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Clinical Advances in Artificial Intelligence in Urology
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Clinical Advances in Artificial Intelligence in Urology

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nephrology & Urology".

Deadline for manuscript submissions: 10 August 2025 | Viewed by 3829

Special Issue Editor

Dr. Theodoros Tokas

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Guest Editor
Department of Urology, University General Hospital of Heraklion, University of Crete, Medical School, Heraklion, Greece
Interests: prostate biopsy; prostate cancer; endourology; prostate hyperplasia; urinary stone disease; laparoscopy; robotic surgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The science of artificial intelligence (AI) is advancing rapidly in various domains, including medicine. Similarly, AI has broadened its uses in the diagnostic and therapeutic domains of urology. Its application can significantly enhance the individualization of diagnosis and treatment and lower healthcare costs. However, its prospective uses and benefits are frequently overlooked or only partially understood. Additionally, to better comprehend this technology and safeguard patients and providers, potential ethical concerns, such as patient safety, cybersecurity, transparency, and data interpretability, need to be assessed before further use of AI is attempted. Numerous extremely complicated non-medical processes have already been investigated, and automated process optimization is possible thanks to recent developments in computer science. When used appropriately, creating AI models can result in more efficient patient-related data processing and analysis, improving urology patient diagnosis and treatment. Artificial intelligence (AI) using machine learning (ML) algorithms and artificial neural network (ANN) processes has been implemented to provide a precise and transversal perspective of a clinical scenario. We invite authors to submit papers for this Special Issue discussing the clinical application of AI in urology for both diagnosis and treatment.

Dr. Theodoros Tokas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • deep learning
  • artificial neural networks
  • computer
  • AI
  • machine learning

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Published Papers (4 papers)

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Research

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9 pages, 707 KiB  
Article
Use of Artificial Intelligence in Vesicoureteral Reflux Disease: A Comparative Study of Guideline Compliance
by Mehmet Sarikaya, Fatma Ozcan Siki and Ilhan Ciftci
J. Clin. Med. 2025, 14(7), 2378; https://doi.org/10.3390/jcm14072378 - 30 Mar 2025
Viewed by 254
Abstract
Objective: This study aimed to evaluate the compliance of four different artificial intelligence applications (ChatGPT-4.0, Bing AI, Google Bard, and Perplexity) with the American Urological Association (AUA) vesicoureteral reflux (VUR) management guidelines. Materials and Methods: Fifty-one questions derived from the AUA guidelines were [...] Read more.
Objective: This study aimed to evaluate the compliance of four different artificial intelligence applications (ChatGPT-4.0, Bing AI, Google Bard, and Perplexity) with the American Urological Association (AUA) vesicoureteral reflux (VUR) management guidelines. Materials and Methods: Fifty-one questions derived from the AUA guidelines were asked of each AI application. Two experienced paediatric surgeons independently scored the responses using a five-point Likert scale. Inter-rater agreement was analysed using the intraclass correlation coefficient (ICC). Results: ChatGPT-4.0, Bing AI, Google Bard, and Perplexity received mean scores of 4.91, 4.85, 4.75 and 4.70 respectively. There was no statistically significant difference between the accuracy of the AI applications (p = 0.223). The inter-rater ICC values were above 0.9 for all platforms, indicating a high level of consistency in scoring. Conclusions: The evaluated AI applications agreed highly with the AUA VUR management guidelines. These results suggest that AI applications may be a potential tool for providing guideline-based recommendations in paediatric urology. Full article
(This article belongs to the Special Issue Clinical Advances in Artificial Intelligence in Urology)
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12 pages, 3235 KiB  
Article
Predicting Semen Analysis Parameters from Testicular Ultrasonography Images Using Deep Learning Algorithms: An Innovative Approach to Male Infertility Diagnosis
by Lutfullah Sagir, Esat Kaba, Merve Huner Yigit, Filiz Tasci and Hakki Uzun
J. Clin. Med. 2025, 14(2), 516; https://doi.org/10.3390/jcm14020516 - 15 Jan 2025
Viewed by 960
Abstract
Objectives: Semen analysis is universally regarded as the gold standard for diagnosing male infertility, while ultrasonography plays a vital role as a complementary diagnostic tool. This study aims to assess the effectiveness of artificial intelligence (AI)-driven deep learning algorithms in predicting semen analysis [...] Read more.
Objectives: Semen analysis is universally regarded as the gold standard for diagnosing male infertility, while ultrasonography plays a vital role as a complementary diagnostic tool. This study aims to assess the effectiveness of artificial intelligence (AI)-driven deep learning algorithms in predicting semen analysis parameters based on testicular ultrasonography images. Materials and Methods: This study included male patients aged 18–54 who sought evaluation for infertility at the Urology Outpatient Clinic of our hospital between February 2022 and April 2023. All patients underwent comprehensive assessments, including blood hormone profiling, semen analysis, and scrotal ultrasonography, with each procedure being performed by the same operator. Longitudinal-axis images of both testes were obtained and subsequently segmented. Based on the semen analysis results, the patients were categorized into groups according to sperm concentration, progressive motility, and morphology. Following the initial classification, each semen parameter was further subdivided into “low” and “normal” categories. The testicular images from both the right and left sides of all patients were organized into corresponding folders based on their associated laboratory parameters. Three distinct datasets were created from the segmented images, which were then augmented. The datasets were randomly partitioned into an 80% training set and a 20% test set. Finally, the images were classified using the VGG-16 deep learning architecture. Results: The area under the curve (AUC) values for the classification of sperm concentration (oligospermia versus normal), progressive motility (asthenozoospermia versus normal), and morphology (teratozoospermia versus normal) were 0.76, 0.89, and 0.86, respectively. Conclusions: In our study, we successfully predicted semen analysis parameters using data derived from testicular ultrasonography images through deep learning algorithms, representing an innovative application of artificial intelligence. Given the limited published research in this area, our study makes a significant contribution to the field and provides a foundation for future validation studies. Full article
(This article belongs to the Special Issue Clinical Advances in Artificial Intelligence in Urology)
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12 pages, 508 KiB  
Article
ChatGPT as a Support Tool for Informed Consent and Preoperative Patient Education Prior to Penile Prosthesis Implantation
by Jacob Schmidt, Isabel Lichy, Thomas Kurz, Robert Peters, Sebastian Hofbauer, Hennig Plage, Jonathan Jeutner, Thorsten Schlomm, Jörg Neymeyer and Bernhard Ralla
J. Clin. Med. 2024, 13(24), 7482; https://doi.org/10.3390/jcm13247482 - 10 Dec 2024
Cited by 1 | Viewed by 1091
Abstract
Background/Objectives: Artificial intelligence (AI), particularly natural language processing (NLP) models such as ChatGPT, presents novel opportunities for patient education and informed consent. This study evaluated ChatGPT’s use as a support tool for informed consent before penile prosthesis implantation (PPI) in patients with [...] Read more.
Background/Objectives: Artificial intelligence (AI), particularly natural language processing (NLP) models such as ChatGPT, presents novel opportunities for patient education and informed consent. This study evaluated ChatGPT’s use as a support tool for informed consent before penile prosthesis implantation (PPI) in patients with erectile dysfunction (ED) following radical prostatectomy. Methods: ChatGPT-4 answered 20 frequently asked questions across four categories: ED and treatment, PPI surgery, complications, and postoperative care. Three senior urologists independently rated information quality using the DISCERN instrument on a Likert scale ranging from 1 (poor quality) to 5 (good quality). Readability was assessed using the Flesch Reading Ease (FRE) and Flesch–Kincaid Grade Level (FKGL) formulas, and inter-rater reliability was measured using intraclass correlation coefficients. Results: The inter-rater reliability coefficient was 0.76 (95% CI 0.71–0.80). Mean DISCERN scores indicated moderate quality: 2.79 ± 0.92 for ED and treatment, 2.57 ± 0.98 for surgery, 2.65 ± 0.86 for complications, and 2.74 ± 0.90 for postoperative care. High scores (>4) were achieved for clarity and relevance, while complex issues, such as risks and alternative treatments, scored the lowest (<2). The FRE scores ranged from 9.8 to 28.39, and FKGL scores ranged from 14.04 to 17.41, indicating complex readability suitable for college-level comprehension. Conclusions: ChatGPT currently provides variable and often inadequate quality information without sufficient comprehensibility for informed patient decisions, indicating the need for further improvements in quality and readability. Full article
(This article belongs to the Special Issue Clinical Advances in Artificial Intelligence in Urology)
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Review

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16 pages, 462 KiB  
Review
Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma
by Mladen Doykov, Stanislav Valkanov, Usman Khalid, Jasmin Gurung, Gancho Kostov, Bozhidar Hristov, Petar Uchikov, Maria Kraeva, Krasimir Kraev, Daniel Doykov, Katya Doykova, Siyana Valova, Lyubomir Chervenkov and Stefan Konsulov
J. Clin. Med. 2025, 14(7), 2272; https://doi.org/10.3390/jcm14072272 - 26 Mar 2025
Viewed by 428
Abstract
Background: Advancements in artificial intelligence (AI) diagnostics for renal cell carcinoma (RCC) provide valuable information for classification and subtyping, which improve treatment options and patient care. RCC diagnoses are most commonly incidental due to a lack of specific characterizations of subtypes, often leading [...] Read more.
Background: Advancements in artificial intelligence (AI) diagnostics for renal cell carcinoma (RCC) provide valuable information for classification and subtyping, which improve treatment options and patient care. RCC diagnoses are most commonly incidental due to a lack of specific characterizations of subtypes, often leading to overtreatment. Accurate diagnosis also allows for personalized patient management. Different diagnostic methods, such as histopathology, multi-omics, imaging, and perioperative diagnostics, show a lot of promise for AI. Objective: This literature review focuses on developments in RCC diagnostics and their outcomes, efficacy, and accuracy in classification. Method: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of RCC. The PubMed and Google Scholar databases were reviewed to extract relevant information. The literature shows that AI can help distinguish RCC from other kidney lesions and track tumor growth. The integration of radiomic features with clinical metadata further enhances the results. This enables clinicians to implement personalized treatment plans. The application of artificial intelligence in perioperative diagnostics enhances decision-making, improves patient safety, mitigates intraoperative complications, and accelerates recovery. Alongside the advancements in AI-assisted diagnostics, there are problems that need to be addressed, including selection bias, demand for larger and diverse datasets, and reliable validation. Conclusions: Despite the challenges, using AI to help with RCC diagnosis could lead to better patient outcomes, a new standard of care for RCC patients, and more personalized cancer management for each patient. Full article
(This article belongs to the Special Issue Clinical Advances in Artificial Intelligence in Urology)
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