Autonomous AI in prostate cancer: the road ahead towards clinical implementation.

Artificial intelligence in prostate cancer diagnosis and management: state-of-the-art approaches and emerging innovations.

Artificial Intelligence in Breast Imaging and Screening: Current Evidence, Applications, and Future Directions

Abstract Artificial intelligence (AI) has emerged as a clinically significant and rapidly evolving technology in breast imaging, with applications spanning cancer detection, risk prediction, workflow optimization, and supplemental imaging modalities. The evidence base has matured rapidly, transitioning from retrospective accuracy studies to prospective randomized controlled trials. This narrative review synthesizes the most recent evidence (2023–2026) on AI applications in breast cancer screening and imaging, including landmark trial results, systematic reviews, and society recommendations. Current data demonstrate that AI-supported mammography screening can increase cancer detection rates by 10 to 29% and reduce interval cancer rates by up to 12% while reducing radiologist workload by 31 to 64% in double-reading screening settings, without increasing false-positive rates. However, the evidence base remains predominantly derived from nondiverse, high-income country populations, and long-term outcome data including breast cancer mortality are not yet available. Challenges related to generalizability, algorithmic bias, overdiagnosis, and regulatory frameworks remain. As the field moves toward clinical implementation, rigorous post-market surveillance, diverse dataset validation, and cost-effectiveness analyses will be essential.

Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.

Artificial intelligence (AI) is rapidly integrating into clinical radiology. As primary diagnosticians, radiologists increasingly interpret AI-generated analyses and are expected to oversee the monitoring and governance of deployed AI systems. Although AI literacy among radiologists is improving, several technical aspects of AI remain insufficiently accessible. One such concept is uncertainty quantification (UQ), which estimates the reliability of AI predictions and can signal when outputs should be interpreted with caution. This review introduces key UQ concepts relevant to radiology, distinguishing between aleatoric uncertainty and epistemic uncertainty arising from data variability and knowledge gaps. We summarize commonly used UQ approaches in current research and practice. Furthermore, through a narrative review of selected recent AI imaging studies, we illustrate how UQ methods are applied in practice and highlight methodological trends, findings, and limitations. Although UQ has the potential to improve the safety and interpretability of AI-assisted screening, challenges remain, including calibration, threshold selection, computational cost, and the need for prospective clinical validation.

Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives

This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.

Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging

Artificial intelligence (AI) is rapidly transforming medical imaging, offering unprecedented opportunities to enhance diagnostic accuracy, streamline workflows, and personalize care. However, its integration into pediatric radiology presents unique challenges that threaten to widen existing global health disparities if not addressed thoughtfully. These challenges are related to data inequality and bias in model development, infrastructure disparities, regulatory and ethical gaps, workforce capacity and training gaps, language and localization barriers, costs and commercialization, and sustainability and long-term support issues. This article, written by representatives from the World Federation of Pediatric Imaging (WFPI), will address the key barriers to global validation and implementation of AI in pediatric radiology and how they can be addressed. By linking these domains to practical actions and responsibilities and outlining a time-sequenced roadmap, this paper provides an equity-focused, pediatric-specific framework to guide global implementation.

The role of artificial intelligence in radiology: decision-making, limitations, and human-AI collaboration

Artificial intelligence (AI) has become one of the most actively discussed tools in modern day radiology, promising to help interpret X-rays, CT scans, and MRIs alongside human radiologists. It has matched or exceeded human accuracy outright, particularly in a few narrow tasks. Furthermore, AI has come at a time when imaging volume has grown far faster than the radiology workforce, leading to heavy workloads for radiologists. However, this progress is not without its issues, including AI models that can be deliberately fooled and an inability to explain how a given prediction was reached in the first place. What remains unresolved is whether this radiology-based AI can be reliably explained, trusted, and applied in a workplace setting when limitations such as the risk of human over-reliance and the availability of training data are considered. To address this, this paper examines peer-reviewed studies covering AI's technical validation, real-world clinical deployment, and behavioral evaluation of how radiologists work alongside this tool. The findings show that 1) a convolutional neural network can exceed average radiologist accuracy on a narrow pneumonia- detection task, 2) a structured radiologist-feedback system can significantly reduce a deployed model's false-positive rate, and 3) a study involving radiologists found AI's accuracy, not a radiologist's intelligence or experience, to be the strongest predictor of whether AI will help or hinder the user. Finally, this paper applies the knowledge from the results to a real-world AI versus radiologist comparison of a chest X-ray case, which results in a single page web interface development (https://yashcoder99.github.io/xray) via JavaScript. Together, this study demonstrates the complexity of AI's value in radiology. As AI usage and deployment rates accelerate and new peer-reviewed evidence becomes available every year, we offer an up-to-date, evidence-backed analysis and application for weighing AI's strengths against its somewhat hidden weaknesses.