AI-Driven Preventive Healthcare: Business Challenges and Opportunities in Cardiovascular Diagnostics
Issa, Youssef (2025)
2025
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025052013485
https://urn.fi/URN:NBN:fi:amk-2025052013485
Tiivistelmä
This study explores the business challenges and opportunities associated with the adoption of AIdriven preventive healthcare solutions in cardiovascular diagnostics. The research focuses on understanding the key barriers to AI implementation and the potential benefits it offers for improving healthcare outcomes.
The literature review examines previous research on AI applications in healthcare, particularly cardiovascular diagnostics, highlighting both the technological advancements and the challenges faced in the adoption of AI. It also explores existing business models, regulatory considerations, and ethical concerns surrounding AI in healthcare.
A qualitative research design was employed, utilizing semi-structured interviews with healthcare professionals and industry experts to gather insights into the business challenges and opportunities of AI in preventive cardiovascular healthcare. Thematic analysis was conducted to identify key themes and sub-themes from the interview data.
The study identified several significant challenges to the adoption of AI, including clinical skepticism, data privacy concerns, and integration issues with existing healthcare systems. On the other hand, promising business opportunities were found in AI-powered diagnostic imaging, risk prediction platforms, and remote monitoring technologies, which have the potential to improve patient outcomes and reduce healthcare costs.
The study concludes that while AI offers significant opportunities to enhance cardiovascular diagnostics, overcoming barriers such as clinical trust, regulatory hurdles, and integration challenges is essential for its widespread adoption. The research provides insights for both practitioners and AI developers to consider when implementing AI solutions in healthcare settings. The research acknowledges limitations such as a small sample size and the geographical scope of the study. Future work should include broader geographic studies, longitudinal research, and wider stakeholder involvement to fully understand the impact of AI in healthcare.
The literature review examines previous research on AI applications in healthcare, particularly cardiovascular diagnostics, highlighting both the technological advancements and the challenges faced in the adoption of AI. It also explores existing business models, regulatory considerations, and ethical concerns surrounding AI in healthcare.
A qualitative research design was employed, utilizing semi-structured interviews with healthcare professionals and industry experts to gather insights into the business challenges and opportunities of AI in preventive cardiovascular healthcare. Thematic analysis was conducted to identify key themes and sub-themes from the interview data.
The study identified several significant challenges to the adoption of AI, including clinical skepticism, data privacy concerns, and integration issues with existing healthcare systems. On the other hand, promising business opportunities were found in AI-powered diagnostic imaging, risk prediction platforms, and remote monitoring technologies, which have the potential to improve patient outcomes and reduce healthcare costs.
The study concludes that while AI offers significant opportunities to enhance cardiovascular diagnostics, overcoming barriers such as clinical trust, regulatory hurdles, and integration challenges is essential for its widespread adoption. The research provides insights for both practitioners and AI developers to consider when implementing AI solutions in healthcare settings. The research acknowledges limitations such as a small sample size and the geographical scope of the study. Future work should include broader geographic studies, longitudinal research, and wider stakeholder involvement to fully understand the impact of AI in healthcare.