Abstract

In this paper, we focus on providing a narrative review of healthcare services in which artificial intelligence (AI) based services are used as part of the operations and analyze key elements to create successful AI-based services for healthcare. The benefits of AI in healthcare are measured by how AI is improving the healthcare outcomes, help caregivers in work, and reducing healthcare costs. AI market in healthcare sector have also a high market potential with 28% global compound annual growth rate. This paper will collect outcomes from multiple perspectives of healthcare sector including financial, health improvement, and care outcome as well as provide proposals and key factors for successful implementation of AI methods in healthcare. It is shown in this paper that AI implementation in healthcare can provide cost reduction and same time provide better health outcome for all.

Highlights

  • The healthcare industry is undergoing a revolution

  • We focused on quantitative studies in which we have collected some services using artificial intelligence (AI) in the healthcare sector by collecting results in publications such as PubMed, Nature Biomedical Engineering, Oxford University Press, Food and Drug Administration, and reports reported by PWC and Accenture

  • There will be shortness of trained nurses and healthcare professionals

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Summary

Dipak Kalra Belgium

University of Gent, Gent, Any reports and responses or comments on the article can be found at the end of the article. Author roles: Väänänen A: Conceptualization, Investigation, Resources, Visualization, Writing – Original Draft Preparation; Haataja K: Writing – Review & Editing; Vehviläinen-Julkunen K: Supervision, Writing – Review & Editing; Toivanen P: Project Administration, Supervision, Writing – Review & Editing. How to cite this article: Väänänen A, Haataja K, Vehviläinen-Julkunen K and Toivanen P. Any further responses from the reviewers can be found at the end of the article

Introduction
References method reference type
References reference type diagnosis
Conclusion and future work
Artificial Intelligence
Chowdhury GG
16. Lee DI
24. Sensely
45. Williams DJP
49. Data Bringe Market Researcher
Findings
53. Food and Drug Administration
Full Text
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