It is suggested that AI systems are commonly performance-validated but insufficiently outcome-verified, which contributes to limited adoption and must be bridged through workflow-native design, prospective clinical validation, interoperable digital infrastructure, and alignment of technological innovation with healthcare system readiness.
A narrative review of recent clinical, translational, and health-services literature was conducted, focusing on AI tools deployed in routine or near-term oncology practice, and organizes evidence along the cancer care continuum, from first presentation to outcome prediction, and integrates ethical, legal, and regulator...
Ganesh H. Divekar, Bharat Bhosale· Indian Journal of Medical an...· 0 citations
This commentary describes how FL is evolving into an essential component across all stages of the precision oncology process, including areas such as radiomics, digital pathology, genomics, multi-omics analysis, molecular tumor board decision-making, patient matching in clinical trials, and development of multimodal AI...
Afzal Hussain, Ashfaq Hussain· AI in Precision Oncology· 0 citations
The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.
Lisa Koopmans, Fernando Vega Lara, Christian Roest et al.· Abdominal Radiology· 0 citations
Overall, AI represents a highly promising adjunct to clinical decision-making in liver transplantation; however, robust prospective validation, standardized reporting frameworks, and clinically interpretable implementations remain necessary prior to widespread adoption.
Panagiotis Boutos, James L. Rogers, Efthymia Kouvela et al.· Journal of Surgical Research· 0 citations
The objective of this study is to systematically evaluate the diagnostic performance, predictive capabilities, and clinical readiness of AI-based models in breast cancer imaging, with emphasis on methodological quality, validation strategies, and translational applicability.
