Artificial intelligence (AI) holds the promise of addressing many of the numerous challenges healthcare faces, which include a growing burden of illness, an increase in chronic health conditions and disabilities due to aging and epidemiological changes, higher demand for health services, overworked and burned-out clinicians, greater societal expectations, and rising health expenditures. While technological advancements in processing power, memory, storage, and the abundance of data have empowered computers to handle increasingly complex tasks with remarkable success, AI introduces a variety of meaningful risks and challenges. Among these are issues related to accuracy and reliability, bias and equity, errors and accountability, transparency, misuse, and privacy of data. As AI systems continue to rapidly integrate into healthcare settings, it is crucial to recognize the inherent risks they bring. These risks demand careful consideration to ensure the responsible and safe deployment of AI in healthcare.
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http://dx.doi.org/10.1210/clinem/dgae017 | DOI Listing |
J Osteopath Med
January 2025
McAllen Department of Trauma, South Texas Health System, McAllen, TX, USA.
Context: The injuries caused by falls-from-height (FFH) are a significant public health concern. FFH is one of the most common causes of polytrauma. The injuries persist to be significant adverse events and a challenge regarding injury severity assessment to identify patients at high risk upon admission.
View Article and Find Full Text PDFExpert Rev Proteomics
January 2025
Skolkovo Institute of Science and Technology, Moscow, Russian Federation.
Introduction: Identifying early risks of developing Alzheimer's disease (AD) is a major challenge as the number of patients with AD steadily increases and requires innovative solutions. Current molecular diagnostic modalities, such as cerebrospinal fluid (CSF) testing and positron emission tomography (PET) imaging, exhibit limitations in their applicability for large-scale screening. In recent years, there has been a marked shift toward the development of blood plasma-based diagnostic tests, which offer a more accessible and clinically viable alternative for widespread use.
View Article and Find Full Text PDFHum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
Front Pharmacol
December 2024
Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Objective: This research project aimed to identify and analyze the top 30 drugs most commonly associated with kidney stone formation using data from the U.S. Food and Drug Administration's Adverse Event Reporting System (FAERS) database.
View Article and Find Full Text PDFExp Biol Med (Maywood)
December 2024
Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate prediction of disease progression challenging. This study aimed to develop molecular-level prognostic models to personalize treatment strategies for IPF patients.
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