Arnulf Stenzl, Andrew J Armstrong, Andrea Sboner, Jenny Ghith, Lucile Serfass, Christopher S Bland, Bob J A Schijvenaars, Cora N Sternberg
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Publications returned by INSIDE PC and PubMed were given normalized discounted cumulative gain (nDCG) scores by PC experts reflecting ranking and relevance.</p><p><strong>Intervention: </strong>INSIDE PC for AI-based semantic literature analysis.</p><p><strong>Outcome measurements and statistical analysis: </strong>INSIDE PC was evaluated for relevance and accuracy for three test questions on the efficacy of therapeutic sequencing of systemic therapies in PC.</p><p><strong>Results and limitations: </strong>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 <sup>177</sup>Lu-prostate-specific membrane antigen-617), INSIDE PC outperformed PubMed for the top five, ten, and 20 publications (+16, +4, and +5 pps, respectively).</p><p><strong>Conclusions: </strong>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.</p><p><strong>Patient summary: </strong>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. 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引用次数: 0
摘要
背景:确定前列腺癌(PC)的最佳治疗排序策略具有挑战性,基于人工智能(AI)的医学文献分析工具可为其提供帮助:目的:证明 INSIDE PC 可以帮助临床医生查询有关 PC 治疗排序的文献,并为评估基于人工智能的支持平台的输出结果制定之前尚未确立的做法:INSIDE PC 是通过定制来自 Transformers 的 PubMed 双向编码器表征而开发的。利用数据可视化和分析技术对出版物进行相关性排序和汇总。INSIDE PC和PubMed返回的出版物由PC专家给出归一化折现累积增益(nDCG)分数,以反映排名和相关性:INSIDE PC用于基于人工智能的语义文献分析:对 INSIDE PC 的相关性和准确性进行了评估,涉及 PC 系统疗法疗效排序的三个测试问题:在这一初步评估中,INSIDE PC在问题1(新型激素疗法[NHT],然后是NHT)的前五名、前十名和前二十名出版物中的表现优于PubMed(nDCG得分分别为+43、+33和+30个百分点[pps])。对于问题 2(NHT,其次是聚[腺苷二磷酸核糖]聚合酶抑制剂[PARPi]),INSIDE PC 和 PubMed 的表现类似。对于问题 3(NHT 或 PARPi 后加 177Lu-前列腺特异性膜抗原-617),INSIDE PC 在前 5 篇、10 篇和 20 篇出版物中的表现优于 PubMed(分别为 +16、+4 和 +5pps):我们应用 INSIDE PC 制定了评估基于人工智能的文献提取工具性能的标准。INSIDE PC的性能与PubMed不相上下,可以帮助临床医生对PC进行治疗排序。患者总结:对于医生和患者来说,医学文献的检索通常非常困难。在本报告中,我们介绍了 INSIDE PC--一种人工智能(AI)系统,该系统可帮助搜索医学期刊上发表的文章,并在更短的时间内确定晚期前列腺癌的最佳治疗顺序。我们发现,INSIDE PC 与另一种搜索工具 PubMed(一种广泛用于搜索和检索医学期刊上发表的文章的资源)一样有效。我们在 INSIDE PC 上的工作展示了使用人工智能搜索医学期刊发表文章的新方法,以及如何评估这些系统以支持共同决策。
Artificial INtelligence to Support Informed DEcision-making (INSIDE) for Improved Literature Analysis in Oncology.
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.
期刊介绍:
European Urology Focus is a new sister journal to European Urology and an official publication of the European Association of Urology (EAU).
EU Focus will publish original articles, opinion piece editorials and topical reviews on a wide range of urological issues such as oncology, functional urology, reconstructive urology, laparoscopy, robotic surgery, endourology, female urology, andrology, paediatric urology and sexual medicine. The editorial team welcome basic and translational research articles in the field of urological diseases. Authors may be solicited by the Editor directly. All submitted manuscripts will be peer-reviewed by a panel of experts before being considered for publication.