Adoption of AI-based diabetes diagnosis: a patient and institutional perspective in Maharashtra and Karnataka

Abstract

Purpose Artificial intelligence (AI) is transforming diabetes management in India, yet its adoption remains limited beyond metropolitan areas. This study aims to explore AI’s role across different phases of diabetes care, focusing on healthcare access, behavioral change and patient engagement in non-metropolitan regions of Maharashtra and Karnataka. Design/methodology/approach A qualitative study was conducted using semi-structured interviews with healthcare professionals and their patients exclusively from 15 hospitals and super-specialty clinics in Maharashtra and Karnataka. Thematic analysis identified key trends in AI adoption, particularly in diagnosis, treatment and patient engagement. Findings AI-powered platforms enhance early access to healthcare, risk assessment and patient-clinician interactions. AI-driven insights support personalized treatment, real-time monitoring and predictive healthcare interventions. In addition, AI fosters behavioral change through continuous engagement and lifestyle recommendations. However, challenges such as infrastructure limitations, data security concerns and lack of AI literacy among healthcare providers hinder widespread adoption. Research limitations/implications This study’s findings are specific to 15 hospitals in Maharashtra and Karnataka, which may limit broader applicability. The perspectives of frontline healthcare workers and patients require deeper exploration. Expanding research to a larger, more diverse sample and conducting longitudinal studies will strengthen insights into AI’s long-term impact on diabetes care. Addressing AI literacy, data security and infrastructure gaps is essential for widespread adoption. Policymakers must establish robust frameworks ensuring algorithmic transparency and equitable access, reinforcing AI’s effectiveness in healthcare. Practical implications Integrating AI in diabetes care enhances early diagnosis, personalized treatment and continuous monitoring, improving patient outcomes. Hospitals must invest in AI literacy programs to equip healthcare professionals with the necessary skills for effective adoption. Policymakers should establish regulatory frameworks to ensure data security, ethical AI use and interoperability with existing healthcare systems. AI developers must focus on user-friendly interfaces to increase patient trust and engagement. Expanding AI adoption in non-metropolitan areas requires infrastructure improvements and public–private partnerships. Strengthening these areas will accelerate AI-driven healthcare transformation, making diabetes management more efficient, accessible and patient-centric. Social implications AI-driven diabetes care can bridge healthcare accessibility gaps, particularly in underserved regions, by enabling early diagnosis and remote monitoring. Increased AI adoption fosters health equity, reducing disparities between urban and rural populations. However, digital literacy and trust in AI remain challenges, necessitating awareness campaigns and patient education initiatives. Ethical AI implementation must prioritize data privacy and algorithmic transparency to maintain public confidence. In addition, AI-driven healthcare can empower individuals with personalized health insights, promoting proactive disease management and healthier lifestyles. By fostering collaboration between healthcare providers, policymakers and technology developers, AI can contribute to a more inclusive and patient-centric healthcare system. Originality/value This study fills a critical research gap by evaluating AI’s impact on diabetes care beyond metropolitan India. With Maharashtra and Karnataka facing high diabetes prevalence and limited AI adoption, understanding its regional healthcare applications is essential. The study highlights practical strategies to overcome adoption barriers, advocating collaborative efforts between hospitals, policymakers and AI developers to maximize AI’s potential in healthcare.

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