Application of artificial intelligence to overcome clinical information overload in urological cancer. (17th January 2022)

• Record Type: Journal Article
• Authors: Stenzl, Arnulf; Sternberg, Cora N.; Ghith, Jenny; Serfass, Lucile; Schijvenaars, Bob J.A.; Sboner, Andrea
• Is Part Of: BJU international. Volume 130:Number 3(2022)
• Page Start: 291
• Page End: 300
• Publication Date: 2022-01-17
• Subjects: artificial intelligence -- machine learning -- prostate cancer therapy -- literature extraction -- automated; Genitourinary organs -- Diseases -- Periodicals; Genitourinary organs -- Surgery -- Periodicals; Urology -- Periodicals; 616.6
• DOI: 10.1111/bju.15662
• Languages: English
• ISSNs: 1464-4096

Abstract

Abstract : Objective: To describe the use of artificial intelligence (AI) in medical literature and trial data extraction, and its applications in uro‐oncology. This bridging review, which consolidates information from the diverse applications of AI, highlights how AI users can investigate more sophisticated queries than with traditional methods, leading to synthesis of raw data and complex outputs into more actionable and personalised results, particularly in the field of uro‐oncology. Methods: Literature and clinical trial searches were performed in PubMed, Dimensions, Embase and Google (1999–2020). The searches focussed on the use of AI and its various forms to facilitate literature searches, clinical guidelines development, and clinical trial data extraction in uro‐oncology. To illustrate how AI can be applied to address questions about optimising therapeutic decision making and individualising treatment regimens, the Dimensions‐linked information platform was searched for 'prostate cancer' keywords (76 publications were identified; 48 were included). Results: AI offers the promise of transforming raw data and complex outputs into actionable insights. Literature and clinical trial searches can be automated, enabling clinicians to develop and analyse publications expeditiously on complex issues such as therapeutic sequencing and to obtain updates on documents that evolve at the pace and scope of the landscape. An AI‐based platform inclusive of 12 trial databases and >100 scientific literature sources enabled the creation of an interactive visualisation. Conclusion: As the literature and clinical trial landscape continues to grow in complexity and with increasing speed, the ability to pull the right information at the right time from different search engines and resources, while excluding social media bias, becomes more challenging. This review demonstrates that by applying natural language processing and machine learning algorithms, validated and optimised AI leads to a speedier, more personalised, efficient, and focussed search compared with traditional methods.