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The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings

Medicine and Health

The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings

D. Aprianto, Pailaha, et al.

This paper dives into the transformative role of artificial intelligence in nursing science and healthcare. The research covers both the promising benefits and potential challenges, such as algorithmic bias and the reliability of AI-driven clinical decisions. Conducted by Daniel Aprianto and Pailaha.

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Playback language: English
Introduction
Artificial intelligence (AI) is rapidly transforming healthcare, with significant potential to improve nursing care. The increasing sophistication of AI technologies presents opportunities and challenges for nurses. This paper addresses the urgent need to redefine the roles of nurses and AI in delivering optimal patient care. The authors highlight the potential of AI to address challenges faced by nurses, such as streamlining documentation and improving patient assessments. However, they emphasize the importance of further AI development to optimize its integration into nursing practice. While research and development of AI in healthcare have increased, there's a lack of studies that move beyond proof-of-concept and evaluate real-world clinical impacts. This article aims to explore and discuss the impact of applying AI in nursing science and healthcare systems to provide improved nursing care, ultimately stimulating further research in this area. The main purpose of the study is to assess the current use of AI systems in nursing and healthcare to provide an overview and stimulate research for the development of AI technology perfectly suited for healthcare applications.
Literature Review
The authors cite several studies to support their claims. Studies highlight the significant potential of AI to improve and expand access to high-quality medical care. Other studies emphasize the importance of AI in improving medical record organization and accessibility. Additionally, research points to the potential of AI to enhance the quality and efficiency of healthcare services through improved collaboration and communication. However, a gap was found in the use of some AI technologies in various healthcare settings, including nursing homes and home care, underlining a need for further development and research in these areas. Furthermore, studies point to the need to address concerns about AI bias and algorithmic reliability before widespread adoption in clinical settings.
Methodology
This paper is a review article. The methodology involves a comprehensive literature review of existing studies on the impact and issues of AI in nursing and healthcare settings. The authors synthesize findings from these studies to present a balanced perspective on the advantages and disadvantages of AI integration in nursing practice. No specific data collection or experimental design is mentioned; rather, the paper relies on the analysis of published research to draw its conclusions.
Key Findings
The paper identifies several key impacts of AI in nursing and healthcare: Expanding access to high-quality medical care through personalized interventions and monitoring, improving medical records by organizing electronic medical records (EMRs) and making them easily accessible, and improving the quality of services through enhanced collaboration, coordination, and communication among healthcare professionals. However, it also highlights significant issues, including the potential for AI systems to perpetuate existing human biases (both algorithmic and social), leading to inaccurate or suboptimal outcomes for certain patient groups. The paper also points to the uncertainty surrounding the validity and reliability of AI algorithms used in clinical decision-making and the consequent question of when these algorithms are reliable enough to be considered the standard of care. The advancement of AI technologies like intelligent agents, machine learning, deep learning, natural language processing, robotic process automation, administrative applications, and explainable AI are highlighted as promising developments in nursing innovation. Conversely, the authors also address the ethical and legal implications of AI in healthcare, especially concerning clinical accountability and responsibility for AI-driven decisions. The paper concludes by summarizing these positive impacts and negative issues, emphasizing the need to address the limitations and potential for bias in AI systems.
Discussion
The findings of this review article address the central research question by presenting a nuanced perspective on AI's role in nursing and healthcare. The identified positive impacts underscore AI's potential to revolutionize healthcare delivery, increasing access, improving efficiency, and enhancing the quality of care. However, the discussion on biases and algorithmic limitations highlights the crucial need for careful consideration and mitigation strategies before widespread implementation. The lack of substantial research on the clinical outcomes of AI in real-world scenarios is an important gap highlighted, indicating a need for future research to evaluate the true effectiveness and impact of AI-driven interventions. The ethical and legal considerations, particularly around accountability, further emphasize the complex challenges inherent in integrating AI into the healthcare system. The authors' conclusions suggest a need for a cautious yet optimistic approach, with a focus on addressing the identified issues to ensure responsible and beneficial AI adoption in nursing practice.
Conclusion
The paper concludes that while AI offers significant potential to improve healthcare and nursing, including expanding access to quality care, improving records, and enhancing service quality, there are critical issues to address, mainly bias and algorithmic reliability. Further research is essential to minimize the potential for errors and ensure ethical and responsible implementation. Future research should focus on validating AI algorithms in diverse populations and establishing clear guidelines for clinical accountability.
Limitations
As a review article, the paper's findings are limited by the scope of the included studies. The authors' interpretation and synthesis of existing research may be subject to bias. Furthermore, the lack of original data collection restricts the ability to draw definitive conclusions about the overall impact of AI on nursing practice. The discussion is primarily theoretical, lacking concrete evidence of AI's effectiveness in specific clinical settings.
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