ReviewMedComm2026
AI-Driven Revolution of Medical Robotics Across Surgical Innovation, Rehabilitation Intelligence, and Multimodal Healthcare Delivery.
Review in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Modulation of the TGF-β/Smad Signaling Pathway by a Bioactive Oxidized Polygonati Rhizoma Polysaccharide Crosslinked Chitosan Hydrogel as Therapeutic Carrier for Parkinson's Disease Treatment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
- "Biodegradable caterpillar inspired soft robotic systems for targeted therapeutic delivery with intelligentRSC advances · 2026Review
- AI-enabled proactive health education: perceptions of public health students - a qualitative study.BMC medical education · 2026Article
- Exploring Artificial Intelligence in Orthopedic Surgery: A Review of Perception, Decision, and Execution Systems.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is catalyzing a paradigm shift in medical robotics, transforming medical robots from teleoperated tools into intelligent partners across clinical domains. This evolution is pivotal in addressing global challenges like aging populations, driven by core AI pillars-including computer vision (CV), deep reinforcement learning, and large language models (LLMs)-that support perception, decision-making, and naturalistic communication, enabling varying degrees of autonomy and adaptive care. However, the literature still lacks a holistic analysis that integrates these advances and tackles the translational challenges hindering clinical adoption. This review bridges this gap by systematically charting the evolution of AI-driven robotics across intelligent surgery, adaptive rehabilitation, and multimodal healthcare delivery. We dissect the core technologies powering this revolution, from digital twins for surgical simulation to LLMs for enhanced human-robot interaction, and critically analyze the associated technical, ethical, and regulatory hurdles. By synthesizing current progress and outlining future frontiers, including embodied AI, nanorobotics, and the concept of the AI-augmented surgeon, this review provides a comprehensive roadmap for accelerating the translation of intelligent medical robotics into routine clinical practice.
Indexed as
Identifiers
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.