Background: This review delves into the effects of artificial intelligence (AI) on healthcare, which is a crucial aspect considering the increasing costs of healthcare worldwide. While there is potential for AI to enh...Background: This review delves into the effects of artificial intelligence (AI) on healthcare, which is a crucial aspect considering the increasing costs of healthcare worldwide. While there is potential for AI to enhance healthcare delivery and efficiency, there are still uncertainties surrounding its effectiveness, value, and broader adoption. This comprehensive literature review aims to explore and synthesize existing knowledge on the economic impact of AI in healthcare. The primary objective of this review is to understand the potential cost savings and efficiency improvements associated with the deployment of AI in healthcare settings. By highlighting the economic implications of AI, this review seeks to offer insights into the value proposition of investing in AI technologies for stakeholders such as healthcare providers, payers, and policymakers. Methods: To conduct this review, we conducted a search of literature from 2020 to 2023 across three databases: PubMed, Scopus and Google Scholar. We specifically focused on studies that discuss the impacts of AI in healthcare and include cost evaluations, using combinations of keywords related to AI, economics, healthcare, and cost evaluation. The inclusion criteria were studies that conducted some form of economic evaluation related to AI in healthcare settings, while exclusion criteria were studies without a cost evaluation component. Data extraction and quality assessment using the CASP checklist were undertaken on the final set of included studies. Results: After screening studies, we identified 10 out of a total of 28 studies and reports that met our criteria of outlining any form of economic impact and evaluation of AI in healthcare settings. Based on our findings, implementing AI in healthcare could potentially lead to cost savings. Several studies suggest savings ranging from $200 billion to $360 billion in the United States alone. The use of AI in healthcare sectors such as ophthalmology, radiology and disease screening has shown positive economic impacts. Conclusion: While AI has potential for cost savings and efficiency improvements, in healthcare settings, it’s crucial to conduct detailed context specific cost evaluations to optimize the adoption and implementation strategies of AI.展开更多
Type II diabetes is a global health concern. This epidemic is elevating in increasing rates in Saudi Arabia. Thus, the study investigates a number of risk factors of Type II diabetes in Hail region, one of Saudi Arabi...Type II diabetes is a global health concern. This epidemic is elevating in increasing rates in Saudi Arabia. Thus, the study investigates a number of risk factors of Type II diabetes in Hail region, one of Saudi Arabia’s highest regions in diabetes records among adults. Data are collected using diabetic subjects from the Diabetes Registry Records in King Khalid Hospital at the city of Hail, Saudi Arabia, where 200 subjects’ records from 2014 to 2018 were included. A binary logistic regression was utilized to assess the association between age, gender, obesity, hypertension, family history, hypercholesterolemia, and hyperglyceridemia as risk factors and Type II diabetes. Some risk factors yielded statistical significant associations such as age (OR = 486.00 for 61 and older;OR = 468.00 for 51 - 60;and OR = 130.50 for 41 - 50;p-values ≤ 0.01), obesity (OR = 3.088;p-value ≤ 0.01), and hypertension (OR = 8.476;p-value ≤ 0.01), while gender, family history, hypercholesterolemia, and hyperglyceridemia were insignificant risk factors in our study. Proper intervention measures targeting diabetes risk factors may tackle or delay this public health issue.展开更多
文摘Background: This review delves into the effects of artificial intelligence (AI) on healthcare, which is a crucial aspect considering the increasing costs of healthcare worldwide. While there is potential for AI to enhance healthcare delivery and efficiency, there are still uncertainties surrounding its effectiveness, value, and broader adoption. This comprehensive literature review aims to explore and synthesize existing knowledge on the economic impact of AI in healthcare. The primary objective of this review is to understand the potential cost savings and efficiency improvements associated with the deployment of AI in healthcare settings. By highlighting the economic implications of AI, this review seeks to offer insights into the value proposition of investing in AI technologies for stakeholders such as healthcare providers, payers, and policymakers. Methods: To conduct this review, we conducted a search of literature from 2020 to 2023 across three databases: PubMed, Scopus and Google Scholar. We specifically focused on studies that discuss the impacts of AI in healthcare and include cost evaluations, using combinations of keywords related to AI, economics, healthcare, and cost evaluation. The inclusion criteria were studies that conducted some form of economic evaluation related to AI in healthcare settings, while exclusion criteria were studies without a cost evaluation component. Data extraction and quality assessment using the CASP checklist were undertaken on the final set of included studies. Results: After screening studies, we identified 10 out of a total of 28 studies and reports that met our criteria of outlining any form of economic impact and evaluation of AI in healthcare settings. Based on our findings, implementing AI in healthcare could potentially lead to cost savings. Several studies suggest savings ranging from $200 billion to $360 billion in the United States alone. The use of AI in healthcare sectors such as ophthalmology, radiology and disease screening has shown positive economic impacts. Conclusion: While AI has potential for cost savings and efficiency improvements, in healthcare settings, it’s crucial to conduct detailed context specific cost evaluations to optimize the adoption and implementation strategies of AI.
文摘Type II diabetes is a global health concern. This epidemic is elevating in increasing rates in Saudi Arabia. Thus, the study investigates a number of risk factors of Type II diabetes in Hail region, one of Saudi Arabia’s highest regions in diabetes records among adults. Data are collected using diabetic subjects from the Diabetes Registry Records in King Khalid Hospital at the city of Hail, Saudi Arabia, where 200 subjects’ records from 2014 to 2018 were included. A binary logistic regression was utilized to assess the association between age, gender, obesity, hypertension, family history, hypercholesterolemia, and hyperglyceridemia as risk factors and Type II diabetes. Some risk factors yielded statistical significant associations such as age (OR = 486.00 for 61 and older;OR = 468.00 for 51 - 60;and OR = 130.50 for 41 - 50;p-values ≤ 0.01), obesity (OR = 3.088;p-value ≤ 0.01), and hypertension (OR = 8.476;p-value ≤ 0.01), while gender, family history, hypercholesterolemia, and hyperglyceridemia were insignificant risk factors in our study. Proper intervention measures targeting diabetes risk factors may tackle or delay this public health issue.
