From diagnosis to management: unveiling the challenges of artificial intelligence solutions in cardiovascular healthcare

Idakwo, Valentine (2025): From diagnosis to management: unveiling the challenges of artificial intelligence solutions in cardiovascular healthcare. BMC Digital Health, 3: 54. ISSN 2731-684X

Abstract

Background

Cardiovascular diseases (CVDs) are the leading cause of mortality in the world. Artificial Intelligence (AI) offers an opportunity to improve the quality of care provided to cardiovascular patients due to its ability to handle large and complex data. Despite promising results obtained in several studies, widespread adoption of AI in cardiovascular care is lacking due to several existing challenges. This study aims to identify and analyze these challenges.

Methods

A mixed-methods approach was employed, combining semi-structured interviews with a self-administered online survey. Sequential sampling was used to select participants. Interview data were analyzed using inductive thematic analysis, while survey responses were examined through summary statistics and correlation analysis.

Results

A total of 5 interviews were conducted, and 91 valid survey responses were obtained. Survey respondents included doctors, nurses, medical researchers, health IT specialists, hospital administrators, and other clinically affiliated participants working with cardiovascular patients. Eight major challenges were identified: data-related challenges, regulatory challenges, infrastructural challenges, gaps in knowledge, transparency challenges, ethical challenges, change management issues, and acceptance challenges.

Conclusion

This mixed-method study finds that the main obstacles to bringing AI into cardiovascular care stem not from algorithmic limitations but from a constellation of data, regulatory, infrastructural and human-factor gaps. Closing these interdependent bottlenecks through coordinated policy, capacity-building and transparent evaluation is therefore essential for translating AI’s proven diagnostic promise into routine clinical practice.

• Dokumententyp: Artikel (Klinikum der LMU)
• Organisationseinheit (Fakultäten): 07 Medizin > Klinikum der LMU München > Medizinische Klinik und Poliklinik I (Kardiologie)
• DFG-Fachsystematik der Wissenschaftsbereiche: Lebenswissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892