Artificial Intelligence-Based Clinical Decision-Making in Erectile Dysfunction: a Narrative Review
- PMID: 39663266
- DOI: 10.1007/s11934-024-01251-3
Artificial Intelligence-Based Clinical Decision-Making in Erectile Dysfunction: a Narrative Review
Abstract
Purpose of review: Artificial Intelligence (AI) has great potential in erectile dysfunction (ED) diagnosis and treatment. This review aims to summarize AI-based clinical decision-making in ED.
Recent findings: Based on the literature search, forty-seven articles related to AI and ED were analyzed and their findings were summarized. AI may help diagnose ED and offer treatment for it. Developing AI chatbots may also be beneficial for ED patients who are embarrassed to seek treatment. However, there are deficiencies in AI programs and a lack of accuracy in offering precise diagnoses and treatments for ED. AI technology integrates positively into ED clinical decision-making processes and needs progressive research to gain precision and efficiency.
Keywords: Artificial intelligence; ChatGPT; Deep neural networks; Erectile dysfunction; Machine learning.
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Conflict of interest statement
Declarations. Human and Animal Rights and Informed Consent: All reported studies/experiments with human subjects performed by the authors were performed in accordance with all applicable ethical standards, including the Helsinki Declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines. Competing Interests: Author E.C.S. has payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events by Kanna Health Limited and Viatris; plus, has stock or stock options in Virility Medical. However, the authors did not receive support from any organization for the submitted work.
References
-
- Moor J. The Dartmouth College Artificial Intelligence Conference: The next fifty years. 2006;27:87. https://doi.org/10.1609/aimag.v27i4.1911
-
- Oh JH, Kerns S, Ostrer H, Powell SN, Rosenstein B, Deasy JO. Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes. Sci Rep. 2017;7:43381. https://doi.org/10.1038/srep43381 . - DOI - PubMed - PMC
-
- Zhang C, Gao X, Fan B, Guo S, Lyu X, Shi J, et al. Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer. World J Urol. 2024;42(1):93. https://doi.org/10.1007/s00345-024-04775-y . - DOI - PubMed
-
- Hung AJ, Chen J, Gill IS. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg. 2018;153(8):770–1. https://doi.org/10.1001/jamasurg.2018.1512 . - DOI - PubMed - PMC
-
- Krater M, Abuhattum S, Soteriou D, Jacobi A, Kruger T, Guck J, Herbig M. AIDeveloper: deep learning image classification in life science and beyond. Adv Sci (Weinh). 2021;8(11):e2003743. https://doi.org/10.1002/advs.202003743 . - DOI - PubMed
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