CHATGPT AND ANESTHESIA

12 Jun 202313 Jun 2023THE ANESTHESIA CONSULTANT Leave a comment

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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

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In 1997 the IBM computer Deep Blue beat world chess champion Gary Kasparov head to head. Is it feasible that CHATGPT, this decades artificial intelligence wunderkind, can equal or better a physician anesthesiologist? I entered three anesthesia consultations into CHATGPT, one preoperative, one intraoperative, and one postoperative. Is CHATGPT as good as an MD? Let’s look at what the computer answered:

PREOPERATIVE QUESTION

An 80-year-old woman with congestive heart failure gets short of breath walking up one flight of stairs. She needs to have her gallbladder removed. What sort of preoperative tests or therapies should this patient have before surgery?

CHATGPT: For an 80-year-old woman with congestive heart failure (CHF) who needs to undergo gallbladder removal surgery, it is essential to conduct a thorough preoperative evaluation to assess her overall health status and identify any potential risks. The specific evaluation required may vary depending on the patient’s medical history, the severity of the CHF, and the recommendations of the healthcare team involved. However, here are some general aspects that may be considered:

Cardiac Evaluation: Given the patient’s CHF and symptoms of shortness of breath, it’s important to assess her cardiac function comprehensively. This may involve an electrocardiogram (ECG) to evaluate heart rhythm, an echocardiogram to assess heart structure and function, and possibly additional tests such as stress testing or cardiac catheterization, depending on the severity of her symptoms and the presence of any known cardiac conditions.
Pulmonary Evaluation: Since the patient experiences shortness of breath, it may be necessary to evaluate her lung function. Pulmonary function tests, such as spirometry, may be conducted to assess lung capacity and function.
Lab Tests: Routine blood tests may be performed to assess the patient’s overall health and check for any abnormalities that could impact surgery. This may include a complete blood count (CBC), comprehensive metabolic panel (CMP), coagulation profile, and other specific tests as deemed necessary.
Imaging Studies: Depending on the patient’s medical history and symptoms, imaging studies like an abdominal ultrasound or a computed tomography (CT) scan may be ordered to confirm the diagnosis of gallbladder disease and assess its severity.
Medication Review: A review of the patient’s current medications, including those used to manage her CHF, is crucial to ensure they are optimized for the surgery. Medications may need to be adjusted or temporarily stopped to minimize potential risks during the procedure.
Anesthesia Evaluation: An evaluation by an anesthesiologist is important to assess the patient’s readiness for anesthesia and determine the most appropriate anesthesia plan for her, taking into account her cardiac condition and any other medical considerations.

It’s important to note that this is a general outline, and the specific preoperative evaluation for the patient should be determined by her healthcare team, including her primary care physician, surgeon, cardiologist, and anesthesiologist. They will consider her individual medical history and unique circumstances to ensure a safe and successful surgery.

INTRAOPERATIVE DECISION

A 60-year-old man with a history of hypertension is having a knee arthroscopy surgery under general anesthesia. In the middle of the anesthetic, his heart rate drops to 20 beats per minute, his blood pressure drops to unmeasurable, and he no longer has peripheral pulses. What do you think is going wrong, and what should be done do to save this man?

CHATGPT: I’m not a doctor, but the symptoms you described during the anesthetic suggest a severe and potentially life-threatening condition. This could indicate a cardiac event or a complication related to the anesthesia. One possible explanation for the sudden drop in heart rate, blood pressure, and loss of peripheral pulses is a condition called “bradycardia with hypotension” or “bradycardia-induced hypotension.”

In this situation, it’s crucial for the medical professionals involved to take immediate action. Here are some general steps that might be considered:

Alert the medical team: The anesthesiologist or healthcare providers in the operating room need to be notified immediately about the patient’s deteriorating condition. The surgeon and additional medical personnel may also be called upon for assistance.
Assess the airway, breathing, and circulation (ABCs): Ensure that the patient’s airway is clear and breathing is supported. Provide supplemental oxygen if necessary. Assess circulation and check for the presence of a pulse. If the patient is unresponsive or there is no pulse, initiate cardiopulmonary resuscitation (CPR) immediately.
Notify the code team: If the patient remains unresponsive and pulseless, it may be necessary to activate a “code blue” or similar emergency response to involve a specialized team experienced in resuscitation procedures.
Perform advanced cardiac life support (ACLS): Healthcare providers trained in ACLS protocols will initiate resuscitation measures, including chest compressions, defibrillation (if indicated for certain types of cardiac rhythms), and administration of medications such as epinephrine to help restore circulation.
Identify and address potential causes: Once the patient’s vital signs have stabilized or as the resuscitation efforts continue, the medical team will work to identify the underlying cause of the cardiac event. This may involve evaluating the patient’s medical history, reviewing medications, and conducting further diagnostic tests such as an electrocardiogram (ECG) or blood tests.

It’s important to note that the above steps are general guidelines and the specific actions taken may vary depending on the patient’s condition, the resources available, and the expertise of the medical team on-site. In such a critical situation, the immediate focus will be on stabilizing the patient and providing appropriate resuscitation efforts to restore circulation and address any life-threatening issues.

POST ANESTHESIA EMERGENCY

In the recovery room after a tonsillectomy, a 5-year-old child coughs up blood, becomes short of breath, and becomes unconscious. What is probably going on, and what should be done to save this child?

CHATGPT: I’m not a doctor, but the symptoms you described following a tonsillectomy can be indicative of a serious complication known as postoperative hemorrhage. In this situation, it’s crucial to seek immediate medical attention. Here are some general steps that might be considered:

Activate the emergency response: Alert the medical staff immediately about the child’s deteriorating condition. Nurses and physicians in the recovery room should be notified, and additional assistance may be needed, such as calling a code or emergency response team.
Ensure a patent airway and support breathing: Assess the child’s airway and breathing. If the child is experiencing difficulty breathing or is unresponsive, the healthcare providers should take steps to establish and maintain a clear airway. They may administer oxygen and provide respiratory support if necessary.
Control bleeding: Postoperative hemorrhage can cause significant blood loss, leading to the symptoms you described. The doctors should perform a thorough examination to identify the source of bleeding and take appropriate measures to control it. This may involve suctioning the blood from the child’s mouth, using medications to constrict blood vessels (such as topical epinephrine), or applying direct pressure to the bleeding site.
Administer fluids and blood products: If the child is experiencing significant blood loss, intravenous fluids and blood products may be necessary to stabilize their condition and restore blood volume.
Provide immediate resuscitation measures: If the child becomes unconscious or experiences cardiac arrest, cardiopulmonary resuscitation (CPR) should be initiated immediately. Healthcare providers trained in pediatric resuscitation will perform chest compressions, provide assisted ventilation, and administer medications as required.
Prepare for surgical intervention: Depending on the severity of the bleeding and the child’s condition, emergency surgical intervention may be necessary to control the bleeding. The surgical team will be assembled, and the child will be transferred to the operating room for further assessment and management.

It’s important to note that the above steps are general guidelines, and the specific actions taken may vary depending on the child’s condition, the resources available, and the expertise of the medical team on-site. In such a critical situation, the immediate focus will be on stabilizing the child, controlling the bleeding, and providing any necessary resuscitation efforts.

ANALYSIS:

Note that CHATGPT preceded two of its three answers with the words, “I’m not a doctor, but,” which likely removes the possibility that CHATGPT creator OpenAI will be sued for medical malpractice. From the answers above, I’ll attest that if you’re an inexperienced anesthesiologist or certified registered nurse anesthetist (CRNA) working alone, there is value having a resource like CHATGPT immediately available. Are there any notable errors in the CHATGPT answers? No, not really. As a written reply to the questions posed, the artificial intelligence of CHATGPT gives a reasonable outline regarding how to proceed.

Anesthesia is an acute care specialty, and often there will be no time to consult CHATGPT regarding what to do next because the patient is trying to die right in front of you, for example in the second and third scenarios above. Expect artificial intelligence in medicine to be most applicable in specialties like radiology, pathology, and dermatology, where digital images are analyzed. In radiology, this would be the evaluation of digital X-rays, MRIs, or CT scans. In pathology, this would be the evaluations of digital images of microscopic biopsies, and in dermatology, this would be the evaluation of digital images of skin lesion photographs. The model for the machine learning of digital images will be similar to the process in which a human child learns. When a child sees an animal, 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.

CHATGPT relays information developed by algorithms. Is the practice of medicine destined to be directed by computer algorithms? Yes. We’re already guided by algorithms/cognitive aides such as the decision trees of Advanced Cardiac Life Support (ACLS) or the Stanford Emergency Manual. These flow charts are useful to direct acute care, and to assure no aspect of emergency treatment is overlooked. Expanded use of algorithms is inevitable as more data is accumulated on the management of large populations of medical patients. Current electronic medical records (EMR) systems exist in every hospital, and the EMRs are generating the sort of Big Data that algorithms will be built on.

I’ve had an interest in AI and robots in medicine for years. All Things That Matter Press published my novel Doctor Vita in 2019, several years before CHAPGPT became reality. Doctor Vita described the introduction of artificial intelligence in medicine machines in a Silicon Valley university hospital. A significant and anticipated advance in Doctor Vita is the ability to interact with patients and doctors by voice input and auditory output. In short, the artificial intelligence in medicine of the future will converse with us, just as Alexa and Siri already do.

The World Health Organization projects there will be a shortage of ten million healthcare workers worldwide by the year 2030. Contemplate the following:

All medical knowledge is available on the Internet. Doctors don’t know everything or remember everything, so we routinely look up facts, evidence, and data on the Internet. A computer can recall all of these facts.
Most every medical diagnosis and treatment can eventually be written as a decision tree algorithm;
Voice interaction software is excellent and improving every year;
The physical exam is of less diagnostic importance than scans and lab tests which can be digitalized; and
A computer is far cheaper than the seven-year post-college education required to train a physician.

Will artificial intelligence assist MDs or replace MDs?Replacement of medical personnel with computers will not be welcomed by patients, doctors, or nurses, but there’s a need for cheaper healthcare, and the concept of automating physician tasks is no longer the domain of science fiction. It’s inevitable that AI will change current medical practice. A bold prediction: AI will change medicine more than any development since the invention of anesthesia in 1849.

Will the evolution of artificial intelligence in medicine lead to improved healthcare outcomes? In 2018 and 2019 autopilots drove two Boeing 737 Max airplanes to crashes despite the best efforts of human pilots to correct their course. Eleven people were killed in crashes involving vehicles using automated driving systems in the United States during a four-month period in 2022. Will we see improved outcomes or worsening outcomes when AI intersects with medicine, and machines are directing medical care? We don’t know yet. 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, will be the coming of artificial intelligence in medicine.

READ ABOUT RICK NOVAK’S FICTION WRITING AT RICK NOVAK.COM

ARTIFICIAL INTELLIGENCE IN MEDICINE

7 Aug 201722 Oct 2018THE ANESTHESIA CONSULTANT 4 Comments

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THE ANESTHESIA CONSULTANT

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group

Latest posts by THE ANESTHESIA CONSULTANT (see all)

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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.