A recent study conducted by researchers from Begum Rokeya University and University of Dhaka offers a detailed examination of how healthcare professionals and the public in Bangladesh perceive and adopt AI technologies.

The study employed a quantitative, cross-sectional survey design, gathering data from 399 participants across Dhaka and Rangpur. These included healthcare professionals, service users, and individuals with varying levels of exposure to AI.

A structured questionnaire, initially developed in English and later translated into Bangla, captured demographic details alongside key variables such as awareness, perception, readiness, and adoption of AI. Responses were measured using a five-point Likert scale, allowing the researchers to assess both attitudes and behavioural tendencies. Data collection took place over three months, combining online surveys with in-person engagement to ensure broader representation.

To maintain analytical rigour, the researchers used SPSS and SmartPLS software for statistical analysis, including structural equation modelling. The reliability and validity of the data were carefully tested. High factor loadings and strong composite reliability scores confirmed internal consistency, while a Kaiser-Meyer-Olkin value of 0.939 indicated excellent suitability for factor analysis. Tests for common method bias further ensured that the findings were not distorted by the data collection process.

The findings highlight that readiness for AI is the most critical factor influencing its adoption in Bangladesh’s healthcare sector. Among the contributing elements, technological awareness and social media influence emerged as the strongest drivers, significantly enhancing readiness levels.

This suggests that exposure to AI-related information, particularly through digital platforms, plays a central role in shaping acceptance. Perceived risk also showed a positive, though comparatively weaker, effect. Rather than discouraging adoption, concerns around privacy and reliability appear to coexist with a recognition of AI’s potential benefits.

Interestingly, factors often considered influential in global studies, such as perceived susceptibility and personal innovativeness, did not show significant impact in this context. This indicates that in Bangladesh, external influences like information access and social discourse may outweigh individual tendencies towards innovation.

Mediation analysis further demonstrated that readiness for AI acts as a crucial link between awareness and actual adoption, reinforcing the idea that knowledge alone is insufficient without confidence in application.

The demographic profile of respondents adds further nuance. Most participants were educated at undergraduate or postgraduate levels, with technical assistants and management personnel forming a large share of the sample. This suggests that operational and administrative actors may currently play a more prominent role in shaping AI adoption than frontline clinical staff, highlighting a potential gap in engagement within core healthcare delivery roles.

Overall, the study reveals that the challenge in Bangladesh is not resistance to AI, but a lack of structured readiness. Awareness, particularly through social media and educational initiatives, is driving interest, while perceived risks are not strong enough to hinder progress.

The important findings point towards the need for stronger regulatory frameworks, improved AI literacy, and targeted training programmes. Without these efforts, the integration of AI into Bangladesh’s healthcare system may remain limited, despite its considerable potential to transform service delivery.