The Potential for Artificial Intelligence and Machine Learning in Healthcare: the future of healthcare through smart technologies
Vuong, Quang Phat (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024082924524
https://urn.fi/URN:NBN:fi:amk-2024082924524
Tiivistelmä
Significant progress has recently been made in the fields of machine learning (ML) and artificial intelligence (AI), offering enormous promise in a variety of sectors, including healthcare. The dominant goal of this study is to illuminate the integration of ML and AI inventions into healthcare and their influence on patient results.
This study includes a thorough analysis of ML and AI innovations in healthcare, including diagnostic tools and customised care. In order to evaluate the upsides and downsides of these inventions, a systematic methodology was implemented through recent case studies, peer-reviewed publications, and industry reports.
The findings indicated that there was a positive change after the use of AI and ML inventions, having improved diagnostic precision, decreased operating expenses, and helped in creating more individualised treatment regimens. The predictive analytics enhancing patient results by providing early intervention and more effective resource management is a clear demonstration of this finding; otherwise, there are various barriers overcome during the implementation of these technologies, including concerns about data privacy issues, the necessity for substantial computational resources, and the need for multidisciplinary cooperation between healthcare professionals and data scientists.
In a nutshell, even if there are a variety of potentials brought by AI and ML inventions to transform healthcare, addressing logistical, ethical, and technological issues is necessary for their effective integration. Besides, it is vital to have sufficient investigation and cooperation, optimising their capabilities and guaranteeing their constructive impact on the future landscape of healthcare.
This study includes a thorough analysis of ML and AI innovations in healthcare, including diagnostic tools and customised care. In order to evaluate the upsides and downsides of these inventions, a systematic methodology was implemented through recent case studies, peer-reviewed publications, and industry reports.
The findings indicated that there was a positive change after the use of AI and ML inventions, having improved diagnostic precision, decreased operating expenses, and helped in creating more individualised treatment regimens. The predictive analytics enhancing patient results by providing early intervention and more effective resource management is a clear demonstration of this finding; otherwise, there are various barriers overcome during the implementation of these technologies, including concerns about data privacy issues, the necessity for substantial computational resources, and the need for multidisciplinary cooperation between healthcare professionals and data scientists.
In a nutshell, even if there are a variety of potentials brought by AI and ML inventions to transform healthcare, addressing logistical, ethical, and technological issues is necessary for their effective integration. Besides, it is vital to have sufficient investigation and cooperation, optimising their capabilities and guaranteeing their constructive impact on the future landscape of healthcare.