Aug 2026· Japanese Journal of Radiology· 0 citations· 106 references
Medicine
TL;DR
The evolving role of nuclear medicine physicians and radiologists as integrators of AI-derived biomarkers are highlighted, who validate AI outputs for high-stakes decisions, retain interpretive authority for complex cases, and oversee quality assurance, ensuring that AI augments rather than replaces specialist expertise.
The aim of this review was to summarize current evidence on AI-based imaging analysis, radiomics, and pathomics for PCa detection, characterization, risk stratification, and pathological assessment, while highlighting the methodological challenges that currently limit clinical implementation.
R. Rahota, Andrei-Vlad Bădulescu, B. Buhas et al.· Journal of Clinical Medicine· 0 citations
This review summarizes the deep-learning architectures, fusion strategies, representative applications, and implementation challenges of mpMRI-centered multimodal AI.
M. Fujiwara, Soichiro Yoshida, Imon Banerjee et al.· Abdominal Radiology· 0 citations
Improving access to high-quality PSMA PET should be regarded not only as a logistical objective but also as a clinical priority to ensure that advances in molecular imaging translate into more precise, timely, and equitable PCa care.
A. Peyrottes, L. Turpin, L. F. Hernández et al.· European Urology Oncology· 2 citations
The future of AI in radiation oncology is unlikely to be the replacement of the radiation oncologist, but rather a human–AI partnership in which automation supports clinical expertise, and the need to define responsibility when AI-generated outputs are used in patient care is defined.
S. Ichou, K. Nouni, A. Lachgar et al.· MedPeer publisher· 0 citations
The most adoption-ready directions for Abdominal Radiology readers are modular multimodal systems that improve triage, guide biopsy targeting, and quantify local extension risk with transparent validation pathways and human-centered deployment design.
Current applications and technical advances of AI in gastric cancer imaging are reviewed, the key barriers to clinical implementation are analyzed, and practical strategies from four perspectives are proposed: data governance, technological innovation, clinical translation, and physician-AI collaboration.
K. Shen, W.-J. Chen, X.-D. Cheng· Zhonghua wei chang wai ke za...· 0 citations
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