Background And Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.
Methods: This review analyzes the impact of AI on healthcare using data from the Web of Science (2014-2024), focusing on keywords like AI, ML, and healthcare applications. It examines the uses and effects of AI on healthcare by synthesizing recent literature and real-world case studies, such as Google Health and IBM Watson Health, highlighting AI technologies, their useful applications, and the difficulties in putting them into practice, including problems with data security and resource limitations. The review also discusses new developments in AI, and how they can affect society.
Results: The findings demonstrate how AI is enhancing the skills of medical professionals, enhancing diagnosis, and opening the door to more individualized treatment plans, as reflected in the steady rise of AI-related healthcare publications from 158 articles (3.54%) in 2014 to 731 articles (16.33%) by 2024. Core applications like remote monitoring and predictive analytics improve operational effectiveness and patient involvement. However, there are major obstacles to the mainstream implementation of AI in healthcare, including issues with data security and budget constraints.
Conclusion: Healthcare may be transformed by AI, but its successful use requires ethical and responsible use. To meet the changing demands of the healthcare sector and guarantee the responsible application of AI technologies, the evaluation highlights the necessity of ongoing research, instruction, and multidisciplinary cooperation. In the future, integrating AI responsibly will be essential to optimizing its advantages and reducing related dangers.
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http://dx.doi.org/10.1002/hsr2.70312 | DOI Listing |
Cardiovasc Revasc Med
December 2024
Cardiology Division, Hennepin Healthcare, Minneapolis, MN, USA.
Background: There is a growing body of data to support the presence of sex disparities in outcomes of cardiovascular related hospitalizations. Despite this, there remains a paucity of data on relationships between sex and in-hospital outcomes in patients receiving a left atrial appendage occlusion device (LAAOD).
Methods: We examined the 2016-2020 Nationwide Readmission Database to identify patients with Atrial Fibrillation receiving a LAAOD.
Life Sci
January 2025
Public Health - Seattle & King County, Seattle, WA, USA; Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA.
This review aims to describe the neurologic post-COVID-19 conditions (PCC, also known as "long COVID"), a complex array of diagnoses that can occur following recovery from acute COVID-19. The review also includes clinical considerations for the recognition, diagnosis and management of neurologic manifestations of PCC. Cognitive impairment ("Brain Fog"), headaches, and neuropathies are specifically reviewed.
View Article and Find Full Text PDFTop Antivir Med
December 2024
University of Minnesota, Minneapolis, USA.
People with HIV (PWH) are living longer and experiencing a greater burden of morbidity from non-AIDS-defining conditions. Chronically treated HIV disease is associated with ongoing systemic inflammation that contributes to the development of chronic conditions (eg, cardiovascular disease) and geriatric syndromes (eg, frailty). Apart from HIV disease, a progressive increase in systemic inflammation is a characteristic feature of biologic aging, a process described as "inflammaging.
View Article and Find Full Text PDFSci Total Environ
January 2025
Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
The COVID-19 pandemic highlighted shortcomings in forecasting models, such as unreliable inputs/outputs and poor performance at critical points. As COVID-19 remains a threat, it is imperative to improve current forecasting approaches by incorporating reliable data and alternative forecasting targets to better inform decision-makers. Wastewater-based epidemiology (WBE) has emerged as a viable method to track COVID-19 transmission, offering a more reliable metric than reported cases for forecasting critical outcomes like hospitalizations.
View Article and Find Full Text PDFJ Surg Res
January 2025
Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana. Electronic address:
Introduction: Patients with obesity seeking bariatric surgery undergo an extensive evaluation by a multidisciplinary team of healthcare professionals' (HCPs) to assess patient compliance among other factors and determine their eligibility for surgery. However, the HCPs' assessments are not devoid of bias that may affect eligibility and preoperative decisions. This study aimed to investigate team members' ability to predict patient outcomes following bariatric surgery.
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