Artificial intelligence (AI) has been introduced in urology research and practice. Application of AI leads to better accuracy of disease diagnosis and predictive model for monitoring of responses to medical treatments. This mini-review article aims to summarize current applications and development of AI in urology setting, in particular for diagnosis and treatment of urological diseases. This review will introduce that machine learning algorithm-based models will enhance the prediction accuracy for various bladder diseases including interstitial cystitis, bladder cancer, and reproductive urology.
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http://dx.doi.org/10.12965/jer.2142596.298 | DOI Listing |
J Med Internet Res
January 2025
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Background: The aging global population and the rising prevalence of chronic disease and multimorbidity have strained health care systems, driving the need for expanded health care resources. Transitioning to home-based care (HBC) may offer a sustainable solution, supported by technological innovations such as Internet of Medical Things (IoMT) platforms. However, the full potential of IoMT platforms to streamline health care delivery is often limited by interoperability challenges that hinder communication and pose risks to patient safety.
View Article and Find Full Text PDFJMIR Hum Factors
January 2025
Institute of General Practice, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Background: The internet is a key source of health information, but the quality of content from popular search engines varies, posing challenges for users-especially those with low health or digital health literacy. To address this, the "tala-med" search engine was developed in 2020 to provide access to high-quality, evidence-based content. It prioritizes German health websites based on trustworthiness, recency, user-friendliness, and comprehensibility, offering category-based filters while ensuring privacy by avoiding data collection and advertisements.
View Article and Find Full Text PDFEnviron Health Perspect
January 2025
Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK.
Background: Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by , with both foodborne and nonfoodborne exposure routes and high mortality. Although ecological drivers of in the environment have been well-characterized, fewer models have been able to apply this to human infection risk due to limited surveillance.
Objectives: The Cholera and Other Illness Surveillance (COVIS) system database has reported infections in the United States since 1988, offering a unique opportunity to both explore the forecasting capabilities machine learning could provide and to characterize complex environmental drivers of infections.
Science
January 2025
Department of Economics, University of Exeter Business School, Exeter, UK.
A field experiment provides a promising proof of concept.
View Article and Find Full Text PDFScience
January 2025
School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA.
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