ROBOTIC ANESTHESIA 

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group
Richard Novak, MD is a Stanford physician board certified in anesthesiology and internal medicine.Dr. Novak is an Adjunct Clinical Professor in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, the Medical Director at Waverley Surgery Center in Palo Alto, California, and a member of the Associated Anesthesiologists Medical Group in Palo Alto, California.
emailrjnov@yahoo.com
THE ANESTHESIA CONSULTANT

How soon will we see robotic anesthesia in our hospitals and surgery centers? In the past three decades the high-tech revolution introduced the internet, the laptop computer, the iPhone, Google, and global positioning satellites. Most of these discoveries originated in Silicon Valley, just miles outside Stanford University Hospital where I’ve been working for the past 42 years. Our medical world inside the hospital has changed more slowly. We’ve seen advances in noninvasive surgery, fiberoptic scopes, transplantation science, cancer therapeutics, and mega healthcare delivery companies. But what’s new in anesthesia the last 30 years? Relatively little. The Glidescope, sugammadex, ultrasound-guided blocks, and the time-consuming Electronic Medical Record arrived, but we typically administer the same medications, use the same airway tubes, and watch the same vital signs monitors as we did in the 1990s. 

Why have there been no new anesthetics? Let me tell you a story: A former Stanford Chairman of Anesthesiology and friend of mine left the university in 2006 to become a pharmaceutical company executive, first at Novartis and then at AstraZeneca. Ten years ago, when I asked him what new anesthesia drugs were in the pipeline, he answered, “None, and there probably will be very few new ones. The drugs you have now are inexpensive generic drugs, and they work very well. The research and development costs to bring a new anesthetic drug to market are prohibitively expensive, and unless that new drug is markedly better, it will not push the inexpensive generic drugs out of use.”

Is the same true for anesthesia devices? Are proposed anesthetic robots too expensive to design, test, and manufacture? Can they be brought to market to assist current anesthesia providers? Can they be brought to market to replace any anesthesia providers? Keep these economic questions in mind as we review the current science of robotic anesthesia.

vanished and vanishing jobs

Jobs have already disappeared in many industries. ATMs replaced bank tellers. Automated garbage trucks replaced garbage men. In the near future automated cars and trucks will replace drivers. In medicine, computerized artificial intelligence for the analysis of digital images is superior to the human eye, placing the jobs of radiologists, pathologists, and dermatologists in peril. 

Will we live to see anesthesiologists replaced by technology? The following three pictures depict fictional anesthesia robots:

fictional medical robots

But this is what real anesthesia robots look like:

real anesthesia robots

An outline of the types of robotic anesthesia is as follows:

  1. PHARMACOLOGIC ROBOTS
  2. MECHANICAL ROBOTS PERFORMING PROCEDURES
  3. DECISION SUPPORT ROBOTS

  1. PHARMACOLOGIC ROBOTS:

In 2012 a United States national marketing firm contacted me to seek my opinion regarding an automated device to infuse propofol. The device was the Sedasys®-Computer-Assisted Personalized Sedation System, developed by Johnson and Johnson/Ethicon. The system incorporated an automated propofol infusion device, along with standard ASA monitors, including end-tidal CO2, into a device to be used to provide conscious sedation for GI endoscopy.

The SEDASYS system

The Sedasys unit infused an initial dose of propofol (typically 30 – 50 mg in young patients) over 3 minutes, and then began a maintenance infusion of propofol at a pre-programmed rate (usually 50 mcg/kg/min).  If the monitors detected signs of over-sedation, that is, falling oxygen saturation, depressed respiratory rate, or a failure of the end-tidal CO2 curve, then the propofol infusion was stopped automatically.  In addition, the machine talked to the patient, and at intervals asked the patient to squeeze a hand-held gripper device.  If the patient was non-responsive and did not squeeze, the propofol infusion was automatically stopped.

The planned strategy was to have gastroenterologists complete a weekend educational course to learn: that Sedasys was not appropriate if the patient is ASA 3 or 4 or had severe medical problems; that Sedasys was not appropriate if the patient had risk factors such as morbid obesity, a difficult airway, or sleep apnea; and gastroenterologists were taught the airway skills of chin lift, jaw thrust, oral airway use, nasal airway use, and bag-mask ventilation. 

I did not recommend the device be FDA-approved, as I saw the potential of inappropriate patients with obesity or sleep apnea slipping through the screening process, as well as the risk that an over-sedated patient could lose their airway and the gastroenterologist would not be able to rescue them, seeing as propofol has no reversal agent. 

With only one prospective clinical trial, the United States Food and Drug Administration did approve the device in 2013. There was limited clinical use of Sedasys, and Ethicon announced in March 2016 that it was pulling Sedasys from the market. 

The failure of Sedasys was attributed to three factors:

  1. If a patient became too “light” during a procedure, the Sedasys system was not capable of increasing the depth of the sedation.
  2. Both patients and endoscopists expected deep general anesthesia, not moderate sedation. 
  3. Gastroenterologists were ill-equipped to shoulder the responsibility of general anesthesia and airway management. 

From the failure of Sedasys it was clear that further refinement in technology and drug use was needed. That refinement was the development of closed-loop devices. A closed-loop control system is a set of mechanical or electronic devices that automatically regulates a process variable to a desired state or set point without human interaction. The cruise-control on your automobile is an example of closed-loop feedback control of driving speed.

In anesthesia, closed-loop devices can infuse the medications propofol and remifentanil, with the rate of the infusions guided by a bispectral (BIS) monitor of EEG (electroencephalography) activity.  Propofol is an ultra-short-acting hypnotic drug, and remifentanil is an ultra-short-acting narcotic. Administered together, these drugs induce total intravenous anesthesia (TIVA).

A closed-loop system can infuse these two drugs automatically. A BIS monitor calculates a score between 0 and 100 for the patient’s level of unconsciousness, with a score of 100 corresponding to wide awake and 0 corresponding to a flat EEG. A score of 40 – 60 is considered an optimal amount of anesthesia depth. A computer controls the infusion rates of two automated infusion pumps containing propofol and remifentanil. The infusion rates depend on whether the measured BIS score is higher or lower than the 40- 60 range. Researchers in Vancouver, Canada expanded this technology into a device called the iControl-RP, where the initials RP stand for remifentanil and propofol. In addition to the BIS monitor, the iControl-RP monitored the vital signs of blood oxygen level, heart rate, respiratory rate, and blood pressure to determine how much anesthesia to deliver.

iControl-RP robot

In a single-blind randomized study published in Anesthesiology in 2015, 42 patients were randomized to the closed-loop iControl-RP group or to a manual group. The results showed the percentage of time with BIS40-60 was greater in the closed-loop group (87%) vs. the manual group (72%). The number of perioperative adverse events and the length of stay in the postanesthesia care unit were similar. The conclusion of the study was that automated control of hypnosis and analgesia guided by the BIS was clinically feasible.

This study led to an article in the The Washington Post in 2015,  in which one of the machine’s co-developers, Dr. Mark Ansermino said, “We are convinced the machine can do better than human anesthesiologists.” The device had been used on 250 patients at that time. The iControl-RP team struggled to find a corporate backer for its project. Dr. Ansermino told The Washington Post, “Most big companies view this as too risky.” He believed a device like this was inevitable. “I think eventually this will happen,” Ansermino said, “whether we like it or not.”

A second pharmacologic robot named McSleepy used three syringe pumps to control the three components of general anesthesia (hypnosis, analgesia, and neuromuscular block) in an automated closed-loop anesthesia drug delivery system. Each component had specific monitoring: BIS; AnalgoScore (an-AL-go-score = a pain score derived from the heart rate and mean arterial pressure) which was used as the control variable to titrate the effective dose of remifentanil; and the train of four (TOF), which was a measure of the twitch strength of a muscle when its peripheral nerve was electrically stimulated.

McSleepy robot

A 2013 study in the British Journal of Anaesthesia  looked at 186 patients managed by McSleepy, in which the McSleepy system showed better control of hypnosis than manually administered anesthesia (see graphs below). 

The control of depth of anesthesia under McSleepy (blue) or manual (green)

The McSleepy system also showed faster extubation times than manually administered anaesthesia. 

A second McSleepy study in the British Journal of Anaesthesia in 2013 showed an application in telemedicine.  The remote control of general anesthetics was successfully performed between two different countries (Canada and Italy). Twenty patients underwent elective thyroid surgeries, with a master-computer in Montreal and a slave-computer in Pisa, demonstrating the feasibility of remote telemedicine control of anesthesia administration.

II.  MECHANICAL ANESTHESIA ROBOTS

Ma’s mask ventilation robot

The first example is a machine designed to provide mask ventilation, as described in the paper “Novel Anesthesia Airway Management Robot for Robot Assisted Non-invasive Positive Pressure Mask Ventilation,” Published by Dr. Ma et al, from China. Ma designed a robot equipped with two snake arms and a mask-fastening mechanism to facilitate trachea airway management for anesthesia. (PIC) The two snake arms were designed to lift a patient’s jaw. The mask-fastening mechanism was used to fasten and hold the mask onto a patient’s face. A joystick control unit managed both the lifting and fastening force. To date this system has not been used on humans, but the device was proposed as a method to perform non-invasive mask positive pressure ventilation via a robotic system.

The Kepler Intubating System

In 2012 Dr. Hemmerling at McGill University in Montreal published a paper in Current Opinions in Anaesthesiology, describing the Kepler Intubation System. The Kepler Intubation System consisted of a remote-control joystick and intubation cockpit, linked to a standard videolaryngoscope via a robotic arm. (PIC) Ninety intubations were performed on a mannequin with this device. The first group of 30 intubations was performed with the operator in direct view of the mannequin. The second group of 30 intubations was performed with the operator unable to see the mannequin. The third group of 30 intubations were performed via semiautomated intubations during which the robotic system replayed a tracing of a previously recorded intubation maneuver. All intubations were successful on the first attempt, with the average intubation times between 41 and 51 seconds for all three groups. The study concluded that a robotic intubation system can complete successful remote intubation within 40 to 60 seconds.

The Magellan Nerve Block System

In 2013 Dr. Hemmerling published the study “First Robotic Ultrasound-Guided Nerve Blocks in Humans Using the Magellan System” in Anesthesia & Analgesia. The Magellan system consisted of three main components: a joystick, a robotic arm, and a software control system. After localization of the sciatic nerve by ultrasound, 35 ml of bupivacaine 0.25% was injected by the robot. Thirteen patients were enrolled. The nerve blocks were successful in all patients. The nerve performance time was 164 seconds by the robotic system, and 189 seconds by a human practitioner. The Magellan System was the first robotic ultrasound-guided nerve block system tested on humans.  

III.  DECISION SUPPORT ROBOTS

A decision-support robot can recognize a crucial clinical situation that requires human intervention and, when allowed by the attending clinician, may administer treatment. It seems likely that cognitive robots which follow algorithms can increase patient safety.

In August 2021 Dr. Alexandre Joosten, an anesthesia professor in Brussels, Belgium and Paris, France, published “Computer-assisted Individualized Hemodynamic Management Reduces Intraoperative Hypotension in Intermediate- and High-risk Surgery: A Randomized Controlled Trial” in Anesthesiology.  This study tested the hypothesis that computer-assisted hemodynamic management could reduce intraoperative low blood pressure in patients undergoing intermediate- to high-risk surgery. This prospective randomized single-blinded study included 38 patients undergoing abdominal or orthopedic surgery. All patients had an indwelling radial arterial catheter to monitor blood pressure continuously. A closed-loop system titrated a norepinephrine infusion based on the blood pressure, and a second separate decision support system infused mini-fluid challenges when low blood pressures were recorded. Results showed the time of intraoperative hypotension was 1.2% in the computer-assisted group compared to 21.5% in the manually adjusted goal-directed therapy group (P < 0.001). The incidence of minor postoperative complications was the same between groups (42 vs. 58%; P = 0.330). The mean stroke volume index and cardiac index were both significantly higher in the computer-assisted group than in the manually adjusted goal-directed therapy group (P < 0.001). The study’s conclusion was that this closed-loop system resulted in a significant decrease in the percentage of intraoperative time with a low mean arterial pressure.

VOICE-ACTIVATED DEVICES

Voice-activated devices are gaining traction in healthcare. The story “Amazon’s Alexa Is Now a Healthcare Provider” was published by Medscape on February 17, 2022.

Alexa at bedside

The article described how thousands of Alexa-enabled devices are in use in hundreds of hospitals in America. Amazon’s Alexa functions as a digital personal assistant whose voice-powered innovation connects patients with their healthcare team members. Patients who are confined to bed can use their voice to communicate directly to a nurse’s smartphone. An Alexa device is positioned near the bed at Cedars-Sinai Medical Center in Los Angeles, making it easy to call for nursing help. (PIC) Alexa can also connect healthcare providers to their patients. Doctors or nurses can appear virtually in a patient’s room on the Alexa Show’s video screen and assess the needs of that patient. I expect voice-activation to link healthcare providers with medical robots in the future.

PROBLEMS WITH ROBOTS REPLACING ANESTHESIA

The medical publications referenced above demonstrate that robotic anesthesia devices exist, yet none of them are in common use at this time. The current and proposed robotic devices are only small steps toward replacing anesthesiologists, because anesthetizing patients requires far more expertise than merely titrating drug levels or performing a solitary mechanical procedure. 

Anesthesia management consists of a wide variety of skills:

  • preoperative assessment of a patient’s medical problems 
  • successful mask ventilation of an unconscious patient (in most cases) followed by placement of an airway tube
  • diagnosis and treatment of any medical complication that occurs as a result of the anesthesia or the surgical procedure
  • removal of the airway tube at the conclusion of most surgeries, and 
  • the diagnosis and treatment of postoperative medical complications

Successful robotic anesthesia devices may eventually eliminate the repetitive aspects of anesthesia management. You may see robots assisting anesthesia providers in the coming decades, depending on the economic viability of the technology. 

Will the intrusion of a robot into anesthesia care be a welcome event? When you’re a patient, do you desire a caring, empathetic human attending to you, or do you desire an algorithm? 

Or in the future, will you desire both?

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The most popular posts for laypeople on The Anesthesia Consultant include:
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READ ABOUT RICK NOVAK’S FICTION WRITING AT RICK NOVAK.COM.

ROBOTIC ANESTHESIA REALLY IS COMING

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group
Richard Novak, MD is a Stanford physician board certified in anesthesiology and internal medicine.Dr. Novak is an Adjunct Clinical Professor in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, the Medical Director at Waverley Surgery Center in Palo Alto, California, and a member of the Associated Anesthesiologists Medical Group in Palo Alto, California.
emailrjnov@yahoo.com
THE ANESTHESIA CONSULTANT

The February 2020 edition of Anesthesiology, our specialty’s preeminent journal, published an article on robotic anesthesia.1

The accompanying editorial by Dr. Thomas Hemmerling was titled “Robots Will Perform Anesthesia in the Near Future.2 The author wrote: 

I have no doubt that closed-loop (i.e. robotic) anesthesia is at least as good as the best human anesthesia. And that, for me, would be good enough to use it every day.”

The primary study by Joosten1 looked at the performance of multiple closed-loop systems for administration of anesthesia in 90 patients undergoing major noncardiac surgery in a single center in Belgium. The conclusion of the study was that the automated system outperformed manual control, as there was minimal but significantly better cognitive function in the patients one week after surgery when the closed loop control was used. 

A BIS monitor

The depth of anesthesia was measured using a BIS (bispectral index) monitor. A BIS electrode was applied to each patient’s forehead and temporal regions to capture the frontal electroencephalogram (EEG) from the brain. 

three Base Primea infusion pumps

In the closed-loop (automated, or robotic) group, two infusion pumps were used to deliver target-controlled intravenous infusions of the hypnotic drug propofol and the narcotic remifentanil, in order to maintain BIS values between 40 and 60. BIS values between 40 and 60 have been shown to correlate with adequate anesthesia depth.

In his editorial, Dr. Hemmerling wrote:

“Robotic anesthesia, defined as anesthesia delivered by an automated control system, will soon be available. It is my opinion that closed loop devices will become available in the United States . . .  

One of the changes our profession has gone through is an ever-increasing demand to multitask, be it by running more than one operating room, or by simultaneously performing administrative or teaching tasks. In addition, the number of parameters to monitor has also increased. It is therefore not surprising that one of the common denominators of studies comparing closed loop control versus manual control is the finding that humans change a given target infusion rate far less frequently than closed loop devices do.

I have no doubt that the practice of running more than one operating room, common in the United States but less so elsewhere, will soon be an international standard. Closed loop devices will allow us to maintain a high standard of quality independent from the amount of physical presence.

Robotic anesthesia delivered in Washington by Dr. Smith would essentially be the same as robotic anesthesia performed in Chicago by Dr. Miller. . . . 

I think technology will advance similar to what we have seen and see in the car manufacturing industry. First, there was manual transmission, then automatic transmission, double clutch systems, navigation systems, all sorts of safety assist systems…soon, there will be self-driving cars.

How will we do anesthesia in the future? It is 2030 and I am scheduled to supervise anesthesia for a 40-yr-old patient undergoing laparoscopic cholecystectomy.

In the operating room, I tell my robot—let’s call it A-bot—about the surgery, the patient, and the type of anesthesia I would like performed. “Can I get a propofol, remifentanil-based anesthesia? Can we target 45 as a Bispectral Index? A-bot, can you maintain mean arterial pressure around 65? Can you maintain cardiac index during surgery of more than 2.5 l · min–1 · m–2? A-bot, I would like to use rocuronium, bolus application is good enough, but please keep neuromuscular blockade lower than 25% at all times. Please choose a respiratory rate of 12 and adjust tidal volumes to maintain end-tidal carbon dioxide of 32 mmHg in 50% air! Let’s provide preemptive analgesia using morphine and ketorolac—usual dosages, A-bot, you know.”

A-bot answers: “Sure will, Tom—you keep me informed about surgical progress?”

“Yep.”

When I look at all the literature, including the fine work by Joosten et al.,1  I have no doubt that closed loop anesthesia is at least as good as the best human anesthesia. And that, for me, would be good enough to use it every day.”2

In 2019 I wrote an editorial that robotic anesthesia was coming.3 And as I wrote the novel Doctor Vita 4 over a 15-year span from 2004-2019, I became more and more convinced of the role technology will play, for better or for worse, in replacing the human element in patient care. The premise of the novel is valid.

Will artificial intelligence in medicine provide the world with healthcare workers who work simply by plugging them in? Will some form of Doctor Vita populate future operating rooms?

An editor in the world’s leading anesthesia journal has predicted it. 

References:

  1. Joosten, A, Rinehart, J, et al. Anesthetic management using multiple closed-loop systems and delayed neurocognitive recovery: A randomized controlled trial. Anesthesiology. 2020; 132:253–66.
  2. Hemmerling TM. Robots will perform anesthesia in the near future. Anesthesiology 2020: 132:219-220.
  3. Novak R. “Artificial Intelligence in Anesthesia and Perioperative Medicine is Coming.” EC Anaesthesia 5.5 (2019): 112- 114. 
  4. Novak R. Doctor Vita. All Things That Matter Press, 2019.




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The most popular posts for laypeople on The Anesthesia Consultant include:
How Long Will It Take To Wake Up From General Anesthesia?
Why Did Take Me So Long To Wake From General Anesthesia?
Will I Have a Breathing Tube During Anesthesia?
What Are the Common Anesthesia Medications?
How Safe is Anesthesia in the 21st Century?
Will I Be Nauseated After General Anesthesia?
What Are the Anesthesia Risks For Children?
The most popular posts for anesthesia professionals on The Anesthesia Consultant  include:
10 Trends for the Future of Anesthesia
Should You Cancel Anesthesia for a Potassium Level of 3.6?
12 Important Things to Know as You Near the End of Your Anesthesia Training
Should You Cancel Surgery For a Blood Pressure = 178/108?
Advice For Passing the Anesthesia Oral Board Exams
What Personal Characteristics are Necessary to Become a Successful Anesthesiologist?






LEARN MORE ABOUT RICK NOVAK’S FICTION WRITING AT RICK NOVAK.COM BY CLICKING ON THE PICTURE BELOW:

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ARTIFICIAL INTELLIGENCE IN MEDICINE

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group
Richard Novak, MD is a Stanford physician board certified in anesthesiology and internal medicine.Dr. Novak is an Adjunct Clinical Professor in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, the Medical Director at Waverley Surgery Center in Palo Alto, California, and a member of the Associated Anesthesiologists Medical Group in Palo Alto, California.
emailrjnov@yahoo.com
THE ANESTHESIA CONSULTANT

I’m fascinated by the topic of artificial intelligence in medicine. This is the third column in a series regarding robots in medicine. (See Robot Anesthesia and Robot Anesthesia II)

ai-medical-1-orig

AI already influences our daily life. Smartphones verbally direct us to our destination through mazes of highways and traffic. Computers analyze our shopping habits and populate our Internet screens with advertisements for products we’ve ogled in the past. Smartphones perform voice-to-text conversions by pattern recognition of human vocal sounds. Fingerprint scanners learn and then recognize the image of our thumbprints with exacting accuracy. Amazon’s Alexa is an AI-powered personal assistant that accepts verbal commands in our homes.

What about AI in medicine (AIM)? AIM is a bold enterprise on the horizon in clinical medicine. Hundreds of AIM scientific publications appear in medical journals each year. I’m not an AIM researcher, but I’m an expert clinician and I love to read. I’ve worked in almost every scenario of medical practice, and because my base is at Stanford University Medical Center in Silicon Valley, many of the advances of the high-tech industry are right here in my backyard. My medical board certifications are in internal medicine and anesthesiology—two fields which have significant overlap in their knowledge base but radically different practice settings. Internal medicine doctors work in clinics, where most diseases are chronic and the most valuable tools for doctors are excellent listening and diagnostic skills. Anesthesiologists work in operating rooms and intensive care units—acute care settings which demand vigilance, steady hands, and quick thinking.

Based on my experience and my reading, I foresee AIM/robots populating three clinical arenas in radically different roles. These arenas will be: 1) diagnosis of images, 2) clinics, and 3) operating rooms/intensive care units. Let’s look at each of these in turn.

  1. Diagnosis of images    This will be the first major application of AIM. We already have electrocardiogram (ECG) machines which interpret a patient’s ECG tracing with high accuracy, and print out the diagnosis for the physician to read. This application debuted in the 1980s and is now the industry standard, although confirmation of diagnosis by a physician is important for some diagnoses such as ST-elevation myocardial infarction (STEMI). More than a few physicians have already lost the skill of reading an ECG themselves because of this device. Future applications of image analysis in medicine will be machine learning for diagnosis in radiology, pathology, and dermatology. The evaluation of digital X-rays, MRIs, or CT scans is the assessment of arrays of pixels. Expect that future computer programs will be as accurate or more accurate than human radiologists. The model for machine learning is similar to the fashion in which a human child learns. A child is not given a list of criteria which define what a dog looks like. Instead, the child sees an animal and his parents tell him that animal is a dog. After repeated exposures, the child learns what a dog looks like. Early on the child may be fooled into thinking that a wolf is a dog, but with increasing experience the child can discern with almost perfect accuracy what is or is not a dog. Machine learning is a subset of deep learning, a concept that makes automated decision-making possible. Deep learning is a radically different method of programming computers. It requires massive database entry, much like the array of dogs that a child sees in the example above, so that the computer can learn the skill of pattern matching. The program repetitively teaches a machine the identity of certain images, and the system hones this algorithm and becomes faster and more accurate in recognizing similar images. An AI computer which masters machine learning and deep learning will probably not give yes or no answers, but rather a percentage likelihood of a diagnosis, i.e. a radiologic image has greater than a 99% chance of being normal, or a skin lesion has greater than a 99% chance of being a malignant melanoma. At the present time the Food and Drug Administration (FDA) does not allow machines to make formal diagnoses, and such AI computer applications are only prototypes. But if you’re a physician who makes his or her living by interpreting digital images, there’s real concern about AI taking your job in the future. Some experts believe AIM devices will not replace radiologists, but rather will make their work more efficient and accurate. For example, AI computers can identify MRI or CT scans which are normal, freeing human radiologists to concentrate on scans where an abnormality exists. In this scenario, radiologists would not lose their jobs to AIM computers, instead radiologists who don’t use AIM machines may lose their jobs to radiologists who do use the AIM technology. In pathology, computerized digital diagnostic skills will be applied to microscopic diagnosis. In dermatology, machine learning will be used to diagnosis skin cancers, based on large learned databases of digital photographs. Dermatologists must rely on years of experience to learn to discern various skin lesions, but an AI computer can ingest hundreds of thousands of images in a period of months.
  2. Clinics  In the clinic setting, the desired AI application would be a computer that could input information on a patient’s history, physical examination, and laboratory studies, and via machine learning and deep learning, establish the patient’s diagnoses with a high percentage of success. AI computers will be stocked with information from multiple sources, including all known medical knowledge published in textbooks and journals, as well as the electronic health records (EHR)/ clinical data from thousands of previous hospital and clinic patients. AI machines can remember this vast array of information better than any human physician. AI machines will organize the input of new patient information into a flowchart, also known as a branching tree. A flowchart will mimic the process a physician carries out when asking a patient a series of questions. The flowchart program contains a series of “if . . . then . . .” branches that depend on the patient’s answers. AI will input the information sources from each new patient, and arrive at diagnoses. Once each diagnosis is established with a reasonable degree of medical certainty, an already-established algorithm for treatment of that diagnosis can be applied. For example, if the computer makes a diagnosis of asthma, then an established textbook treatment regimen of bronchodilators will be activated. It’s projected that AIM applications in clinic settings will decrease unnecessary diagnostic tests, lower therapeutic costs, and reduce the manpower needed for outpatient medicine.
  3. Operating rooms  The best current example of robot technology in the operating room is the da Vinci operating robot, used primarily in urology and gynecologic surgery. This robot is not intended to have an independent existence, but rather enables the surgeon to see inside the body in three dimensions and to perform fine motor procedures at a higher level. In my previous essays Robot Anesthesia and Robot Anesthesia II, I described models of robots designed to perform intravenous sedation or intubation of the trachea, products which are futuristic but currently have no market share. The good news for procedural physicians such as anesthesiologists or surgeons is this: it’s unlikely any AI computer or robot will be able to independently replace the manual skills such as airway management, endotracheal intubation, or surgical excision. Regarding anesthesiology, I expect future AIM robots will be hyperattentive monitoring devices which follow the vital signs of anesthetized patients, and then utilize feedback loops to titrate or adjust the depth of anesthetic drugs as indicated by these vital signs. Such a robot would not replace a human anesthesiologist, but could serve as an autopilot analogue during the maintenance or middle phase of long anesthetics, freeing up the anesthesia professional so that he or she need not be physically present. This parallels the original genesis of the role of a nurse anesthetist—to be present during stable phases of anesthetic management—so that the physician anesthesiologist could roam to other operating rooms as needed.

What will an AIM robot doctor look like? It’s unlikely it will look like a human. Most sources project it will look like a smartphone. I’d expect the screen to be bigger than a smartphone screen, so an AIM robot doctor will likely look like a tablet computer. For certain applications such as clinic diagnosis or new image retrieval, the AIM robot will have a camera, perhaps on a retractable arm so that the camera can approach various aspects of a patient’s anatomy as indicated. Individual patients will need to sign in to the computer software system—this will be done via tools such as retinal scanners, fingerprint scanners, or face recognition programs—so that the computer can retrieve that individual patient’s EHR data from an Internet cloud. It’s possible individual patients will be issued a card, not unlike a debit or credit card, which includes a chip linking them to their EHR data.

How will we define if these medical computers are truly intelligent? The accepted test for machine intelligence is the Turing test, as described by computer scientist Alan Turing in 1950. In the Turing test, a human evaluator interacts with two players via a computer keyboard. One of the players is a human and the other a machine. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test, and is deemed intelligent.

What will be the economics of AIM? Who will pay for it? Currently America spends 17.6% of its Gross National Product on healthcare, and this number is projected to reach 20% by 2025. Entrepreneurs realize that healthcare is a multi-billion dollar industry, and the opportunity to earn those healthcare dollars is a seductive lure. Companies are looking to merge increasing computing power available at steadily decreasing costs, big data from large EHR patient populations, and artificial intelligence with an aim to drive down the costs of health care while increasing effectiveness. Expect to see the development of increasingly cheaper AIM devices to augment the skills of human physicians, or maybe replace them in some job descriptions. The government’s medical costs may decrease if work currently done by expensive-to-train physicians is instead performed by nurse practitioners or nurses aided by artificial intelligence machines, supervised by relatively few human physicians. Google is working on an AIM project in the United Kingdom entitled DeepMind. DeepMind is using machine learning to analyze eye scans from more than a million patients, with the aim to create algorithms which can detect early warning signs of eye diseases that human physicians might miss. Google researchers have also developed an AIM computer to screen for and analyze the spread of breast cancer cells in lymph node tissue on pathology slide images. Scientists at the Memorial Sloan-Kettering Cancer Center in New York have programmed over 600,000 medical evidence reports, 1.5 million patient medical records, and two millions of pages of text from medical journals into IBM’s Watson computer. Equipped with more information than any human physician could ever remember, Watson is projected to become a diagnostic machine superior to any doctor.

There’s a worldwide shortage of physicians. The earliest a human physician can enter the workforce is age 29, after completing 4 years of college, 4 years of medical school, and 3 years of the shortest residency (e.g. internal medicine, pediatrics, or family practice residency). A major advantage of AIM is that the machines won’t require 24 years of education. Can America afford to train people for almost three decades to then sit in a clinic and perform histories and physicals on patients who have chronic illnesses such as hypertension, hyperlipidemia, and obesity? Shifting these jobs to allied healthcare providers such as physician assistants or nurse practitioners is a cheaper alternative, but what could be cheaper than an AIM machine module which either assists one physician to evaluate a vast number of patients, or an AIM module of the future which replaces the physician entirely?

When can we expect to see new AIM tools adopted in clinical practice? Web-based smartphone apps such as Your.MD and Babylon already exist to assist physicians in diagnosis. You can anticipate the application of machine learning in the diagnosis of digital images soon. The DeepMind and Watson computers are blazing a trail toward machine learning in clinical medicine. Expect the FDA to assess the new technologies, and when it is safe and appropriate, to approve machine diagnosis as part of the practice of medicine. Remember how fast we advanced from a cell phone the size of a breadbox to the powerful smartphone that fits in the palm of your hand today. In the ten years since the introduction of the iPhone in 2007, who could have imagined the vast array of applications we carry in our pocket or purse in 2017?

AIM is coming. It will arrive be sooner than we think, and in all likelihood it will be more powerful and more wonderful than we could imagine. A brave prediction: AIM will change medicine more than any development since the invention of anesthesia in 1849.

I can’t wait to see it.

 

Recommended reading:

Hsieh, Paul. AI in Medicine: Rise of the Machines, Forbes, April 30, 2017. 

Mukherjee, Siddhartha. A.I. vs M.D. What Happens When Diagnosis is Automated? The New Yorker, April 3, 2017. 

Manney, Kevin. How Artificial Intelligence Will Heal America’s Sick Healthcare System. Newsweek, May 24, 2017. 

Omni staff. Artificial Intelligence in Medicine. Omni, 2016.

Bhavsar N, Norman A. Artificial Intelligence is Completely Transforming Healthcare. Futurism, April 3, 2017.

Dickson B. How Artificial Intelligence is Revolutionizing Healthcare, TheNextWeb.com, May 2017.

Russell S, Norvig P. Arificial Intelligence, A Modern Approach, 3rd Edition, 2010, Prentice Hall.

 

NOTE:

Coming in 2019, DOCTOR VITA, Dr. Novak’s second novel, an Orwellian science fiction tale of how Artificial Intelligence in Medicine will change the world we live in forever.

 

The most popular posts for laypeople on The Anesthesia Consultant include:

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The most popular posts for anesthesia professionals on The Anesthesia Consultant  include:

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How do you imagine the future of medical care? Cherubic young doctors holding your hand as you tell them what ails you? Genetic advances or nanotechnology gobbling up cancerous cells and banishing heart disease? Rick Novak describes a flawed future Eden where the only doctor you’ll ever need is Doctor Vita, the world’s first artificial intelligence physician, endowed with unlimited knowledge, a capacity for machine learning, a tireless work ethic, and compassionate empathy.

artificial-intelligence-in-medicine

In this science fiction saga of man versus machine, Doctor Vita blends science, suspense, untimely deaths, and ethical dilemma as the technological revolution crashes full speed into your healthcare.

robo_aberta

Set on the stage of the University of Silicon Valley Medical Center, Doctor Vita is the 1984 of the medical world– a prescient tale of Orwellian medical advances.

LEARN MORE ABOUT RICK NOVAK’S FICTION WRITING AT RICK NOVAK.COM BY CLICKING ON THE PICTURE BELOW:

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ROBOT ANESTHESIA II

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group
Richard Novak, MD is a Stanford physician board certified in anesthesiology and internal medicine.Dr. Novak is an Adjunct Clinical Professor in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, the Medical Director at Waverley Surgery Center in Palo Alto, California, and a member of the Associated Anesthesiologists Medical Group in Palo Alto, California.
emailrjnov@yahoo.com
THE ANESTHESIA CONSULTANT

Are anesthesiologists on the verge of being replaced by a new robot? In a word, “No.” The new device being discussed is the iControl-RP anesthesia robot.

THE iCONTROL-RP ANESTHESIA ROBOT

On May 15, 2015, the Washington Post published a story titled, “We Are Convinced the Machine Can Do Better Than Human Anesthesiologists.”

In recent years there have been significant advances in the automated delivery of the intravenous anesthetic drugs propofol and remifentanil. (Orliaguet GA, Feasibility of closed-loop titration of propofol and remifentanil guided by the bispectral monitor in pediatric and adolescent patients: a prospective randomized study, Anesthesiology 2015 Apr;122(4):759-67). Propofol is an ultra-short-acting hypnotic drug that causes sleep. Remifentanil is an ultra-short-acting narcotic that relieves pain. Administered together, these drugs induce what is referred to as Total Intravenous Anesthesia, or TIVA. Total Intravenous Anesthesia is a technique anesthesiologists use when they choose to avoid using inhaled gases such as sevoflurane and nitrous oxide. Anesthesiologists administer TIVA by adjusting the flow rates on two separate infusion pumps, one infusion pump containing each drug.

A closed-loop system is a machine that infuses these drugs automatically. These systems include several essential items: The first is a processed electroencephalogram (EEG) such as a bi-spectral monitor (BIS monitor) attached to the patient’s forehead which records a neurologic measure of how asleep the patient is. The BIS monitor calculates a score between 0 and 100 for the patient’s level of unconsciousness, with a score of 100 corresponding to wide awake and 0 corresponding to a flat EEG. A score of 40 – 60 is considered an optimal amount of anesthesia depth. The second and third essential items of a closed-loop automated system are two automated infusion pumps containing propofol and remifentanil. A computer controls the infusion rate of a higher or lower amount of these drugs, depending on whether the measured BIS score is higher or lower than the 40- 60 range.

Researchers in Canada have expanded this technology into a device they call the iControl-RP, which is in clinical trials at the University of British Columbia. The iControl-RP is a closed-loop system which makes its own decisions. The initials RP stand for the two drugs being titrated: remifentanil and propofol. In addition to monitoring the patient’s EEG level of consciousness (via a BIS monitor device called NeuroSENSE), this new device monitors traditional vital signs such as blood oxygen levels, heart rate, respiratory rate, and blood pressure, to determine how much anesthesia to deliver.

Per published information on their research protocol, the iControl-RP allows either remifentanil or propofol to be operated in any of three modes: (1) closed-loop control based on feedback from the EEG as measured by the NeuroSENSE; (2) target-controlled infusion (TCI), based on previously-described pharmacokinetic and pharmacodynamic models; and (3) conventional manual infusion, which requires a weight-based dose setting. (Reference: Closed-loop Control of Anesthesia: Controlled Delivery of Remifentanil and Propofol Dates, Status, Enrollment Verified by: Fraser Health, August 2014, First Received: January 15, 2013, Last Updated: March 5, 2015, Phase: N/A, Start Date: February 2013, Overall Status: Recruiting, Estimated Enrollment: 150).

In Phase 1 of the iControl-RP testing involving 50 study subjects, propofol will be administered in closed-loop mode and a remifentanil infusion will be administered based on a target-controlled infusion. In phase 2 involving 100 study subjects, both propofol and remifentanil will be administered in closed-loop mode. The investigators aim to demonstrate that closed-loop control of anesthesia and analgesia based on EEG feedback is clinically feasible.

In both phases, an anesthesiologist will monitor the patient as per routine practice and have the ability to modify the anesthetic or analgesic drugs being administered. That is, he or she will be able to adjust the target depth of hypnosis, adjust the target effect site concentration for remifentanil, immediately switch to manual control of either infusion, administer a bolus dose, or immediately stop the infusion of either drug. iControl-RP is connected to the NeuroSENSE EEG monitor, the two infusion pumps for separately controlled propofol and remifentanil administration, and the operating room patient vital signs monitor. A user interface allows the anesthesiologist to set the target EEG depth level, switch between modes of operation (manual, target-controlled infusion, or closed-loop), and set manual infusion rates or target effect-site concentrations for either drug as required.

Per the article in the Washington Post. (Todd C. Frankel, Washington Post, May 15, 2015), one of the machine’s co-developers Mark Ansermino, MD said, “We are convinced the machine can do better than human anesthesiologists.” The iControl-RP has been used to induce deep sedation in adults and children undergoing general surgery. The device had been used on 250 patients so far.

Why is this robotic device only a small step toward replacing anesthesiologists?

A critical realization is that anesthetizing patients requires far more skill than merely titrating two drug levels. Every patient requires (1) preoperative assessment of all medical problems from the history, physical exam, and laboratory evaluation of each individual patient, so that the anesthesiologist can plan and prescribe the appropriate anesthesia type; (2) placement of an intravenous line through which the TIVA drugs may be administered; (3) mask ventilation of an unconscious patient (in most cases), followed by placement of an airway tube to control the delivery of oxygen and ventilation in and out of the patient’s lungs; (4) observation of all vital monitors during surgery, with the aim of directing the diagnosis and treatment of any complication that occurs as a result of anesthesia or the surgical procedure; (5) removal of the airway tube at the conclusion of most surgeries, and (6) the diagnosis and treatment of any complication in the newly awake patient following the anesthetic.

In the future, closed-loop titration of drugs may lessen an anesthesiologist’s workload and free him or her for other activities. In the distant future, closed-loop titration of drugs may free a solitary anesthesiologist to initiate and monitor multiple anesthetics simultaneously from a control booth via multiple video screens and interface displays. But the handling of all tasks (1) – (6) by an automated robotic device is still the stuff of science fiction. The Washington Post article said an early role for the machine could be in war zones or remote areas where an anesthesiologist is unavailable. One could conjecture that a closed-loop anesthesia system may be used to facilitate surgery in outer space some day as well.

In either case, an anesthesiologist or some other highly-trained medical professional will still be required on site to achieve tasks (1) – (6).

The iControl-RP has not been approved by the U.S. Food and Drug Administration.

The iControl-RP team has struggled to find a corporate backer for its project. Dr. Ansermino, the anesthesiologist inventor in Vancouver, told the Washington Post, “Most big companies view this as too risky,” but he believed a device like this was inevitable. “I think eventually this will happen,” Ansermino told the Washington Post, “whether we like it or not.”

That may be, but I suspect companies are risk averse regarding the iControl-RP because investment is guided by analysts and physicians who must consider the practical applications and risks of any new medical device. The issues of leaving (1) – (6) up to a robotic device are impractical at best, and dangerous to the patient at worse.

The most popular posts for laypeople on The Anesthesia Consultant include:

How Long Will It Take To Wake Up From General Anesthesia?

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How Safe is Anesthesia in the 21st Century?

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The most popular posts for anesthesia professionals on The Anesthesia Consultant  include:

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Learn more about Rick Novak’s fiction writing at ricknovak.com by clicking on the picture below:  

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