Vol. 29 No. 2 (2026) Cover Image
Vol. 29 No. 2 (2026)

Published: June 20, 2026

Pages: 384-391

Articles

Engineering Determinants of Artificial Intelligence Adoption in Indian Healthcare Systems: A Regression-Validated Institutional Readiness Model

Prateek Singh 1 Sudipta Das 2 Justin Babu 3 Alok Satsangi 4
1 Department of Hospital Management, NSHM Knowledge Campus, Durgapur-India
2 Department of Public Health, NSHM Knowledge Campus, Durgapur- India.
3 Department of Hospital Management, NSHM Knowledge Campus, Durgapur- India.
4 NSHM Knowledge Campus, Durgapur- India.
History
  • Received: March 4, 2026
  • Revised: April 20, 2026
  • Accepted: April 28, 2026
  • Available Online: June 20, 2026
DOI

https://doi.org/10.29194/NJES.29020384

Abstract

The introduction of Artificial Intelligence (AI) in medical care is becoming an acknowledged challenge in the framework of engineering systems, in which the level of computational readiness, the integration of the infrastructure, the level of user competence, and the limitations of the ethical aspect play a role. The paper analyzes the most important engineering predictors of a readiness to use AI in Indian hospitals, which are assessed using correlation and multiple linear regression frameworks._x000D_ There was a structured survey on 120 healthcare workers and 20 deep interviews with experts to evaluate system-level perceptions and institutional preparedness. The results of regression showed that the model is well fitted (R 2 = 0.61, p < 0.001) with Perceived Usefulness (= 0.43), Infrastructure Availability (= 0.38) and AI Training Exposure ( = 0.26) identified as significant and positive predictors of AI adoption readiness. The Ethical Concern Scores (β = -0.32) had a significant negative impact, highlighting the importance of governance as an important constraint of the system._x000D_ These findings were reinforced by qualitative thematic analysis, which identified interoperability challenges, lack of computational training, data governance oversights, and infrastructure vulnerability as significant engineering limitations. The respondents positively indicated the conditional acceptance of AI on the basis of the explainable model transparency, as well as the system of institutional AI training._x000D_ The research adds a new regression-tested engineering preparedness pathway to AI implementation in Low- and Middle-Income Country (LMIC) hospital setting, suggesting system interface, capacity formation, and ethical algorithm control as the main keystones on the sustainable AI implementation in Indian healthcare.

Keywords

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