Previously submitted to: Online Journal of Public Health Informatics (no longer under consideration since Dec 01, 2024)
Date Submitted: Mar 18, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Methodical integration of AI in healthcare: A systematic review
ABSTRACT
Background:
Stakeholders in healthcare systems face complexity and difficulties, but artificial intelligence (AI) has become a disruptive force in many industries, including healthcare, with the potential to improve patient care and quality of life.
Objective:
This review paper is the first to address the methodical integration of AI in healthcare, including its possible uses in patient interaction, treatment suggestions, and illness diagnosis
Methods:
by a thorough analysis of relevant indexed literature limited to English-language articles and without regard to time, including PubMed/Medline, Scopus, WOS, IEEE Xplore, and other sources. The focused inquiry explores the implications of AI deployment in healthcare settings and the envisaged outcomes of such adoption.
Results:
Our research indicates that the application of AI in healthcare has great potential to improve clinical laboratory testing, therapy selection, and illness detection. In addition to promoting personalized care, optimizing pharmaceutical doses, and improving population health management, artificial intelligence (AI) provides increased accuracy, cost reduction, time efficiency, and mistake minimization. AI is capable of making judgements for medical professionals, diagnosing diseases, and creating personalized treatment programmers
Conclusions:
Beyond just automating tasks, artificial intelligence (AI) entails creating technologies that aim to enhance patient care in a range of healthcare contexts. However, for the ethical and effective use of AI in healthcare, concerns like data security, bias reduction, and the requirement for human understanding must be taken into account.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.