Artificial intelligence is spreading throughout healthcare, bringing with it many benefits. Artificial intelligence has an impact on, among other things, surgery. For example, during pre-operative planning, AI processes enormous amounts of patient data to create personalized treatment plans, going beyond a one-size-fits-all approach.
AI-powered intraoperative guidance supports precision medicine by combining patient-specific preoperative data with real-time insights gained during surgery. This approach allows surgeons to tailor techniques to each patient’s unique anatomy, accommodating for anatomical changes, identifying the safest surgical paths, and minimizing risk.
Gabriel Jones is the CEO of Proprio, a surgical technology company that has developed Paradigm, a navigation platform that integrates technology from artificial intelligence, machine learning, featherlight field and depth sensors to provide a real-time, three-dimensional view of the anatomy and surgical site.
Here, Jones talks about artificial intelligence in preoperative planning and intraoperative guidance, as well as artificial intelligence in visualization and digital twins and predictive analytics.
Question: You suggest that AI can process enormous amounts of patient data before surgery to create personalized treatment plans, going beyond a one-size-fits-all approach. Please explain what this looks like.
AND. Understanding elaborate anatomical relationships is imperative to effectively plan and perform surgery. Conventional approaches often relied on stagnant imaging, which provided only a snapshot of the patient’s anatomy.
However, thanks to emerging technology, we are able to digitize the entire surgical field. Using advanced artificial intelligence and machine learning, we can create energetic, real-time visualizations of a patient’s unique anatomy.
Imagine a scenario where a surgeon could interact with a fully digital model of the patient’s anatomy before surgery. This model incorporates data from various sources – such as MRI, CT scan and patient history – to create a 3D representation that can be manipulated in real time. This allows surgeons to assess the location of what they are looking for, but also its relationship to the surrounding anatomy, enabling highly tailored surgical plans.
This approach moves us away from a one-size-fits-all way of thinking because it integrates each patient’s individual data. Surgeons can simulate various surgical techniques and visualize potential outcomes based on the specific anatomical differences that occur. The result is a personalized treatment plan that increases precision and ultimately improves patient safety and outcomes.
Q. Where does AI-powered intraoperative guidance come from and how can it support precision medicine?
AND. Traditionally, surgeons have relied on preoperative imaging and surrogate markers to guide procedures. While these tools have served us well, they can quickly become obsolete if the patient moves or the reference points shift during surgery. This may result in surgeons having to work with inexact information, which poses significant risks.
AI-powered intraoperative guidance changes this situation by combining preoperative imaging data with AI-powered featherlight field and depth sensor technologies. This integration enables the delivery of real-time anatomical visualizations, constantly adapted to the current position of the patient.
For example, if a patient’s body shifts during surgery, the system can instantly adjust visual data to provide correct, real-time guidance throughout the procedure. This combination of artificial intelligence and imaging technologies streamlines workflow, reduces unnecessary radiation exposure, and improves overall surgical guidance.
The result is significant improvement in surgical precision and safety for both patients and surgical teams. Surgeons can be assured that they are receiving the right data at the right time and that it reflects the patient’s anatomy, allowing them to better accommodate different levels of skill and experience while reducing the likelihood of errors.
Q. Please explain visualization and digital twins and discuss how artificial intelligence can generate 3D models of the surgical field in real time and what this makes possible.
AND. Visualization and digital anatomical twins are powerful tools to improve surgical accuracy, safety and outcomes. A digital twin is a virtual replica of a patient’s anatomy that simulates and predicts real-world processes in real time.
By creating a digital twin, surgeons can virtually explore different surgical scenarios, test different approaches, and predict outcomes before making decisions in the operating room. This feature enables treatment planning precisely tailored to each patient’s unique anatomy and specific circumstances.
Thanks to the integration of featherlight field technology and depth sensors with artificial intelligence, we can generate 3D models of the operating field in real time – i.e. digital twins. This allows surgeons to see under structures, in corners and across planes often hidden using time-honored imaging techniques.
With up-to-date surgical tools, surgeons can visualize critical structures such as nerves and blood vessels in three dimensions, increasing their ability to navigate elaborate anatomy.
This unprecedented level of visibility enables not only better planning, but also more informed decisions during operations. Surgeons can adjust their approach at any time, significantly improving accuracy and minimizing the risks associated with hidden anatomical complexities.
Q. You say that predictive analytics can operate data from previous procedures to predict patient outcomes. How does this assist surgeons and strengthen precision medicine?
AND. In surgery, knowledge is power. The more we understand from past procedures, the better we will be able to gain knowledge about future surgical practices. By capturing and analyzing surgical data from all cases, we can determine optimal outcomes and learn lessons from even the most basic or elaborate procedures.
Predictive analytics plays a key role in this process by examining patterns and outcomes of similar cases combined with individual patient factors. Using AI algorithms, we are able to identify potential risks and complications based on historical data.
This means that before the surgeon even enters the operating room, he or she has insight into the best treatments for each patient. By taking into account individual patient characteristics – such as differences in anatomy, prior medical history and specific risks – surgeons can make highly data-driven decisions that optimize care.
This level of personalized planning improves patient outcomes while minimizing the risk of adverse events. As predictive analytics capabilities continue to improve, the goal is to enable surgeons to operate this data for continuous improvement, ultimately improving the standard of care across the board.