ABSTRACT
BACKGROUND: Defining optimal therapeutic sequencing strategies in prostate cancer (PC) is challenging and may be assisted by artificial intelligence (AI)-based tools for an analysis of the medical literature. OBJECTIVE: To demonstrate that INSIDE PC can help clinicians query the literature on therapeutic sequencing in PC and to develop previously unestablished practices for evaluating the outputs of AI-based support platforms. DESIGN, SETTING, AND PARTICIPANTS: INSIDE PC was developed by customizing PubMed Bidirectional Encoder Representations from Transformers. Publications were ranked and aggregated for relevance using data visualization and analytics. Publications returned by INSIDE PC and PubMed were given normalized discounted cumulative gain (nDCG) scores by PC experts reflecting ranking and relevance. INTERVENTION: INSIDE PC for AI-based semantic literature analysis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: INSIDE PC was evaluated for relevance and accuracy for three test questions on the efficacy of therapeutic sequencing of systemic therapies in PC. RESULTS AND LIMITATIONS: In this initial evaluation, INSIDE PC outperformed PubMed for question 1 (novel hormonal therapy [NHT] followed by NHT) for the top five, ten, and 20 publications (nDCG score, +43, +33, and +30 percentage points [pps], respectively). For question 2 (NHT followed by poly [adenosine diphosphate ribose] polymerase inhibitors [PARPi]), INSIDE PC and PubMed performed similarly. For question 3 (NHT or PARPi followed by 177Lu-prostate-specific membrane antigen-617), INSIDE PC outperformed PubMed for the top five, ten, and 20 publications (+16, +4, and +5 pps, respectively). CONCLUSIONS: We applied INSIDE PC to develop standards for evaluating the performance of AI-based tools for literature extraction. INSIDE PC performed competitively with PubMed and can assist clinicians with therapeutic sequencing in PC. PATIENT SUMMARY: The medical literature is often very difficult for doctors and patients to search. In this report, we describe INSIDE PC-an artificial intelligence (AI) system created to help search articles published in medical journals and determine the best order of treatments for advanced prostate cancer in a much better time frame. We found that INSIDE PC works as well as another search tool, PubMed, a widely used resource for searching and retrieving articles published in medical journals. Our work with INSIDE PC shows new ways in which AI can be used to search published articles in medical journals and how these systems might be evaluated to support shared decision-making.
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.
