Development Status and Multilevel Classification Strategy of Medical Robots

Abstract

The combination of artificial intelligence technology and medical science has inspired the emergence of medical robots with novel functions that use new materials and have a neoteric appearance. However, the diversity of medical robots causes confusion regarding their classification. In this paper, we review the concepts pertinent to major classification methods and development status of medical robots. We survey the classification methods according to the appearance, function, and application of medical robots. The difficulties surrounding classification methods that arose are discussed, for example, (1) it is difficult to make a simple distinction among existing types of medical robots; (2) classification is important to provide sufficient applicability to the existing and upcoming medical robots; (3) future medical robots may destroy the stability of the classification framework. To solve these problems, we proposed an innovative multilevel classification strategy for medical robots. According to the main classification method, the medical robots were divided into four major categories—surgical, rehabilitation, medical assistant, and hospital service robots—and personalized classifications for each major category were proposed in secondary classifications. The technologies currently available or in development for surgical robots and rehabilitation robots are discussed with great emphasis. The technical preferences of surgical robots in the different departments and the rehabilitation robots in the variant application scenes are perceived, by which the necessity of further classification of the surgical robots and the rehabilitation robots is shown and the secondary classification strategy for surgical robots and rehabilitation robots is provided. Our results show that the distinctive features of surgical robots and rehabilitation robots can be highlighted and that the communication between professionals in the same and other fields can be improved.

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

“…Early AI programs, notably Newell and Simon's General Problem [12,214] c) AESOP robotic system, developed by Computer Motion Inc. in 1992, received FDA approval in 1994 for laparoscopic optics and voice control. [13] d) ROBODOC, made by Integrated Surgical Systems (ISS, Santa Monica, CA) in 1992, was the first robot used for robot-assisted total hip replacement surgery. [215,216] e) ZEUS robotic system, introduced in 1998, was used for fallopian tube anastomosis and coronary artery grafting.…”

“…AESOP was initially intended to provide stable video imaging and reduce the need for an assistant; however, it was later found necessary to allow minor camera adjustments during surgery. [ 13 ] In 1998, ZEUS was introduced and was equipped with a video monitor and dual handles for instrument control using a 2D interface. This system was utilized for fallopian tube anastomosis in 1998 and coronary artery grafting in 1999.…”

“…[ 214 ] Copyright 2020, Elsevier Inc. c) Reproduced with permission. [ 13 ] Copyright 2021, MDPI. d) Reproduced with permission.…”

“…b) PUMA robotic system, the earliest robot to assist in clinical surgery, was used in neurosurgical biopsies by the Memorial Medical Center in Long Beach, CA, USA [12,214]. c) AESOP robotic system, developed by Computer Motion Inc. in 1992, received FDA approval in 1994 for laparoscopic optics and voice control [13]. d) ROBODOC, made by Integrated Surgical Systems (ISS, Santa Monica, CA) in 1992, was the first robot used for robot-assisted total hip replacement surgery [215,216].…”

“…b) Reproduced with permission [214]. Copyright 2020, Elsevier Inc. c) Reproduced with permission [13]. Copyright 2021, MDPI.…”