Supriya Khade‡, Mohini Mahatme‡, Neha Meshram‡, Vikram Bobade‡Corresponding author: Supriya Khade ( khadesupriya96@gmail.com ) Academic editor: Pencho Tonchev © Supriya Khade, Mohini Mahatme, Neha Meshram, Vikram Bobade. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Citation:
Khade S, Mahatme M, Meshram N, Bobade V (2026) A comprehensive exploration of clinicians‘ perspectives on the challenges & barriers in implementing Artificial Intelligence in healthcare – A questionnaire based study from a tertiary care hospital in Central India. Journal of Biomedical and Clinical Research 19: 119-128. https://doi.org/10.3897/jbcr.e182990 |  |
Introduction: Artificial Intelligence (AI) has the potential to transform healthcare in various ways. It can turn large amounts of patient data into actionable information, improve public health surveillance, accelerate health responses & produce faster & more targeted research & development. In context of developing countries, the potential of AI in public health needs to be assessed. This study enables a comprehensive exploration of clinicians' views, aiming to identify actionable insights for addressing barriers to AI implementation in healthcare systems.
Methodology: It is a cross-sectional study in which a pre-validated questionnaire developed. A purposive sample of 94 clinicians from various specialities taken in the study. Data is collected using a structured questionnaire designed after an extensive literature review & expert consultation. Data were analyzed using the appropriate statistical test.
Results: The study identified key challenges hindering AI adoption in healthcare, based on responses from 94 clinicians. The primary barriers include insufficient infrastructure (68.5%), lack of AI-specific training (44.7%) & limited collaboration between healthcare sectors (63.8%). Clinicians' skepticism (58%) about AI’s decision-making accuracy and ethical concerns regarding patient data security (74.5%) were significant obstacles. Fragmented healthcare data systems (70%) further hindered the effective AI integration.
Conclusion: While AI has substantial potential to enhance healthcare delivery, particularly in optimizing operations and personalizing treatment, addressing these challenges through comprehensive strategies involving ethical frameworks, robust data management & stakeholder engagement is crucial for successful implementation & acceptance of AI technologies in clinical practice.