DOCTOR VITA IS COMING

6 Sep 20196 Sep 2019THE ANESTHESIA CONSULTANT Leave a comment

Author
Recent Posts

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

Latest posts by THE ANESTHESIA CONSULTANT (see all)

HIGH FLOW NASAL OXYGEN:AN ANESTHESIA GAME-CHANGER - 15 Nov 2023
STANFORD EMERGENCY MANUAL POCKET VERSION - 28 Sep 2023
CALL FROM THE JAILHOUSE REVIEW: “I LOVED HOW THE BOOK WAS CRAFTED.” - 15 Sep 2023

My name is Rick Novak, and I’m a double-boarded anesthesiologist and internal medicine doctor and a writer of medical fiction. I’m here to talk about Doctor Vita, a vision of the future of Artificial Intelligence in Medicine.

I’m an Adjunct Clinical Professor of Anesthesiology, Perioperative and Pain Medicine at Stanford and the Deputy Chief of the department. I don’t tout myself as an expert in AI technology, but I am an expert in taking care of patients, which I’ve done in clinics, operating rooms, intensive care units, and emergency rooms at Stanford and in Silicon Valley for over 30 years.

AI is already prevalent in our daily life. Smartphones verbally direct us to our destination through mazes of highways and traffic. Self-driving cars are in advanced testing phases. The Amazon Echo brings us Alexa, an AI-powered personal assistant who follows verbal commands in our homes.Artificial intelligence in medicine (AIM) will grow in importance in the decades to come and will change anesthesia practice, surgical practice, perioperative medicine in clinics, and the interpretation of imaging. AI is already prevalent in our daily life. Smartphones verbally direct us to our destination through mazes of highways and traffic. Self-driving cars are in advanced testing phases. The Amazon Echo brings us Alexa, an AI-powered personal assistant who follows verbal commands in our homes. AIM advances are paralleling these inventions in three clinical arenas:

1. Operating rooms: Anesthesia robots fall into two groups: manual robots and pharmacological robots. Manual robots include the Kepler Intubation System intubating robot:

designed to utilized video laryngoscopy and a robotic arm to place an endotracheal tube, the use of the DaVinci surgical robot to perform regional anesthetic blockade, and the use of the Magellan robot to place peripheral nerve blocks.

Magellan robot for placing regional anesthetic blocks

Pharmacological robots include the McSleepy intravenous sedation machine, designed to administer propofol, narcotic, and muscle relaxant:

and the iControl-RP machine, described in The Washington Post as a closed-loop system intravenous anesthetic delivery system which makes its own decisions regarding the IV administration of remifentanil and propofol. This device monitors the patient’s EEG level of consciousness via a BIS monitor device as well as traditional vital signs. One of the machine’s developers, Mark Ansermino MD stated, “We are convinced the machine can do better than human anesthesiologists.” The current example of surgical robot technology in the operating room is the DaVinci operating robot. 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. The good news for procedural physicians is that it’s unlikely any AIM robot will be able to independently master manual skills such as complex airway management or surgical excision. No device on the horizon can be expected to replace anesthesiologists. Anesthetizing patients requires preoperative assessment of all medical problems from the history, physical examination, and laboratory evaluation; mask ventilation of an unconscious patient; placement of an airway tube; observation of all vital monitors during surgery; removal of the airway tube at the conclusion of most surgeries; and the diagnosis and treatment of any complication during or following the anesthetic.

2. Clinics: In a clinic setting a desired AIM application would be a computer to input information on a patient’s history, physical examination, and laboratory studies, and via deep learning establish a diagnosis with a high percentage of success. IBM’s Watson computer has been programmed with over 600,000 medical evidence reports, 1.5 million patient medical records, and two million pages of text from medical journals. Equipped with more information than any human physician could ever remember, Watson is projected to become a diagnostic machine superior to any doctor. AIM machines can input 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 increasingly more specific questions. Once each diagnosis is established with a reasonable degree of medical certainty, an already-established algorithm for treatment of that diagnosis can be applied. Because anesthesiology involves preoperative clinic assessment and perioperative medicine, the role of AIM in clinics is relevant to our field.

Artificial Intelligence and X-ray Interpretation

3. Diagnosis of images: Applications of image analysis in medicine include machine learning for diagnosis in radiology, pathology, and dermatology. The evaluation of digital X-rays, MRIs, or CT scans requires the assessment of arrays of pixels. Future computer programs may be more accurate than human radiologists. The model for machine learning is similar to the process in which a human child learns–a 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. Deep learning is a radically different method of programming computers which requires a massive database entry, much like the array of dogs that a child sees in the example above, until a computer can learn the skill of pattern matching. An AIM computer which masters deep learning will probably not give yes or no answers, but rather a percentage likelihood of a diagnosis, i.e. a radiologic image has a greater than a 99% chance of being normal, or a skin lesion has a greater than 99% chance of being a malignant melanoma. In pathology, computerized digital diagnostic skills will be applied to microscopic diagnose. In dermatology, machine learning will be used to diagnosis skin cancers, based on large learned databases of digital photographs. Imaging advances will not directly affect anesthesiologists, but if you’re a physician who makes his or her living by interpreting digital images, you should have real concern about AIM taking your job in the future.

There’s currently a shortage of over seven million physicians, nurses and other health workers worldwide. Can AIM replace physicians? Contemplate the following . . .

All medical knowledge is available on the Internet:

Most every medical diagnosis and treatment can be written as a decision tree algorithm:

Voice interaction software is excellent:

The physical exam is of less diagnostic importance than scans and lab tests which can be digitalized:

Computers are cheaper than the seven-year post-college education required to train a physician:

There is a need for cheaper, widespread healthcare, and the concept of an automated physician is no longer the domain of science fiction. Most sources project an AIM robot doctor will likely look like a tablet computer. For certain applications such as clinical 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 via retinal scanners, fingerprint scanners, or face recognition programs, so that the computer can retrieve the 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.

What will be the economics of AI in medicine? Who will pay for it? America spends 17.8% 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 alluring.

It’s inevitable that AI will change current medical practice. Vita is the Latin word for “life.” I’ve coined the name “Doctor Vita” for the AI robot which will someday do many of the tasks currently managed by human physicians.

These machines will breathe new life into our present healthcare systems. In all likelihood these improvements will be more powerful and more wonderful than we could imagine. A bold prediction: AI will change medicine more than any development since the invention of anesthesia in 1849. Doctor Vita from All Things That Matter Press describes a fictional University of Silicon Valley Medical Center staffed by both AI doctors and human doctors. How physicians interact with these machines will be a leading question for our future. AI in medicine will arrive in decades to come. Michael Crichton wrote Jurassic Parkin 1990, 29 years ago, and we still do not see genetically recreated dinosaurs roaming the Earth. But we will see AI in medicine within 29 years. You can bet on it.

Here’s a dilemma: In 2018 and 2019 autopilots drove two Boeing 737 Max airplanes to crashes despite the best efforts of human pilots to correct their course. To date there have been 3 deaths of drivers in self-driving Tesla automobiles. What will happen when AI intersects with medicine and we have machines directing medical care? In the spirit of Jules Verne, this century’s trip around the world, to the center of the earth, to the moon, or beneath the ocean’s surface is the coming of Artificial Intelligence in Medicine.

For the bibliography click here.

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?

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

ARTIFICIAL INTELLIGENCE IN MEDICINE

7 Aug 201722 Oct 2018THE ANESTHESIA CONSULTANT 4 Comments

Author
Recent Posts

THE ANESTHESIA CONSULTANT

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group

Latest posts by THE ANESTHESIA CONSULTANT (see all)

HIGH FLOW NASAL OXYGEN:AN ANESTHESIA GAME-CHANGER - 15 Nov 2023
STANFORD EMERGENCY MANUAL POCKET VERSION - 28 Sep 2023
CALL FROM THE JAILHOUSE REVIEW: “I LOVED HOW THE BOOK WAS CRAFTED.” - 15 Sep 2023

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.

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.
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.
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.