AI-enhanced robotic hands are rapidly reshaping tumour surgery by merging real-time sensing, precision mechanics, and intelligent decision support, yet current systems still struggle with early lesion detection, limited tactile sensitivity, and inconsistent accuracy across cancer types. This review addresses these gaps by examining how next-generation robotic hands, empowered by multimodal AI, augmented imaging, hybrid guidance, and minimally invasive mechatronics, can improve early tumour localization and safer resections. The study synthesizes insights from urologic, breast, colorectal, gastric, thoracic, and gynecologic oncology to highlight shared trends such as the shift toward personalized robotics, smart biopsy tools, light-mediated theranostics, flexible platforms, and real-time intraoperative analytics. A comparative reading of quantitative and qualitative evidence reveals strong gains in surgical precision and patient outcomes, yet also contradictions regarding cost-effectiveness, reproducibility of AI predictions, and disparity in adoption between high- and low-resource settings. Using a narrative review approach, key findings point to robotic hands with enhanced tactile sensors and AI-driven micro-maneuvering as promising breakthroughs for detecting microtumours, reducing positive margins, and guiding on-table diagnostics. Recommendations emphasize stronger clinical validation, interoperable imaging ecosystems, and ethical design. The implications extend to safer surgeries, shorter recovery, and more equitable cancer care. Limitations include heterogeneous study designs and early-stage prototypes. Future research should explore adaptive learning models, haptic-guided autonomy, and broader trials. Overall, AI-enhanced robotic hands signal a transformative pathway for earlier detection and more precise tumour removal.
Ng et al. (2026) studied this question.