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expert systems (such as MYCIN for the recognition of pathogenic bacteria) were developed to solve
medical diagnoses using rules from 50 years ago. For the operation of such a system, all facts and rules, as
well as symptoms and histories of patients’ diseases should be collected from specialists. Then, the software
author needs to enter the data into the computer to conduct “reasoning in accordance with the laws of
logic” and to constantly update the system with new data. These systems do not “scale out” and cannot
succeed because of a high level of complication of a problem (e.g., starting in 1984 project CYC - sum
of all data on human judgments). Only research on the human brain, discoveries explaining the actions
of neurons (e.g., Alan Hodgkin, Andrew Huxlay, Bernard Katz) and attempts to transfer this biological
knowledge to computer science have led to adequate progress. A good example is one of the deep learning
pioneers Terrence Sejnowski, whose achievements can be found in both human brain research and the
creation of an artificial brain on this basis [29-33] . Advances in machine vision were made when the focus was
on the characteristics of the objects rather than on individual pixels. Deep learning is therefore effective
for solving problems of image analysis because its structure and operation was based on the analysis of the
process of vision and image recognition by the human brain. Fortunately, it took much less time than the
evolution for creating our species.

What’s more, all doctors are already looking forward to the possibility of using navigation, similar to
the ones Google has provided in our cars. As we all remember, the development of car navigation was
preceded by creating accurate maps. The diagnostics offered by robots are approaching us soon to achieve
such accuracy that will enable similar outcomes in medicine. The first is orthopedics and neurosurgery for
which we do not require updates in the millisecond time mode. And the first robot tools that used it with
great success were the radiosurgical knives of oncological robots (CyberKnife).

AI and robotics are transforming medical services. We note successes in analyzing medical images
(histopathology) and building a knowledge base. In 2017, a Chinese robot called Xiao Yi, developed by
Tsinghua University passed China’s National Medical Licensing Examination. The robot scored 456 points
[34]
in the test which was well above the passing mark of 360 . OK. So we now have AI with the knowledge of
a medical student, but do we have an experienced doctor?

There are several FDA-approved devices and platforms for robotic surgery and these include the da Vinci
Surgical System, Sensei X Robotic Catheter System, FreeHand 1.2 and invendoscopy E200 system. Also
approved are Flex® Robotic System, Senhance, ARES, the Single-Port Instrument Delivery Extended
Research (SPIDER) and the NeoGuide Colonoscope. Other technology platforms waiting for FDA approval
TM
include MiroSurge, ViaCath System, SPORT Surgical System, SurgiBot, Versius Robotic System, Master
and Slave Transluminal Endoscopic Robot, Verb Surgical, Miniature In Vivo Robot, and the Einstein
[35]
Surgical Robot .
We hope that our robot Robin Heart will soon join this group. Surgical robots are a way to introduce
standardization and reduce invasiveness, while ensuring proper operation safety.

“In the future, robotic surgeons will be more involved in the healthcare requirements of individuals. Robots
require a communication link and applications that connect the robots to their clients or users. These
communication links are usually supported through client/server network connections. Therefore, the
networking system is vulnerable to cyber-attacks and consequently, the security and privacy of the robotic
[35]
platforms is paramount” .
In general, robots will get smarter until they finally become necessary. Despite the problems (temporary, in
the category of the time of development of our civilization), artificial intelligence and robots are part of the
evolution of humanity and medicine.