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

Background Artificial intelligence (AI)–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. 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.