RAS-ACS Communications Committee Essay Contest: Artificial intelligence in surgery: A call to action

Editor’s note: Each year, the Communications Committee of the Resident and Associate Society of the American College of Surgeons offers an essay contest centered on a theme that the committee has selected. The theme of the 2020 essay contest was Surgeon versus Machine: Evaluating the Role of Artificial Intelligence and Innovative Technology in Surgery. Following is the winning essay.

Artificial intelligence (AI) is changing surgery several decades after similar transformations occurred in the automotive and airline industries but with a similar sense of inevitability. There are biologic limits on human information processing, decision making, and dexterity; AI offers performance advantages in each of these domains.^1-6

But surgeons need not be pitted against or replaced by machines. Instead, surgeons have opportunities to integrate AI applications into clinical workflows and steer them toward optimal patient care. At present, AI is enhancing predictive analytic decision-support, surgical skill assessment and coaching, and intraoperative care. At the systems level, machine-learning models can predict trauma patients’ acuity and distribution across trauma centers and make recommendations for cancer treatments that match recommendations from multidisciplinary tumor boards.^7-9

AI’s advantages

Preoperatively, machine-learning algorithms can use livestreaming electronic health record data to predict postoperative complications, achieving greater accuracy than clinicians.^10-12 In surgical skill assessment and coaching, combinations of intraoperative video, virtual reality, computer vision, and machine-learning algorithms can classify surgeon skill and support perioperative and postoperative decision making.^13-16

Intraoperatively, machine-learning models can use waveform data to predict hypotension and prompt anesthesiologists to act sooner, more often, and differently, resulting in fewer hypotensive episodes and less time-weighted hypotension.¹⁷ Autonomous robotic platforms can perform end-to-end sutured bowel anastomoses with leak pressures that are significantly greater than leak pressures for anastomoses sewn by surgeons.⁵

These technological achievements seem pedestrian relative to potential, future applications for AI in surgery. In the future, AI likely will provide clinical decision support with levels of accuracy and precision that have been unattainable with previous technologies and may change the way some operations are performed.

Reinforcement learning mimics human learning by using trial-and-error simulations to identify discrete actions that yield the greatest probability of achieving an ultimate goal. Applied to vasopressor infusions and intravenous fluid boluses for patients with sepsis, reinforcement learning can select resuscitation strategies that are associated with increased survival compared with standard care.¹⁸

Autonomous microrobots also are on the horizon. Researchers at the Massachusetts Institute of Technology, Cambridge, designed an origami-like robot that folds into an ingestible pill, unfolds in the body, and moves in response to external magnetic fields.¹⁹ In a silicone model of the human esophagus and stomach, the microrobot dislodged a battery embedded in the stomach wall and patched the stomach wall defect in approximately five minutes.

At present, no high-level evidence shows that AI improves patient outcomes compared with existing standards for performing operations or surgical decision-making tasks. Yet, history suggests that as technologies improve, AI eventually will achieve cost-effective performance advantages, and market forces will drive adoption by health care networks and hospitals, similar to what has occurred in the automotive and airline industries.

Surgeon leadership is needed

Rather than reacting to this possibility with denial, ire, or indifference, surgeons should engage in the development of AI technologies and lead the clinical implementation process. Surgeon leadership is critically important in navigating the pitfalls of machine-learning predictive analytics. If the unique pathophysiology of an individual patient is underrepresented in the data used for model training, then model predictions for that patient will err.

Disturbingly, this phenomenon introduces the potential for model bias against underrepresented minorities. Prediction models that use biased datasets produce biased outputs, as previously demonstrated when an algorithm was used to predict crime recidivism.²⁰ Even when large datasets are well-balanced, they often omit potentially useful information from patient interviews and physical examinations. For these reasons and others, high-performance prediction models still make errors.

But can models tell when they are wrong? There are methods for determining whether a model is confident that its predictions are accurate, but they tend to overestimate model confidence.²¹ Finally, decision-support tools often impose trade-offs among risks and benefits that may misalign with individual patient preferences.

Consider a postoperative patient with both pulmonary edema and prerenal azotemia. A reinforcement learning model trained to maximize 30-day survival may recommend diuresis to avoid prolonged mechanical ventilation, narrowly achieving a greater probability of short-term survival but imposing long-term requirements for renal replacement therapy and poor quality of life. What if the patient values quality of life more than a slight increase in 30-day survival? Each of these pitfalls can be remedied with human knowledge and intuition informed by rigorous training, a thorough bedside patient interview and physical examination, and careful interpretation of model outputs in a clinical context.

AI surgical platforms also face challenges in clinical implementation. Some robotic surgical platforms feature virtual constraints that are intended to protect anatomic structures from instruments.²² But what if a blood vessel that is beyond the virtual constraint is hemorrhaging from an avulsion injury generated by traction applied within the operative field? The virtual constraint could delay or prevent the surgeon from gaining control of the injured blood vessel, harming the patient and pitting human against machine in assigning liability.

Ensuring patient safety

Similar dilemmas in assigning liability may occur when microrobots err. More importantly, patients could be harmed. Therefore, establishing the safety and efficacy of AI applications in surgery is a critical first step. It seems prudent to perform prospective clinical implementation on a small scale under close surveillance and scrutiny, similar to Phase 1 and 2 clinical trials for experimental medications.²³ As frontline providers of surgical care, surgeons should lead these trials, and collaborate with computer scientists, engineers, and commercial entities toward safe, effective clinical implementation of AI surgical platforms.

This essay is a call to action for surgeons to engage and lead in the clinical application of AI in surgery. AI clinical decision support, surgical skill assessment and coaching, and surgical platforms each offer performance advantages that could improve care for surgical patients. Experiences in other industries suggest that automation in surgery is inevitable. Surgeons are uniquely equipped with the knowledge, skills, and experience necessary to lead the safe, effective clinical adoption of AI in surgery, with the ultimate goal of providing the best care possible.

References

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