Artificial intelligence is bringing in a new era of medicine.
Algorithms combined with advanced robotics can aid
everything from diagnostic imaging and analysis,
to remote surgical assistance
and even autonomously performed procedures.
The data collected by rehabilitation devices and prosthetics
could also improve individualized recovery in patients.
In the July special issue of Science, experts offered insight
on how these extraordinary advances bring hope
for more consistent and effective treatment in the future.
The future of surgery is likely to include robots
with some ability to work on their own.
Surgical robots are classified by their level of autonomy,
and the degree to which they use algorithms
to make medical decisions.
Level 0 robots have no autonomy
and rely on a human operator to perform surgical procedures.
Level 1 robots make use of AI to provide assistance with procedures,
but still relies on human control.
At Level 2, robots have autonomy over certain tasks.
Repetitive or tedious subtasks within a procedure,
like cutting cancerous tissues,
are assigned by the surgeons to the robot.
Conditional autonomy at Level 3 involves robots
generating a strategy or list of strategies for a task,
but still relies on a human to select or approve the strategy.
The Smart Tissue Autonomous Robot operates at this level,
applying machine learning to generate and execute a plan for suturing.
This is currently the highest level of autonomy possible
with today’s technology, but advances on the horizon
may bring us closer to fully autonomous systems.
Image-guided robotics combine computer vision
with images from cameras, ultrasound, MRI, or CT scans
to identify key anatomy and precisely direct robots to targets.
Early applications of AI in image-guided robots
were focused on steering needles through soft tissues
to reach targets for biopsies.
Now, attention has moved on leveraging AI
to understand images on a higher level
and make more accurate navigational decisions.
Interpreting images on a fine scale and changing course
based on that information could
lead to autonomous ultrasound scanning,
or self-guided maneuvering of devices for endoscopies
and minimally invasive surgeries.
A significant challenge that remains is the high level of
expertise required from radiologists and surgeons
to train the algorithms that control this technology.
Still, robotic systems that can see and interpret their surroundings
are likely to have a more prominent role
in the future of medical care.
Surgical technology already uses robots to assist
in minimally invasive surgeries.
But the rigid components of current surgical robots
limit access to certain areas of the body,
and in some cases can cause tissue injuries.
Researchers have been exploring the potential for soft robotics
made of pliable materials that can stretch, bend,
compress, and shift from soft to rigid.
One notable project was the EU’s STIFF-FLOP project.
STIFF-FLOP developed a soft robotic system from
biocompatible silicone that used advanced machine learning
for its teleoperation.
It remains an open question whether soft robotics
will develop the precision needed for intricate
surgical applications, or whether traditional surgical robots
will acquire some of the properties of soft robotic technology.
Wearable robots could transform the rehabilitation experience
for both patients and health professionals.
Hard mechanical exoskeletons and soft robotic exosuits
already in development can improve patient outcomes
and offer the assistance needed to get back to daily life.
The transformative potential for these wearable robots, however,
comes from the data that they can collect.
The ability to continuously track movement
and adjust robotic assistance based on personal progress
could revolutionize rehabilitation.
But challenges in these systems is in the calibration of devices
that separate signals of recovery from noise in the data.
Sensor placement, day-to-day fit of devices, and regular variability
in how patients' bodies feel and function
are among the many complicating factors in developing generalized
algorithms to allow widespread use of wearable robots.
Artificial intelligence is tightening the relationship
between robotic prostheses and their users.
Machine learning algorithms allow robotic limbs
to sense intended motion through neuromuscular signals,
enabling more seamless control of prosthetic hands
and motorized lower limbs.
This relationship is developing even further with machine vision
designed to sense the surrounding environment.
Prosthetic legs that can see upcoming terrain
can help the user adapt to their environment.
These advances have the potential to restore and enhance
prosthetic users' abilities to complete everyday tasks.
In addition to meeting high standards for safety,
developers will also have to gain users’ trust
in the idea of AI-enabled limbs.
As medical technology continues to develop,
artificial intelligence will play an expanding role
in how we diagnose, treat, and understand the human body.