Current Opinion in Urology最新文献

Integration of novel artificial intelligence tools in pediatric urologic practice.

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-03-10 DOI: 10.1097/MOU.0000000000001275

Lauren M McGee, Elizabeth Soo, Casey A Seideman

{"title":"Integration of novel artificial intelligence tools in pediatric urologic practice.","authors":"Lauren M McGee, Elizabeth Soo, Casey A Seideman","doi":"10.1097/MOU.0000000000001275","DOIUrl":"10.1097/MOU.0000000000001275","url":null,"abstract":"Purpose of review: There has been an explosion of creative uses of artificial intelligence (AI) in healthcare, with AI being touted as a solution for many problems facing the healthcare system. This review focuses on tools currently available to pediatric urologists, previews up-and-coming technologies, and highlights the latest studies investigating benefits and limitations of AI in practice.Recent findings: Imaging-driven AI software and clinical prediction tools are two of the more exciting applications of AI for pediatric urologists. As nuanced pattern recognition improves in trained computer models, pediatric urologists will be able to better counsel and risk stratify patients with chronic diseases and surgical needs. AI is also being extensively used in product development for enuresis treatment. Large language models such as ChatGPT continue to be of strong interest as a patient-facing education tool, but it lacks the accuracy needed to serve as a suitable alternative to human response.Summary: AI is increasingly investigated for use across healthcare fields, including pediatric urology. Use of AI and machine learning (ML) is being explored for patient interface, imaging assessment, outcomes prediction, and product development. Though still in preclinical stages for most systems, ML presents as a promising new clinical tool with potential to shape healthcare systems and medical practice.","PeriodicalId":11093,"journal":{"name":"Current Opinion in Urology","volume":" ","pages":"230-235"},"PeriodicalIF":2.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large language models for automating clinical trial matching.

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-03-20 DOI: 10.1097/MOU.0000000000001281

Ethan Layne, Claire Olivas, Jacob Hershenhouse, Conner Ganjavi, Francesco Cei, Inderbir Gill, Giovanni E Cacciamani

{"title":"Large language models for automating clinical trial matching.","authors":"Ethan Layne, Claire Olivas, Jacob Hershenhouse, Conner Ganjavi, Francesco Cei, Inderbir Gill, Giovanni E Cacciamani","doi":"10.1097/MOU.0000000000001281","DOIUrl":"10.1097/MOU.0000000000001281","url":null,"abstract":"Purpose of review: The uses of generative artificial intelligence (GAI) technologies in medicine are expanding, with the use of large language models (LLMs) for matching patients to clinical trials of particular interest. This review provides an overview of the current ability of leveraging LLMs for clinical trial matching.Recent findings: This review article examines recent studies assessing the performance of LLMs in oncologic clinical trial matching. The research in this area has shown promising results when testing these system using artificially created datasets. In general, they looked at how LLMs can be used to match patient health records with clinical trial eligibility criteria. There is still a need for human oversight of the systems in their current state.Summary: Automated clinical trial matching can improve patient access and autonomy, reduce provider workload, and increase trial enrollment. However, it may potentially create a feeling of \"false hope\" for patients, can be difficult to navigate, and still requires human oversight. Providers may face a learning curve, while institutions must address data privacy concerns and ensure seamless EMR/EHR integration. Given this, additional studies are needed to ensure safety and efficacy of LLM-based clinical trial matching in oncology.","PeriodicalId":11093,"journal":{"name":"Current Opinion in Urology","volume":" ","pages":"250-258"},"PeriodicalIF":2.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ethical aspects of artificial intelligence: what urologists need to know.

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-03-20 DOI: 10.1097/MOU.0000000000001278

Rounak Verma, Findlay Macaskill, Anna Kim, Nicholas Raison, Prokar Dasgupta

{"title":"Ethical aspects of artificial intelligence: what urologists need to know.","authors":"Rounak Verma, Findlay Macaskill, Anna Kim, Nicholas Raison, Prokar Dasgupta","doi":"10.1097/MOU.0000000000001278","DOIUrl":"10.1097/MOU.0000000000001278","url":null,"abstract":"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.","PeriodicalId":11093,"journal":{"name":"Current Opinion in Urology","volume":" ","pages":"224-229"},"PeriodicalIF":2.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative artificial intelligence powered chatbots in urology. 泌尿科人工智能生成聊天机器人。

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-03-19 DOI: 10.1097/MOU.0000000000001280

Zohaib Khawaja, Mohammed Zain Ulabedin Adhoni, Kevin Gerard Byrnes

{"title":"Generative artificial intelligence powered chatbots in urology.","authors":"Zohaib Khawaja, Mohammed Zain Ulabedin Adhoni, Kevin Gerard Byrnes","doi":"10.1097/MOU.0000000000001280","DOIUrl":"10.1097/MOU.0000000000001280","url":null,"abstract":"Purpose of review: The integration of artificial intelligence (AI) into healthcare has significantly impacted the way healthcare is delivered, particularly with generative AI-powered chatbots. This review aims to provide an analysis of the application, benefits, challenges and future of generative AI-powered chatbots in Urology.Recent findings: Recent advancements in AI have led to significant improvements in chatbot performance and applicability in healthcare. Generative AI chatbots have shown promise in patient education, symptom assessment, administrative tasks, and clinical decision-making in urology. Studies demonstrate their ability to reduce clinic burden, improve patient satisfaction, and enhance accessibility. However, concerns remain about accuracy, data privacy, and integration into clinical workflows.Summary: Increasing number of studies have shown the ability of generative AI to enhance urological practice. As technology advances, generative AI is likely to integrate into multiple aspects of urological practice. Concerns with generative AI will need to be examined before safe implementation.","PeriodicalId":11093,"journal":{"name":"Current Opinion in Urology","volume":" ","pages":"243-249"},"PeriodicalIF":2.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The advance of artificial intelligence in outpatient urology: current applications and future directions.

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-03-20 DOI: 10.1097/MOU.0000000000001282

Jacob S Hershenhouse, Karanvir S Gill, Jamal Nabhani

{"title":"The advance of artificial intelligence in outpatient urology: current applications and future directions.","authors":"Jacob S Hershenhouse, Karanvir S Gill, Jamal Nabhani","doi":"10.1097/MOU.0000000000001282","DOIUrl":"10.1097/MOU.0000000000001282","url":null,"abstract":"Purpose of review: Prudent integration of artificial intelligence (AI) into outpatient urology has already begun to revolutionize clinical workflows, improve administrative efficiency, and automate mundane and laborious tasks in the clinic setting.Recent findings: This narrative review explores the current applications of AI in outpatient settings, focusing on previsit, during-visit, and postvisit processes that may improve the experiences of clinicians and patients. We discuss the use of AI in administrative tasks, clinical decision support, documentation, and patient communication. Additionally, we highlight future directions for AI in urology, including integrated solutions that span prediagnosis to posttreatment and disease surveillance. While AI shows promise in reducing physician burden and increasing efficiency, challenges remain.Summary: Taking lessons from the introduction of the electronic health record (EHR), end-to-end AI integration will require rigorous validation, workflow adaptation, and iterant tailoring to meet the demands of the clinic setting before widespread adoption can occur.","PeriodicalId":11093,"journal":{"name":"Current Opinion in Urology","volume":" ","pages":"214-218"},"PeriodicalIF":2.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Patient-reported outcomes in genital gender-affirming surgery. 生殖器性别确认手术中患者报告的结果。

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-01-29 DOI: 10.1097/MOU.0000000000001262

Nkiruka Odeluga, Robert Fisch, Tenny R Zhang, Nnenaya Mmonu

引用次数: 0

Man and machine: exploring the intersection of artificial intelligence and men's health. 人与机器：探索人工智能与男性健康的交叉点。

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-02-27 DOI: 10.1097/MOU.0000000000001274

Evan J Panken, Akash U Patel, Josh Schammel, Justin M Dubin

引用次数: 0

Editorial introduction.

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-04-03 DOI: 10.1097/MOU.0000000000001277

引用次数: 0

Artificial intelligence and patient education.

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-02-12 DOI: 10.1097/MOU.0000000000001267

Olivia Paluszek, Stacy Loeb

引用次数: 0

Generative artificial intelligence in oncology.

IF 2.1 3区医学

Current Opinion in Urology Pub Date : 2025-05-01 Epub Date: 2025-02-28 DOI: 10.1097/MOU.0000000000001272

Conner Ganjavi, Sam Melamed, Brett Biedermann, Michael B Eppler, Severin Rodler, Ethan Layne, Francesco Cei, Inderbir Gill, Giovanni E Cacciamani

{"title":"Generative artificial intelligence in oncology.","authors":"Conner Ganjavi, Sam Melamed, Brett Biedermann, Michael B Eppler, Severin Rodler, Ethan Layne, Francesco Cei, Inderbir Gill, Giovanni E Cacciamani","doi":"10.1097/MOU.0000000000001272","DOIUrl":"10.1097/MOU.0000000000001272","url":null,"abstract":"Purpose of review: By leveraging models such as large language models (LLMs) and generative computer vision tools, generative artificial intelligence (GAI) is reshaping cancer research and oncologic practice from diagnosis to treatment to follow-up. This timely review provides a comprehensive overview of the current applications and future potential of GAI in oncology, including in urologic malignancies.Recent findings: GAI has demonstrated significant potential in improving cancer diagnosis by integrating multimodal data, improving diagnostic workflows, and assisting in imaging interpretation. In treatment, GAI shows promise in aligning clinical decisions with guidelines, optimizing systemic therapy choices, and aiding patient education. Posttreatment, GAI applications include streamlining administrative tasks, improving follow-up care, and monitoring adverse events. In urologic oncology, GAI shows promise in image analysis, clinical data extraction, and outcomes research. Future developments in GAI could stimulate oncologic discovery, improve clinical efficiency, and enhance the patient-physician relationship.Summary: Integration of GAI into oncology has shown some ability to enhance diagnostic accuracy, optimize treatment decisions, and improve clinical efficiency, ultimately strengthening the patient-physician relationship. Despite these advancements, the inherent stochasticity of GAI's performance necessitates human oversight, more specialized models, proper physician training, and robust guidelines to ensure its well tolerated and effective integration into oncologic practice.","PeriodicalId":11093,"journal":{"name":"Current Opinion in Urology","volume":" ","pages":"205-213"},"PeriodicalIF":2.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143536803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0