Artificial INtelligence to Support Informed DEcision-making (INSIDE) for Improved Literature Analysis in Oncology.

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.

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.

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.

INSIDE PC for AI-based semantic literature analysis.

INSIDE PC was evaluated for relevance and accuracy for three test questions on the efficacy of therapeutic sequencing of systemic therapies in PC.

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).

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.

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.

European urology focus. 2024 Jun 13 [Epub ahead of print]

Arnulf Stenzl, Andrew J Armstrong, Andrea Sboner, Jenny Ghith, Lucile Serfass, Christopher S Bland, Bob J A Schijvenaars, Cora N Sternberg

Department of Urology, University Hospital Tübingen, Tübingen, Germany. Electronic address: ., Department of Medicine, Duke Cancer Institute Center for Prostate and Urologic Cancer, Duke University, Durham, NC, USA., Department of Pathology and Laboratory Medicine, Englander Institute for Precision Medicine, Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA., Pfizer Inc, Collegeville, PA, USA., Pfizer Oncology, Paris, France., Digital Science, London, UK., Department of Medicine, Englander Institute for Precision Medicine, Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA. Electronic address: .

Go Beyond the Abstract and Read a Commentary by the Authors