Ethical and clinical implications of AI integration in cardiovascular healthcare
1 Department of Computer Science, DePaul University Chicago, USA.
2 Department of Computer Science, Chicago State University, USA.
3 Department of Computer Science, Illinois Institute of Technology, USA.
4 Department of Computational Medicine & Bioinformatics, University of Michigan, USA.
5 Department of Computer Science, University of Lahore, Lahore, Pakistan.
5 Department of Computer Science, University of Lahore, Lahore, Pakistan.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 2479–2501
Publication history:
Received on 13 August 2024; revised on 22 September 2024; accepted on 25 September 2024
Abstract:
Then parsed out of the stories we've been writing over the past year, about 23 companies with Artificial Intelligence (AI) at their core that are redefining how cardiovascular healthcare is being delivered in this digitally connected age driving either faster & more precise diagnosis/case triaging through AI-augmented physician tools or recommending treatments based on AI-enabled insights from clinical data as well as hopefully aiding in patient outcomes. This paper reviews some AI methods in cardiovascular care, including the advantages and disadvantages of implementing these methodologies from an ethical perspective. Applications analyzing diagnostic images and detecting potential cardiovascular events as well as those that touched how patient care is delivered were ripe for artificial intelligence, which broadly encompasses machine learning, deep learning and predictive modeling. However, as we turn more to AI for answers to health questions, a host of ethical issues from patient privacy and civil liberties tech bias to transparent explainable AI come into play. These problems cast a large shadow on the trust of patients, national health care equity and general AI application performance in routine clinical practice.
Certainly, as it relates to the clinical side of things, AI has been shown to improve diagnostic accuracy in heart disease by learning and understanding patterns that may be too complex for human clinicians. AI-driven technologies can be used in identifying personal traits between patients that may then make way for personalized patient-specific treatments, and this could result in more effective care strategies. Nevertheless, this still faces numerous challenges in practical implementation, which includes hardware restrictions and constraints in the regulation’s domain as well as a learning curve for healthcare workers to get used to changing technologies. The clinical implications are delineated in this review, and we divide the challenges for application of AI into subgroups allowing the use of AI systems in other cardiovascular areas, streamlining of clinical processes make health care less expensive and improve patient care.
It also provides AI ethics frameworks and guidelines, which aim to promote ethical principles in the development and deployment of AI technologies, including perspectives on transparency, accountability and fairness within AI. The study says that case studies and examples of AI applications in cardiovascular health today will outline recommendations for developers, clinicians and policymakers to comply with ethical standards when developing AI technologies clinically.
Keywords:
Artificial Intelligence; Cardiovascular Healthcare; Ethics; Clinical Implications; AI Bias; Patient Privacy
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0