# AI‐Driven Revolution of Medical Robotics Across Surgical Innovation, Rehabilitation Intelligence, and Multimodal Healthcare Delivery

## TL;DR

AI is transforming medical robotics into intelligent tools for surgery, rehabilitation, and healthcare delivery, but challenges remain in adoption and standardization.

## Contribution

This review provides a comprehensive analysis of AI-driven medical robotics and outlines a roadmap for clinical translation.

## Key findings

- AI technologies like computer vision and LLMs are enabling autonomous and adaptive medical robotics.
- Challenges include high costs, lack of standardization, and trust deficits between humans and machines.
- Future trends include embodied AI, nanorobotics, and AI-augmented surgeons.

## 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.

The figure illustrates the background, applications, limitations, and future trends of medical robotics in the context of global aging and strained medical resources. It highlights issues such as the increasing demand for healthcare due to aging populations, with a focus on applications in surgery, rehabilitation, medical examination, and assistance. Key challenges include high costs, lack of standardization, and human–machine trust deficits. The future trends section emphasizes the role of autonomous systems in decision‐making, machine perception, and the integration of safety measures like algorithmic registration and new materials for improved security and efficiency in medical robotics.