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Purpose Of Review: The integration of artificial intelligence in urology presents both transformative opportunities and ethical dilemmas. As artificial intelligence driven tools become more prevalent in diagnostics, robotic-assisted surgeries, and patient monitoring, it is crucial for urologists to understand the ethical implications of these technologies. This review examines key ethical concerns surrounding artificial intelligence in urology, including bias, transparency, accountability, and data privacy.
Recent Findings: Recent literature highlights algorithmic bias as a significant challenge, where artificial intelligence models trained on nondiverse datasets may produce inequitable outcomes. The "black-box" nature of artificial intelligence systems complicates transparency and interpretability, raising concerns about clinician and patient trust. Emerging reporting standards, such as STREAM-URO and IDEAL frameworks, and WHO Guidelines provide structured approaches for ethical artificial intelligence integration in urology.
Summary: The ethical deployment of artificial intelligence in urology requires a balanced approach that prioritizes fairness, accountability, and patient autonomy. Clinicians must advocate for explainable artificial intelligence, ensure equitable access, and integrate human oversight into artificial intelligence assisted decision-making. Future research should focus on improving dataset diversity, enhancing artificial intelligence interpretability, and establishing robust ethical guidelines to ensure that artificial intelligence advances align with medical ethics and patient-centered care.
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http://dx.doi.org/10.1097/MOU.0000000000001278 | DOI Listing |
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Community Medicine, Faculty of Health, UiT The Arctic University of Norway, Tromsø, Norway.
Background: The ability to access and evaluate online health information is essential for young adults to manage their physical and mental well-being. With the growing integration of the internet, mobile technology, and social media, young adults (aged 18-30 years) are increasingly turning to digital platforms for health-related content. Despite this trend, there remains a lack of systematic insights into their specific behaviors, preferences, and needs when seeking health information online.
View Article and Find Full Text PDFEmerg Top Life Sci
September 2025
Hurdle.bio / Chronomics Ltd., London, UK.
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure.
View Article and Find Full Text PDFRetina
September 2025
Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 15, CH-3010.
Purpose: To evaluate inter-grader variability in posterior vitreous detachment (PVD) classification in patients with epiretinal membrane (ERM) and macular hole (MH) on spectral-domain optical coherence tomography (SD-OCT) and identify challenges in defining a reliable ground truth for artificial intelligence (AI)-based tools.
Methods: A total of 437 horizontal SD-OCT B-scans were retrospectively selected and independently annotated by six experienced ophthalmologists adopting four categories: 'full PVD', 'partial PVD', 'no PVD', and 'ungradable'. Inter-grader agreement was assessed using pairwise Cohen's kappa scores.
Cuad Bioet
September 2025
Universidad de A Coruña. Facultad de Derecho, Campus de Elviña, s/n, 15071, A Coruña. 981 167000 ext. 1640
The implications of the use of artificial intelligence (AI) in many areas of human existence compels us to reflect on its ethical relevance. This paper addresses the signification of its use in healthcare for patient informed consent. To this end, it first proposes an understanding of AI, as well as the basis for informed consent.
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