Framework for AI Adoption in Healthcare Sector: Integrated DELPHI, ISM–MICMAC Approach

Abstract

Artificial Intelligence (AI) adoption is transforming many industries, but its impact on the healthcare sector is life-changing. Recent studies and tests show that AI can deliver identical or better prognoses, diagnoses, and surgical outcomes compared with medical professionals. Healthcare sectors are adopting AI, and its applications are reforming it by reducing expenditure and exceeding patient satisfaction. The dearth of AI advocacy and adoption has forfeited large annual opportunity costs for the health industry and AIE (artificial intelligence engineers) and uncountable possibilities for AI to save lives. There is, however, a shortage of studies using quantitative models to test the barrier interrelationship and its effect on AI adoption, especially from the perspective of a developing country like India. Therefore, this study explores the barriers to adopting AI in healthcare in India. A total of 250 barriers related to technology adoption are determined after the thorough analysis of previous studies and several FGDs. Barriers are reduced to 16 most relevant barriers through multiple Healthcare expert FGDs and the DELPHI method. Interpretive Structural Modelling (ISM) and MICMAC (Crossimpact matrix multiplication applied to classification) are the analytical techniques used to classify the barriers into different impact levels and importance. The derived outcomes from the ISM and MICMAC methods illustrate that the unavailability of infrastructure and policy support and AI's potential cybersecurity vulnerabilities are the predominant problems for AI adoption in healthcare. Our research exploration eventually anticipates and offers solutions for recognizing the key obstacles that must be overcome (on a priority basis) and ensuring successful AI adoption in India's healthcare. The outcome is proposed to add to the theory of technology adoption, which facilitates the organizations in selecting the technology and opening the path for its spread and use by healthcare stakeholders. The study's findings indicate that government interventions are required to promote AI adoption, especially in developing countries. These outcomes will assist healthcare professionals and policymakers in fostering AI in healthcare. Our research contributes to the AI and healthcare episteme through empirical corroboration and probing the quantitative effect of barriers to AI adoption.

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