Artificial Intelligence in Urology—A Survey of Urology Healthcare Providers
Background/Objectives: Artificial intelligence (AI) has been utilised in urological conditions such as urolithiasis, urogynaecology and uro-oncology. The aim of this study is to examine the attitudes and beliefs about AI technology amongst urology healthcare providers. Methods: A structured online questionnaire, created from a modified Delphi method with a panel of urologists and urology surgical trainees, was delivered through the Urological Asia Association’s annual congress. The questionnaire, with 25 items of mixed type responses (five-point Likert scale, nominal-polytomous and open-ended), acquired data regarding demographics, perception and attitudes towards general usage of AI in urological care. Results: A total of 464 respondents from 47 different countries were collected. The results showed that 83.4% of participants believed AI will improve efficiency and 18.8% believed they are knowledgeable in AI technology, with ordinal logistic regression showing both urology specialists and trainees are more likely to agree to these responses. Overall, 51.5% believed AI adoption will not replace clinical practice, and regression analysis found those with previous AI training are more likely to agree to this response. We found AI is commonly used in research, patient education and administrative tasks and identified key enablers as regulatory approval, AI clinical effectiveness and access to AI training. Conclusions: Overall attitudes and beliefs towards the use of AI in urology is positive and encouraging. AI training and education and regulatory reform needs to be addressed to allow integration of AI into clinical practice. A limitation of the study lies in its generalisability to global settings due to the demographics of the respondents.
Authors
- Agus Rizal Ardy Hariandy Hamid (ORCID: https://orcid.org/0000-0002-7538-2811)
- Yam-Ting Ho (ORCID: https://orcid.org/0009-0006-2812-1818)
- Tauheed Fareed
- Nahid Rahman Zico
- Isaac Thangasamy (ORCID: https://orcid.org/0000-0003-1820-6114)
- Subrata Deb (ORCID: https://orcid.org/0000-0003-3836-7820)
- Jeremy Yuen‐Chun Teoh (ORCID: https://orcid.org/0000-0002-9361-2342)
- Sandra Elmer (ORCID: https://orcid.org/0000-0002-7308-9451)
- Mohammad Shoaib (ORCID: https://orcid.org/0000-0002-9571-7295)
- Rizal Rian Dhalas
- A. H. M. Imrul Tareq
- Muhammad Zohair (ORCID: https://orcid.org/0009-0002-1853-5057)
Institutions
- The University of Sydney (AU)
- The Royal Melbourne Hospital (AU)
- Khyber Medical College (PK)
- Chinese University of Hong Kong (HK)
- Universitas Airlangga (ID)
- Sylhet MAG Osmani Medical College (BD)
- Combined Military Hospital (PK)
- Hayatabad Medical Complex (PK)
- Nepean Hospital (AU)
- University of Indonesia (ID)
- Prince of Wales Hospital (CN)
- Institute of Management Sciences Peshawar (PK)
- Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo (ID)
- Medical University of Vienna (AT)
- Dhaka Medical College and Hospital (BD)
Publication Details
- Journal
- Société Internationale d’Urologie Journal
- Published
- 2025-08-12
- DOI
- https://doi.org/10.3390/siuj6040053
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65