ReviewDiscover nano2026
From imaging enhancement to surgical autonomy: the evolutionary role of nanotechnology in AI-driven robotic surgery.
Review in Discover nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Robot-assisted minimally invasive surgery provides stable visualisation and manipulation, but direct access to local tissue state remains limited. Lesion boundaries, mechanics, molecular activity and microenvironmental changes are often inferred from conventional feedback. This review evaluates how nano-enabled imaging, flexible and local sensing, and micro/nanorobotic intervention may extend signals and localised actions in robot-assisted procedures. We organise the evidence along an information-flow framework: signal acquisition, processing or computational interpretation, task-relevant state estimation, conversion into a decision variable or control-relevant input, and feedback-guided robotic action. Processing may involve calibration, signal processing, computational modelling, conventional machine learning, deep learning or artificial intelligence (AI)-based methods. The evidence reviewed is strongest for localised signal acquisition and supervised intervention. Selected forms of computational interpretation have been demonstrated, and localisation- or motion-derived geometric states enter feedback controllers in some systems. By contrast, biochemical, spectral, tactile and physiological states are usually displayed, analysed offline or returned through human-facing feedback; they rarely modify an online surgical controller. Closed-loop navigation in phantoms, surrogate-device studies and constrained navigation autonomy therefore represent partial control capabilities, not clinically relevant surgical autonomy. The principal missing transitions are robust validation of task-relevant tissue states, uncertainty-aware conversion of those states into control inputs, and safe action within realistic surgical workflows. Near-term clinical use is more likely to centre on bounded nano-enabled sensing or intervention modules embedded within supervised procedures. Progress towards bounded task autonomy will require reproducibility, material safety, workflow compatibility, explicit human supervision and prospective evidence of patient and task benefits.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.