Edge AI for Real-Time Tumor Margin Assessment: A Systematic Literature Review

Abstract

The growing need for accurate and effective evaluation of tumor margins in cancer surgery has spurred substantial interest in Edge AI, where artificial intelligence and edge computing work together to support immediate decision-making. Conventional approaches frequently experience lags and errors, while Edge AI presents the capability to process tissue samples during surgery with exceptional accuracy and reduced delay. This systematic literature review investigates the present status of Edge AI implementations in real-time tumor margin evaluation, with particular attention to its contribution to advancing diagnostic precision and surgical results. We identify key trends and challenges across four dimensions: real-time cancer diagnosis technologies, intraoperative margin assessment, AI applications in oncology, and emerging innovations. A strict approach was adopted to integrate pertinent research, which guaranteed a thorough examination of technological progress, clinical assessments, and obstacles to deployment. The results indicate Edge AI systems can markedly improve the velocity and dependability of margin detection, yet issues including computational limitations and dataset variability continue to be unaddressed. Multiple investigations show encouraging outcomes in decreasing positive margin rates, but additional verification in varied clinical environments is required. The review highlights the transformative potential of Edge AI in cancer surgery while underscoring the need for interdisciplinary collaboration to address technical and regulatory hurdles. This work synthesizes current understanding, establishing a basis for subsequent investigation and clinical implementation of Edge AI in tumor margin evaluation.

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