Artificial Intelligence Modeling and Priapism.
Autor: | Pozzi E; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA. pozzi.edoardo@hsr.it.; University Vita-Salute San Raffaele, Milan, Italy. pozzi.edoardo@hsr.it.; Division of Experimental Oncology, Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy. pozzi.edoardo@hsr.it., Velasquez DA; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA., Varnum AA; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA., Kava BR; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA., Ramasamy R; Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA. |
---|---|
Jazyk: | angličtina |
Zdroj: | Current urology reports [Curr Urol Rep] 2024 Oct; Vol. 25 (10), pp. 261-265. Date of Electronic Publication: 2024 Jun 18. |
DOI: | 10.1007/s11934-024-01221-9 |
Abstrakt: | Purpose of Review: This narrative review aims to outline the current available evidence, challenges, and future perspectives of Artificial Intelligence (AI) in the diagnosis and management of priapism, a condition marked by prolonged and often painful erections that presents unique diagnostic and therapeutic challenges. Recent Findings: Recent advancements in AI offer promising solutions to face the challenges in diagnosing and treating priapism. AI models have demonstrated the potential to predict the need for surgical intervention and improve diagnostic accuracy. The integration of AI models into medical decision-making for priapism can also predict long-term consequences. AI is currently being implemented in urology to enhance diagnostics and treatment work-up for various conditions, including priapism. Traditional diagnostic approaches rely heavily on assessments based on history, leading to potential delays in treatment with possible long-term sequelae. To date, the role of AI in the management of priapism is understudied, yet to achieve dependable and effective models that can reliably assist physicians in making decisions regarding both diagnostic and treatment strategies. (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.) |
Databáze: | MEDLINE |
Externí odkaz: |