ANESTHESIOLOGY Daily News
ANESTHESIOLOGY Daily News
Day
Friday
Saturday
Sunday
Monday
Tuesday
Topics
Ambulatory Anesthesia
Cardiac Anesthesia
Diagnostic POCUS
Enhanced Recovery After Surgery (ERAS)
Fundamentals of Anesthesiology
Geriatric Anesthesia
Neuroanesthesia
Obstetric Anesthesia
Opioid Crisis
Pain Medicine
Pediatric Anesthesia
Perioperative Medicine
Physician Well-Being & Burnout
Professional Issues
Regional Anesthesia & Acute Pain
Safety & Quality
Workforce Shortages
Resources
Meeting Info
Sessions
Claim CME
Archive
Resources
  • Meeting Info
  • Sessions
  • Claim CME
  • Archive
Topics
  • Ambulatory Anesthesia
  • Cardiac Anesthesia
  • Diagnostic POCUS
  • Enhanced Recovery After Surgery (ERAS)
  • Fundamentals of Anesthesiology
  • Geriatric Anesthesia
  • Neuroanesthesia
  • Obstetric Anesthesia
  • Opioid Crisis
  • Pain Medicine
  • Pediatric Anesthesia
  • Perioperative Medicine
  • Physician Well-Being & Burnout
  • Professional Issues
  • Regional Anesthesia & Acute Pain
  • Safety & Quality
  • Workforce Shortages
By Day
  • Friday
  • Saturday
  • Sunday
  • Monday
  • Tuesday
Facebook iconTwitter X icon LinkedIn iconInstagram icon
Oct 12th, 2021

Is artificial intelligence coming for your job?

The risks of AI getting it wrong with a patient.


Christopher W. Connor, MD, PhD
Christopher W. Connor, MD, PhD

“Hey, Siri, will artificial intelligence take the place of the anesthesiologist?” That question was at the heart of Monday’s “Artificial Intelligence and Machine Learning for Anesthesiologists,” presented by Christopher W. Connor, MD, PhD, Anesthesiologist at Brigham & Women’s Hospital, Harvard Medical School in Boston.

Dr. Connor described how machine learning allows decision-making algorithms to emerge from simple mathematics. He also differentiated between current successful applications of AI versus requirements for clinical use.

To date, there has been recent astonishing progress with language translation, image recognition, natural speech processing, textual analysis, and self-learning. “Are we on the upswing or have we plateaued?” he asked.

He provided the example of the first line of the great American novel if he were to write it:

His car was his car, and her car was her car.

Predicting a global bestseller, he put his first line into Google Translation to show what his first line in French would be:

Sa voiture était sa voiture et sa voiture était sa voiture.

With a simple translation into French, he said, “Google just killed the great first line of my bestseller” because it took the literalness of the translation and did not respect the nuances of the language or the poetry of his prose. With continued learning, language translations might improve on this literal word-for-word type of translation.

So, he asked, when does AI work best?

Classical AI works best when the problem is well-defined and closed, such as with games. In a game, he said the range of possible actions can be reasonably easy to enumerate and the outcomes are reasonably easily valued. In chess, for example, he said there are 20 possible moves for white and 20 possible moves for black.

This doesn’t describe anesthesiology. Anesthesiology is a pressured cycle of interpretation, physical action, and response rather than any single cognitive act, he pointed out. For instance, in a simple and straightforward patient situation, his knowledge base could inform his clinical judgment to determine the most likely outcome successfully. However, if the patient’s situation is complicated, he might base his clinical judgment on whether the patient resembles someone he might have taken care of once in his 15-year career. In this instance, he is disregarding the majority of his knowledge base for his clinical judgment.

He went on to present questions about AI in anesthesiology, suggesting “machine-assisted discovery” may be more likely than classic machine learning.

Although there is an outcome that should either be attained or avoided, it is not certain what factors lead to that outcome, and a clinical test that predicts that outcome can’t be designed. The available data provides circumstantial evidence of that outcome.

Dr. Connor said, “The signal is too diffuse across the dataset for it to be learned reliably from a small number of cases. Also, the clinical decision-making relies upon a subconscious judgment that the anesthesiologist can’t elucidate.”

He went on to explain how machine learning and neural networks evolve with principal component analysis or dimensional reduction.

He posed the question, if there is to be AI in the OR, how much? He used the idea of human decision-making and loops to illustrate his answer. “In the loop – human involvement is necessary in order for the process to occur – is where we are today,” he said. A midpoint might be on the loop, in which automated processes perform the majority of the work, and humans verify the process. The final is out of the loop, in which machine accuracy is sufficiently accurate and self-correcting so that the process can run unmonitored.

He said he was hard pressed to think of anything in the OR that could truly be out of the loop, except perhaps line isolation.

“I don’t think robots are coming to take our jobs,” he said, in response to a question from the audience.

In anesthesiology, there are so many inputs and judgments. He also pointed at regulatory and legal hurdles that stand tall against full AI implementation.

“What if AI is wrong with a patient? Whose fault would it be? Are we willing to roll the dice?”

Visit Anesthesiology Today Annual Meeting Edition for more articles.

 

From The ASA Monitor
Advocacy in Action
Advocacy in Action
You Should Run for Office!
You Should Run for Office!
Congratulations to the 2024 Excellence in Research and Presidential Scholar Award Winners
Congratulations to the 2024 Excellence in Research and Presidential Scholar Award Winners
Episode 142: Inside the Monitor – Advocacy
Episode 142: Inside the Monitor – Advocacy
Introducing ASA’s New Center for Perioperative Medicine
Introducing ASA’s New Center for Perioperative Medicine
Empowering Minds: The Role of Mental Health-Wellness in Advocacy Awareness for Residents
Empowering Minds: The Role of Mental Health-Wellness in Advocacy Awareness for Residents
More Content
Shahla Siddiqui, MD, DABA, MSc, FCCM
Anesthesiology 2021
Pain points among genders
Oct 12th, 2021
Left to right: Amy S. Pearson, MD, Elizabeth Malinzak, MD, and Amanda Xi, MD, MSE.
Anesthesiology 2021
Millennial generation’s value to anesthesiology
Oct 12th, 2021
Left to right from top: Sonya Pease, MD, MBA, Srinivas Yendru, DO, and Conrad Myler, MD. Left to right at bottom: Kelly Ivins-O’Keefe, MD, Robert W. Brandt, MD, and Cinnamon Sullivan, MD.
Anesthesiology 2021
Attendee reflections from special ASA annual meeting
Oct 12th, 2021
Lauren C. Berkow, MD, FASA, and Felipe Urdaneta, MD.
Anesthesiology 2021
Keep direct laryngoscopy in your armamentarium and add a rescue cart
Oct 12th, 2021
Steven L. Shafer, MD
Anesthesiology 2021
Rovenstine Lecture a tribute to a pioneer and mentor
Oct 12th, 2021
James C. Eisenach, MD
Anesthesiology 2021
Severinghaus Lecture honors namesake in ‘Gadgeteering for Pain Relief’
Oct 12th, 2021
Left to right: George Mashour, MD, PhD, and Alex Proekt, MD, PhD.
Anesthesiology 2021
The best minds in anesthesiology at Celebration of Research
Oct 11th, 2021
Lis Evered, PhD.
Anesthesiology 2021
COVID-19 and postoperative delirium
Oct 11th, 2021
Dominic Carollo, MD.
Anesthesiology 2021
Informed consent when caring for minors
Oct 11th, 2021
Angela Selzer, MD, and Ashish K. Khanna, MD, FCCM, FCCP, FASA.
Anesthesiology 2021
Preventing intraoperative hypotension
Oct 11th, 2021
Michael F. Aziz, MD, and Tracey Straker, MD, MPH, MS, FASA.
Anesthesiology 2021
COVID-19 impact on airway management
Oct 11th, 2021
Lee A. Fleisher, MD
Anesthesiology 2021
Lee Fleisher shares his goals for aligning standards, quality
Oct 11th, 2021
ANESTHESIOLOGY Daily News
© 2024 American Society of Anesthesiologists (ASA)
1061 American Lane | Schaumburg, IL 60173