Will Surgeons Be Replaced By Robots – Bob Kocher Bob Kocher Partner – Venrock Former Expert Senior Fellow – USC Schaeffer Center for Health Policy & Economics Zeke Emanuel ZE Zeke Emanuel Vice Provost and Chair – University of Pennsylvania Department of Medical Ethics and Health Policy Venture Partner – Oak HC/FT
This analysis is part of the USC-Schaeffer Initiative for Health Policy, which is a partnership between Economic Studies at and the USC Schaeffer Center for Health Policy & Economics. The initiative aims to inform the national health care debate with rigorous, evidence-based analysis leading to practical recommendations, leveraging USC’s strengths and .
Will Surgeons Be Replaced By Robots
Vinod Khosla, a legendary Silicon Valley investor, claims that robots will replace doctors by 2035. And there is some evidence that he may be right.
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A 2017 study by Massachusetts General Hospital and MIT showed that an artificial intelligence (AI) system was equal to or better than radiologists in reading mammograms for high-risk cancerous lesions requiring surgery. A year earlier, and reported by the Journal of the American Medical Association, Google showed that computers were similar to ophthalmologists in examining retinal images of diabetics. And recently, computer-controlled robots performed successful intestinal surgery on a pig. Although the robot took longer than a human, its stitches were much better – more precise and uniform with less chance of breakage, leakage and infection. Tech boosters believe AI will lead to more evidence-based care, more personalized care and fewer errors.
Of course, improving diagnostic and therapeutic outcomes are laudable goals. But AI is only as good as the people who program it and the system in which it operates. If we are not careful, AI could not make healthcare better, but instead inadvertently exacerbate many of the worst aspects of our current healthcare system.
Using deep and machine learning, AI systems analyze vast amounts of data to make predictions and recommend interventions. Advances in computing power have enabled the creation and cost-effective analysis of large sets of payer claims data, electronic health record data, medical images, genetic data, laboratory data, prescription data, clinical emails, and patient demographic information to power AI models .
AI is 100 percent dependent on that data, and as with everything in computing, “garbage in, garbage out,” as the saying goes. A major concern about all of our health care datasets is that they perfectly record a history of unwarranted and inequitable disparities in access, treatments, and outcomes in the United States.
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According to a 2017 National Academy of Medicine report on health care disparities, non-whites continue to experience worse outcomes for infant mortality, obesity, heart disease, cancer, stroke, HIV/AIDS and total mortality. Shockingly, Alaska Natives suffer a 60 percent higher infant mortality rate than whites. And worse, the AIDS death rate among African Americans is actually on the rise. Even among whites, there are significant geographic differences in outcomes and mortality. Biases based on socioeconomic status can be exacerbated by incorporating patient-generated data from expensive sensors, phones, and social media.
The data we use to train our AI models can produce results that perpetuate—and even exacerbate—rather than correct these persistent inconsistencies. Machines do not and cannot verify the accuracy of the underlying data provided to them. Rather, they assume that the data are perfectly accurate, reflect high quality, and are representative of optimal care and outcomes. Therefore, the generated models will be optimized to get closer to the results we generate today. AI-generated inconsistencies are even more difficult to address, as models are largely machine-generated and unexplained “black boxes,” and far more difficult to audit than our current healthcare delivery processes for the people.
Another major challenge is that many clinicians make assumptions and care choices that are not well documented as structured data. Experienced clinicians develop an intuition that allows them to identify a sick patient even though he or she may appear identical to another less sick patient according to numbers entered into computer programs. This results in some patients being treated differently than others for reasons that will be difficult to distil from electronic health record data. This clinical judgment is not well supported by data.
These challenges become big when healthcare systems try to use AI. For example, when the University of Pittsburgh Medical Center (UPMC) assessed the risk of death from pneumonia in patients arriving at their emergency department, the AI model predicted that death rates decreased when patients were over 100 years old or had a diagnosis of asthma. While the AI model correctly analyzed the underlying data – UPMC did have a very low death rate for these two groups. It was incorrect to conclude that they were at lower risk. Rather, their risk was so high that emergency room staff gave these patients antibiotics before they were even registered in the electronic medical record, so the time stamps for the life-saving antibiotics were inaccurate. Without understanding the clinician’s assumptions and their impact on the data—in this case, the exact timing of antibiotic administration—this kind of analysis can lead to AI-inspired protocols that harm high-risk patients. And this is not an isolated example; clinicians make hundreds of assumptions like these every day across multiple conditions.
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Before entrusting our care to AI systems and “doctor robots,” we must first commit to identifying biases in data sets and correcting them as much as possible. Furthermore, AI systems should be evaluated not only on the accuracy of their recommendations, but also on whether they maintain or mitigate disparities in care and outcomes. One approach could be to create national test datasets with and without known biases to understand how well the models are tuned to avoid unethical care and meaningless clinical recommendations. We can go a step further and use peer review to evaluate findings and make suggestions for improving AI systems. This is similar to the highly effective approach used by the National Institutes of Health to evaluate grant applications and by journals to evaluate research results. These interventions could go a long way toward improving public trust in AI, and perhaps someday allow the patient to receive the kind of impartial care that human doctors should have provided all along.
Dr. Emanuel is vice chancellor and chair of the Department of Medical Ethics and Health Policy at the University of Pennsylvania, a venture partner at Oak HC/FT, and the recent author of Prescription for the Future: The Twelve Transformational Practices of High-Performing Healthcare Organizations. (2017). The contribution of modern robotics to healthcare is undeniable. In the last decade, robots have made remarkable progress in changing the healthcare system around the world. Robots assist healthcare professionals in a variety of tasks ranging from performing surgical procedures to delivering medications and even facilitating their interactions with patients. With the advent of next-generation medical robots, both patients and healthcare professionals will reap enormous benefits.
Intuitive Surgical’s da Vinci System allows the surgeon to bend and rotate tissue with much greater flexibility than surgical instruments. The da Vinci surgical robotic system is equipped with a 3D magnification system and miniature instruments that translate the movements of the surgeon’s hand into very precise movements inside the patient. All this means that surgeons can perform surgery with minimal incisions.
The da Vinci system was approved by the US Food and Drug Administration (FDA) in 2000. Largely due to its success, surgical robots are witnessing an increase in financial investment and the market is expected to grow to $20 billion by 2024. Established companies and startups are releasing next-generation robot systems that are more flexible, affordable and portable than the da Vinci system.
Robot Assisted Surgery
Cambridge Medical Robotics (CMR) has developed the world’s smallest surgical robot called Versius. Similar in size to a human hand, this system allows surgeons to perform operations with greater flexibility.
Johnson & Johnson and Google’s life sciences subsidiary Verily Life Sciences are also partnering to work on a next-generation robotic system. To assist orthopedic surgeons in performing knee and hip replacement surgeries, Stryker has launched Mako Surgical Robot systems. The FDA recently approved Auris Health’s Monarch robotic system for the diagnosis and treatment of lung and cancer tumors. TransEnterix has launched the Senhance Surgical Robot System to perform colorectal and gynecological surgeries. Companies such as Medtronic, Titan Medical and Mazor Robotics are also launching surgical robots in emerging markets. New entrants to the field of medical robotics are challenging traditional companies by providing better technology at affordable prices, helping healthcare systems.
In addition to assisting surgeons, robots can also help reduce the workload of regular medical staff. Robots already help nurses with simple, repetitive and time-consuming tasks in hospitals. The TUG robot is programmed to carry multiple racks of drugs, lab samples, or other sensitive materials to any location in the hospital. A startup company, Diligent Robotics, is testing a mobile robot called Poli at multiple hospitals in Austin, Texas, helping nurses spend more time with patients.
In addition to reducing nurses’ workloads, robots can also assist with other patient care duties. Veebot Robots can quickly select the right locations to draw blood. RIBA (Robot for Interactive Body Assistance) is strong
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