International journal of surgery (London, England) · 2025
Mapping the application landscape of artificial intelligence in prostate cancer: a global bibliometric analysis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wei Yuang, Mei Zubing, Xie Chuang, Yuan Fuwen, Xu Dongliang
Аннотация
BACKGROUND: Artificial intelligence (AI) is transforming medical research, with its impact in neural networks, clinical imaging and computational biology. Prostate cancer (PCa), a leading malignancy in men, benefits from AI's capabilities in enhancing diagnostic precision and personalizing treatments, addressing challenges in disease complexity and clinical management.
METHODS: This bibliometric study analyzed 2581 publications from the Web of Science Core Collection (2014-2024) using CiteSpace (V.6.3.1). A refined search strategy targeted AI-related terms and PCa, with data processed for coauthorship, keyword co-occurrence, and co-citation analyses to map the intellectual landscape and research trends. The innovative year-by-year perspective was applied to display the research trajectory and trend within the domain.
RESULTS: AI-PCa research grew exponentially particularly post-2020. The United States and China led in publication output, with key journals in radiology and oncology dominating. Influential authors like Baris Turkbey and Geert Litjens drove interdisciplinary advancements. Research shifted from traditional machine learning to deep learning, focusing on digital pathology and PI-RADS for improved diagnostics.
CONCLUSION: This study highlights the transformative role of AI in PCa, revealing rapid research growth and a shift toward advanced diagnostic tools. These insights provide a roadmap for future AI-driven innovations, promising enhanced precision in PCa management and improved patient outcomes.
METHODS: This bibliometric study analyzed 2581 publications from the Web of Science Core Collection (2014-2024) using CiteSpace (V.6.3.1). A refined search strategy targeted AI-related terms and PCa, with data processed for coauthorship, keyword co-occurrence, and co-citation analyses to map the intellectual landscape and research trends. The innovative year-by-year perspective was applied to display the research trajectory and trend within the domain.
RESULTS: AI-PCa research grew exponentially particularly post-2020. The United States and China led in publication output, with key journals in radiology and oncology dominating. Influential authors like Baris Turkbey and Geert Litjens drove interdisciplinary advancements. Research shifted from traditional machine learning to deep learning, focusing on digital pathology and PI-RADS for improved diagnostics.
CONCLUSION: This study highlights the transformative role of AI in PCa, revealing rapid research growth and a shift toward advanced diagnostic tools. These insights provide a roadmap for future AI-driven innovations, promising enhanced precision in PCa management and improved patient outcomes.