In medicine, specialties tend to fall into two broad camps.
Some physicians spend their days diagnosing problems, interpreting data and prescribing treatments. These cognitive fields — including primary care, neurology and psychiatry — rely on clinical reasoning to guide patients through illness.
Others focus on performing procedures: inserting catheters, replacing joints and removing tumors. In interventional specialties (including cardiology, neurosurgery, orthopedics and urology), expertise takes the form of technical skill and procedural mastery.
Within the culture of medicine, these two factions have long jostled for prestige and power. For most of healthcare’s history, diagnostic prowess made internal medicine the envy of its peers, placing it at the top of the professional hierarchy.
But two technological advances turned that relationship on its head. First, imaging machines like CT, MRI and ultrasound (introduced in the 1970s and 1980s) made diagnosis faster and more precise. Second, heart-lung machines, stents, scopes and minimally invasive tools revolutionized treatment, allowing interventional specialists to open arteries, replace valves and remove organs through tiny incisions.
As new devices enabled specialists to perform procedures that once seemed impossible, interventional fields rose to the top of medicine’s hierarchy. Today, these are the specialties that attract the most competitive trainees and command much higher salaries than their primary care colleagues do.
Now, another inflection point is emerging. The convergence of generative AI and surgical robotics is likely to fundamentally alter how operations are performed — and upend the traditional hierarchy between these two groups of specialties.
How Generative AI Plus Robotics Would Perform Surgery
The idea of a robot performing autonomous surgery (that is, completing an operation without a human guiding the controls) sounds like science fiction. Until recently, it was. But since the public debut of ChatGPT in November 2022, the rapid evolution of generative AI has redefined what is technologically possible.
Hundreds of millions of people use tools like ChatGPT, Gemini and Claude, yet most have only a vague sense of how large language models work. These large language models do far more than “predict the next word.” If that were all they did, these GenAI tools would routinely produce incoherent paragraphs and nonsensical explanations. Instead, they consistently provide sophisticated reasoning, detailed plans and expert-level summaries.
Generative AI achieves this positive outcome through imitation. Specific to medicine, these models are trained on the enormous corpora of medical textbooks, scientific journals, surgical videos and clinical conversations available on the internet. Through this training process — supported by billions of internal parameters — the system learns to mimic how humans solve problems, explain concepts and carry out complex tasks. As application developers provide ever-more training, the outputs become increasingly accurate and indistinguishable from expert human performance.
Today’s generative AI systems can outline the precise steps required to remove a gallbladder. But performing that surgery requires two additional capabilities: (1) large-scale exposure to real surgical procedures and (2) a physical mechanism to translate its responses into precise actions. Surgical robots accomplish both.
Modern Surgical Robotics: The Missing Link
Over the past two decades, operative robots have allowed doctors to work through smaller incisions with enhanced visualization, increased precision and tremor-free control. A typical robotic procedure works like this:
- The surgeon sits at a console, watching a high-definition video feed of the operative field.
- Using hand controls, the surgeon directs the robot’s arms, which carry out movements inside the patient with sub-millimeter accuracy.
For generative AI to operate autonomously, developers would provide information from actual surgical cases. The large language model would analyze the visual data coming from the operative cameras inside the patient and match it to the precise hand movements surgeons make at the console in response. After training on tens of thousands of recorded procedures, the model would learn to reproduce the same stimulus-response patterns that expert surgeons use — just as it currently learns to generate accurate answers or create videos when prompted.
This approach parallels how self-driving cars are trained. But unlike the chaos of city streets (where vehicles, cyclists and pedestrians constantly move in unpredictable ways) an operating room is a controlled space, and human anatomy is more consistent and predictable than the external environment.
Furthermore, generative AI will have an easier time distinguishing anatomical structures than a self-driving car has when deciding whether an object leaving the curb is a scrap of paper, a rolling ball or a child running into the street.
To ensure safety, FDA regulators will be able to compare outcomes of AI-directed procedures with those performed by doctors. Expert surgeons would assess anonymized operative videos without knowing whether a human or AI directed the robotic arms. Only when generative AI’s performance is comparable to human physicians would approval be granted.
In the meantime, progress in robotics continues to accelerate. Elon Musk recently predicted that Tesla’s humanoid robot project Optimus would be able to perform “sophisticated medical procedures — perhaps things that humans can’t even do.”
Putting The Robotic Pieces Together
The building blocks for autonomous robotic surgery already exist. Whether it becomes reality in five years or 10 will depend less on technological progress and more on how quickly and effectively hospitals, surgeons and technology companies collaborate to train these systems. Three changes are needed now to prepare for that future.
A. Residency training will need to change
The United States already faces a deep shortage of primary care physicians, the specialty most associated with preventing disease, improving longevity and managing chronic illness. Yet residency programs continue to produce too few primary care doctors.
Whether generative AI and robotics primarily assist surgeons or eventually perform select procedures autonomously, these technologies will increase surgical efficiency. Tomorrow’s surgeons will function less like mechanics (performing every step manually) and more like pilots supervising highly reliable autonomous systems.
Operations involving predictable anatomy and minimal scarring will be early candidates for AI-directed robotics. But in patients who have undergone prior abdominal surgery, adhesions can distort anatomy in unpredictable ways. Safely navigating these variations will require the judgment and experience of seasoned surgeons, ideally working in high-volume centers of excellence.
Because it takes five to seven years for a resident to finish training in an interventional specialty, academic programs must begin rebalancing their positions now, reducing the number of surgical trainees and expanding primary care residency spots.
B. Payment models must be updated
U.S. healthcare’s fee-for-service reimbursement system rewards higher volume, not superior clinical outcomes.
Hospitals earn more when operations take longer. And when reimbursement is tied to each inpatient day, there is little financial incentive to schedule procedures after hours or on weekends.
A shift toward bundled payments — single rates that cover all costs related to a given surgical episode — would incentivize efficiency, safety and innovation. This financial restructuring aligns with a larger national opportunity: better control of chronic disease like diabetes, heart failure and hypertension. Preventing or better managing these lifelong health problems could avert up to half of all heart attacks, cancers, strokes and kidney failures, according to CDC projections, which would result in an estimated savings of $1.8 trillion.
As fewer patients require major operations, and simpler procedures are performed autonomously, the healthcare system will need fewer procedural specialists. The cost savings from fewer chronic disease complications and a reduced need for interventionalists would fund higher salaries for primary care physicians without decreasing income for the remaining specialists.
C. Medical culture will have to evolve
Clinicians have long resisted technologies that threaten professional autonomy, judgment or income. Autonomous robotic surgery will be no exception. But rising economic pressure from the growing unaffordability of care, combined with the promise of safer and more consistent outcomes, will ultimately drive adoption.
Underserved communities, including low-income rural and urban areas without sufficient specialty expertise, are likely to implement these tools first. Once shown to be safe, others will quickly follow.
Patients will hesitate at first. Technologies that take over tasks once performed exclusively by humans generate concern. When ATMs were first introduced, many Americans worried their deposits might disappear. But as people gained experience and the systems proved reliable, trust grew and the technology became routine.
Generative-AI-enabled surgical robots will follow a similar trajectory. And as physicians and patients recognize the similarities between cognitive and procedural tasks, the perceived divide between specialties will close.