The Impact of AI Integration in Healthcare: Exploring Regulatory,
Cultural, and Strategic Barriers
Name
venkatanarayanan-sriya108-msms-mgt-2025-thesis.pdf
Description
Thesis PDF
Size
510.89 KB
Format
Adobe PDF
Checksum (MD5)
92354bf0606cadcb8cf2551df4410767
Author(s)
Venkatanarayanan, Sriya
Advisor(s)
Kellogg, Katherine
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
This thesis investigates the barriers and enablers to predictive AI adoption in healthcare through a thematic synthesis of 13 academic articles and real-world case studies published over the last five years. Barriers were categorized into three domains: regulatory, cultural, and strategic. These included challenges such as fragmented regulation, clinician skepticism, data quality limitations, and poor alignment with clinical workflows. Cross-cutting patterns, stakeholder tensions, and recurring meta-themes revealed that these barriers are deeply interconnected. Drawing from over 200 individual findings, an actionable visual framework was developed to guide responsible and sustainable predictive AI integration. The proposed model, consisting of an internal “Pyramid” of enablers and an external “Circular Loop” of ecosystem conditions, provides a practical structure for aligning governance, engagement, and workflow with ongoing commitments to equity, collaboration, safety, and transparency.
MIT Department
Sloan School of Management
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