AI in Prostate Cancer Screening & Diagnosis: A Registry-Based Study of ClinicalTrials.gov Trials.
Introduction: Artificial intelligence (AI) is increasingly applied in prostate cancer screening and diagnostic evaluation; however, the structure, methodological characteristics, and clinical positioning of AI-focused trials remain incompletely characterized. This study aimed to map the clinical tri...
| Publicado en: | Inquiry (00469580) Vol. 63; pp. 1 - 15 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
6/16/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194639611&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194639611 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 6/16/2026 vid: 63 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194639611 194639611 194639611 10.1177/00469580261457387 194639611 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AI in Prostate Cancer Screening & Diagnosis: A Registry-Based Study of ClinicalTrials.gov Trials. aug: au: Hareeri, Rawan H. Aldurdunji, Mohammed M. affil: Department of Pharmacology and Toxicology, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia sug: subj: Prostatic Neoplasms Diagnosis Cancer Screening Methods Artificial Intelligence Utilization Clinical Trials Medical Practice, Evidence-Based Human Male Magnetic Resonance Imaging Positron-Emission Tomography Prostate Ultrasonography Tomography, X-Ray Computed Descriptive Statistics Funding Source Male ab: Introduction: Artificial intelligence (AI) is increasingly applied in prostate cancer screening and diagnostic evaluation; however, the structure, methodological characteristics, and clinical positioning of AI-focused trials remain incompletely characterized. This study aimed to map the clinical trial landscape of AI applications in prostate cancer diagnosis using registry-based evidence mapping. Methods: A registry-based evidence-mapping analysis was conducted using ClinicalTrials.gov. Trials registered up to 15 November 2025 were systematically identified using search terms related to prostate cancer and AI-based methodologies. Eligible studies included interventional and observational trials evaluating AI applications for diagnostic purposes. Data were extracted on study design, diagnostic modality, functional role of AI, comparator framework, and validation strategy. Descriptive statistics and cross-tabulation analyses were used to characterize patterns across studies. The study selection process was presented using a PRISMA-style flow diagram. Results: A total of 84 trials met the inclusion criteria. Imaging-based AI applications predominated, accounting for 52.4% of studies, with magnetic resonance imaging (MRI) representing the most frequently investigated modality (34.5%). Biomarker-based (16.7%), multimodal (15.5%), and computational pathology (7.1%) approaches were less frequently reported. The most common functional applications were classification and risk prediction (48.8%) and lesion detection and segmentation (29.8%). Most studies employed prospective observational designs (84.5%) and frequently relied on stand-alone AI evaluation frameworks (39.2%). Histopathology or biopsy confirmation was the most commonly reported reference standard (56.0%). Only a limited number of trials incorporated workflow integration or clinical decision-support evaluation. Conclusion: AI research in prostate cancer diagnostics appears to be primarily centered on imaging-based, early-phase, and performance-oriented studies. Current evidence suggests that AI systems are predominantly positioned as decision-support tools rather than fully integrated clinical solutions. Greater emphasis on multicenter validation, standardized reporting, and clinically relevant outcome evaluation may be required to support broader clinical implementation. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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