Simulation involves the creation of a virtual environment or a system that imitates its real-world counterpart. Simulations mimic real-world scenarios and can be used to test hypotheses and predict the responses of complex systems and phenomena. AI is being used in surgical simulation to advance surgical training by providing a realistic environment. It also adds to patient safety by letting surgeons devise a customized operation plan. A brief overview of the advantages, disadvantages, future prospects, and examples of AI in surgical simulation is given below.

Advantages of AI in Surgical Simulation

Accuracy:

Machine learning is a subdivision of artificial intelligence. Machine learning algorithms are trained using enormous quantities of data such as medical imaging, patients’ medical history, lab test results, and surgical procedures. All this data can then be utilized to design a more accurate simulation. Thus when AI is employed for producing simulations, the resulting simulations are more realistic and offer a more immersive experience. Furthermore, this data can be used to give more accurate predictions about the diagnosis and treatment options, in both the simulation environment and in real life.

AI virtual patients are built in such a way that they can accurately simulate features of real patients including their physiology and anatomy. In this way, surgeons can try out the elaborate techniques required for a particular operation before performing the surgery on a real patient.

Personalized:

One of the limitations of simulation training is the shortage of experts to assess student performance. However, with the integration of AI in medical simulation, real-time feedback can be given to students. AI can carry out motion-based analysis, thus, each resident’s hand movements can be inspected to determine which areas of the procedure may require improvement. In addition to getting a personalized performance review, AI can be used to tailor lesson plans to each student’s needs. AI can identify which individual is ready for independent practice.

Non-Technical Training:

AI in surgical simulation also allows surgeons to hone their non-technical skills such as decision-making, leadership, teamwork, and communication, both with patients and colleagues. Students can be asked to handle surgical simulations with ethical concerns. This prepares them to handle such scenarios in real-life and teaches them how to make a firm decision. Moreover, AI can help students brush up their non-technical skills by providing feedback and guidance in real-time.

Examples of AI in Surgical Simulation

VR Simulators:

Although virtual reality simulators have existed for years, they had a few shortcomings, such as image quality. Scientists at the Stanford Computational Imaging Lab used their knowledge of both optics and artificial intelligence to bridge the gap between simulations and reality. They developed neural holographic displays using the first ever computer-generated holography algorithms that produced full-color images at 1080p resolution in real-time.

Artificial intelligence has also been used to make virtual doctors or team members in surgical simulations. They may range from simple chatbots to more complex AI characters. You can interact with them like you would with a normal human being. These interactions have been shown to aid inter-professional communication training.

Robot Assisted Surgery:

Using artificial intelligence, robots capable of carrying out intricate procedures involved in surgeries are being designed. In order to execute surgeries with the assistance of these robots, surgeons can first practice in a simulated environment. VR allows the realistic visualization of the patient’s insides as well as the robotic tools.

Predictive Analysis:

Surgeons can use simulators to test different surgical scenarios. Machine learning algorithms are then trained to make predictions about the likely outcomes of these scenarios. Surgeons can also optimize the behavior of AI assistants that are meant to be used during the surgery.

Challenges of AI in Surgical Simulation

• The performance evaluation by AI may not accurately reflect the trainee’s performance as real-life surgical scenarios are way more complicated. The criteria against which the participants’ performance is assessed is inadequate.
• Studies have shown that sometimes AI algorithms fail to correctly identify complex structures such as tumors. In order to perfectly train AI algorithms, experts will have to assemble huge datasets containing a great number of annotations and segmentations of anatomical structures. Manually conducting this dreary process requires a great deal of resources.
• Regulatory bodies need to establish a proper, universally accepted guideline for the use of AI in surgical stimulation. Regulatory-approved AI-powered surgical simulators are few and far between.
• AI in surgical simulation also poses a financial burden. Traditional methods of surgical training, on the other hand, are cheaper. The price of such technology is kept a secret. The fact that furnishing a simulation laboratory can take up to millions of dollars is undeniable.
• Medical associations and universities need to make room for AI technology in medical curricula. Learning about AI would help doctors effectively employ it in clinical practice. Doctors should know how to interpret data generated by AI and recognize potential biases in algorithms. They should also know how to address the ethical and privacy concerns that arise because of using this technology.
• In the field of cosmetic surgery, AI can’t be counted on to define attractiveness. AI discriminates against ethnicity and gender. The dataset available for such surgeries is limited which also confines the training received by AI. Facial surgeries practiced in a simulated environment, will result in lack of diversity.

Future of AI in Surgical Simulation

Once these challenges have been overcome, AI will become a part of routine surgeries and training. AI in surgical simulation is being explored for neurosurgeries. Some doctors predict that in the next five years, no surgeon would be allowed in the operating theatre before having undergone training in a simulated environment. Such computer simulation systems have been patented which will give warnings to students and stop them from proceeding if they’re doing something unsafe.

As AI systems are improved, they will create even more realistic simulations. When the accuracy of the feedback and assessment system of AI is increased, the training session for each surgeon will focus on skills that that particular surgeon needs to get a grip on. AI will be used for anesthetic evaluations and predicting the amount of blood loss likely intraoperatively or post-operatively. One day, when AI is sufficiently trained, it will be used for reaching the final diagnosis and pointing toward the correct treatment plan.

Of all the above-mentioned examples and possibilities, none are being used routinely. In short, the untapped potential of AI can only be realized once the challenges associated with its cost, safety, and reliability are resolved. For now, this technology can only be used to expand our cognitive abilities and can not be treated as a replacement to humans.