. Sixth, the AI-related outcomes were assessed using newly developed single-item measures. Although their content validity was supported through literature review, expert review, and pilot testing, formal psychometric validation, including test–retest reliability, was not performed. Finally, although comparisons with international studies were included, differences in healthcare systems, digital infrastructure, and regulatory contexts may limit direct comparability across settings.
Conclusion
Participants in this online survey demonstrated heterogeneous levels of AI awareness and relatively high reported willingness to use selected AI-assisted healthcare services, particularly for emergency support and telehealth applications. However, willingness to pay additional costs remained limited. Income, health literacy, eHealth literacy, and several chronic conditions were associated with reported awareness, demand, and willingness to pay for AI-assisted healthcare services. These findings suggest potential public interest in AI-supported healthcare while also highlighting the importance of affordability, digital access, health literacy, and public trust in future implementation efforts. The findings should be interpreted in light of the non-probability online sampling approach and may not be generalizable to the broader Vietnamese adult population.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Author contributions
Tam Ngo Thi designed the study, coordinated data collection, and drafted the manuscript.Thanh Nguyen Hoang contributed to data analysis and interpretation.Hoang Duc Hanh contributed to study methodology, data interpretation, and manuscript revision.Long Nguyen Hoang supervised the study, critically revised the manuscript, and is the corresponding author.All authors approved the final version.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of the Thang Long Institute of Medicine and Pharmacy (Code: 03.25/GCN-HĐĐĐNCYSH-IMP). All procedures were conducted in accordance with relevant guidelines and regulations. Informed consent was obtained electronically from all participants prior to participation.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.