Central Line

Episode Number: 157

Episode Title: AI from A to Z (Part One)

Recorded: March 2025

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VOICE OVER:

Welcome to ASA’s Central Line, the official podcast series of the American Society of Anesthesiologists, edited by Dr. Adam Striker.

DR. ADAM STRIKER:

Welcome to Central Line. I'm your host and editor, Dr. Adam Striker. Today we're going to do something we don't do very often. Rather than a single interview, we're going to share four short conversations with anesthesiologists who have an interest and expertise in different aspects of artificial intelligence. Members of the Committee on Informatics and Information Technology, or CIIT, shared their thoughts on the big picture, on patient safety and predictive models, on remote monitoring, and on academic and subspecialty applications. So let's hear what they had to say.

We're going to start off with Dr. Beth Minzter, who took a little time to give us the overview.

Well, Dr. Minzter, welcome to the show. You're going to give us a quick intro to AI in anesthesiology. So let's start off with a little bit of a definition. How would you define AI?

DR. BETH MINZTER:

Thank you Adam, and good morning. I can be defined as a field within computer science that aims to allow computers and algorithms to perform cognitive tasks similar to humans by learning and recognizing patterns in data. It is concerned with the computational understanding of what is commonly called intelligent behavior, and it can simulate cognitive functions of the human mind atom, such as pattern recognition and problem solving. AI refers to the development of computer systems that can perform tasks that would usually require human intelligence, such as learning, reasoning, problem solving, prediction, decision making, speech recognition, and perception.

Now, machine learning is a subset of artificial intelligence that focuses on enabling machines to learn from data without it being explicitly programmed, generally based on algorithms that can adapt and learn based on feedback. Machine learning algorithms can analyze data, learn from it, and make predictions or decisions based on that learning. Deep learning is a subset of machine learning, and it involves training artificial neural networks to recognize patterns in data. It is used in image and speech recognition, natural language processing, and other applications. Other subsets of AI include robotics, computer vision, and systems designed to mimic the decision-making abilities of a human expert. AI systems and learning must be evaluated continuously and continually to assess validity, safety, accuracy and reliability, and to avoid bias based on incorrect learning and based on data sets, insufficiently comprehensive, not compatible, and not large enough, or with more similarities than those that exist in real clinical practice.

DR. STRIKER:

Well, that is a very comprehensive definition. Thanks for that. And as you know, people have very strong feelings about AI. Let's just ask the elephant in the room question, should we be scared? Or is AI going to solve some of our most salient issues?

DR. MINZTER:

Well, rather than be scared at them, I think that we can be thoughtfully or intelligently concerned. Look for and be open to opportunities. Be involved in development, maintain vigilance, accept the certainty of change, and become educated so that these tools are used only for good. Rather than be scared, I think perhaps we should have what I call a tempered enthusiasm for the potentially huge applications for positive outcomes and gains, all the while maintaining a healthy respect for the limitations and challenges that exist for its use in clinical practice. Physicians must and should remain decision makers. We must work to ensure data privacy and protection and lobby for proper security measures. This technology is intended to help clinicians treat events, not treat events. The word I like that's often used is enhancement of our decision making skills, diagnostic accuracy and therapeutic response. So there's no suggestion of clinician replacement at this time. Physicians will need to step up and speak out when we identify and recognize concerns. In direct response to your question, I think there are opportunities to improve what we currently do to take care of patients rather than solve salient issues per se. The thought is that I can help enhance clinical decision making by physician anesthesiologists, improve outcomes, and reduce negative events and errors. But systems have yet to master human empathy and situational awareness. AI, at its core, thrives on information. It can help us look at data in new ways. The goal is that we can use it to help us make better decisions for our patients.

DR. STRIKER:

The history of AI is long and complex and certainly beyond the scope of this conversation. But I do want to ask you where anesthesiology fits. Has anesthesiology been an early adapter when it comes to AI and medicine, or is the specialty catching up?

DR. MINZTER:

Well, according to one source, the term artificial intelligence was first introduced by John McCarthy in 1955. Its application in medicine has increased in the last two decades, largely due to the rapid advances in computing technology and cloud storage. Some sources suggest the first attempts to use algorithms to aid the practice of anesthesia occurred as early as the 50s also. In the last two decades, anesthesiology has been making large strides in the utilization of AI and has joined specialties such as radiology and pathology in its use. Similar to other areas of medicine, we make use of much patient information in our decision making. So wherever AI is utilized in clinical medical practice, it has the potential for integration into and influence in the practice of Theology. A challenge to be met for successful clinical integration is to help anesthesiologists understand the mechanism by which a prediction is performed by the AI algorithm. In other words, to limit the, quote, black box, unquote, nature of the algorithms, the models need to provide adequate insight into the reason a recommendation is given in a specific clinical situation. As understanding the mechanism is critical in our anesthesia practice. Consequences of incorrect prediction can be serious.

DR. STRIKER:

That's an excellent point. So where exactly are we right now? How is AI technology currently being used broadly speaking in in the anesthesia space? And is there a difference in use as it relates to the field of anesthesiology versus individual physicians?

DR. MINZTER:

Well, broadly speaking, Adam, AI methods can be applied in screening diagnostic and therapeutic techniques. AI technology can be grouped into areas of application involving depth of anesthesia monitoring, image and visually guided techniques, prediction of the risks of events during and after anesthesia, and control of anesthesia such as drug administration. To your last question, Adam, is there a difference in use? Yes, AI can collect and process data more quickly can we as humans. But it requires humans to interpret and act on those data. AI driven systems and anesthesiology will need human context and interpretation. In other words, AI is simply a tool. Though rapidly developing, it still demands individual physician interpretation and action for proper and safe use and for continued development.

DR. STRIKER:

Well, another great explanation. Dr. Minzter, thanks so much for all your time.

DR. MINZTER:

You're welcome.

DR. STRIKER:

Well, because patient safety is at the heart of everything we do. We spoke with Dr. Vesela Kovacheva about how AI is being used for patient safety, and also how it's being used with predictive models. Dr. Kovacheva where is AI having the greatest impact on patient safety?

DR. VESELA KOVACHEVA:

I think there is a lot of opportunities with this new technology to be integrated into the workflow of the anesthesiologist. And so if we can think about our daily work as kind of separated into three main stages. So for example the preoperative evaluation then intraoperative maintenance and then planning for postoperative recovery. We can integrate different AI technologies throughout the patient's perioperative journey. So for example when we think about preoperative optimization, we can harness all patient information coming from their electronic health records, from their different preoperative tests, their medical history, the vital signs, and then ensure that all of this information gets integrated into the decision making progress and develop different algorithms, which can help us risk stratify the patients and potentially target those at high risk for complications where actually planning, intervening, or even considering different approaches will make a difference for their recovery. And so in this way, when the patient presents for their surgery, they are fully optimized as best as possible conditions so that they can have the best possible outcome. And then considering the intraoperative course, we can use different technologies that can basically be a second pair of eyes that can continuously monitor and potentially integrate all these information from vital signs, from intraoperative changes in the patient's condition or potentially new labs that we're drawing and kind of, again, support our decision to achieve the best, most steady, most appropriate for the patient, intraoperative maintenance and then going into postoperative recovery. We can use all patient information, their preoperative as well as intraoperative course to design the best postoperative intervention, for example, optimize patient's opioid or pain management control, optimize their fluid intake so that, again, we achieve fast recovery, minimize complications to ensure their safety and fast recovery.

DR. STRIKER:

Well, let's talk about predictive models. One challenge for the field is access to high quality data. What are the challenges with that and also what are the opportunities?

DR. KOVACHEVA:

Yeah, that's a great question. Um, this field is rapidly growing, and as an anesthesiologist, we are surrounded by data throughout our daily life, starting from the electronic health records, which contain a lot of structured data, as well as unstructured data, that is from texts coming from different nodes or from records from preoperative tests. In addition to that, we also have vital sign data, which is time series data and different waveform data. So sometimes we use imaging like POCUS or transesophageal echo. So all of these data modalities can actually be harnessed. And significant amount of this information can be used to create better more predictive models.

DR. STRIKER:

Well I know bias is a significant concern when it comes to data, especially as it pertains to artificial intelligence. What kinds of bias should we be thinking about and how can we detect and address those biases so that we as physicians can act responsibly?

DR. KOVACHEVA:

Yeah. Bias has been a topic that has been widely researched recently, and we have a lot of information about the opportunities and actually the disadvantages of some of the artificial intelligence algorithms and biases. One of the main concerns is, we're adopting this technology, and it is very clear that there are different groups of patients. And due to access to care, to the nature of the interventions, we may be missing data in a not random way for some of these patient cohorts. And when that's happening, the algorithms do not perform optimal in those patients. And so that is the origin of bias. And because of that this missing data, the algorithms may be less efficient or sometimes even can be harmful if they are applied to those patients groups. And there is different ways to to overcome bias. One way is to simply not use the algorithms for those patients and to harness prospectively data of those patient groups. Another way is to create, for example, synthetic data, in which we represent how these patients should be managed, again in order to achieve those most optimal behavior of the algorithms. But I think regardless of using any of those approaches, we certainly have to do more research how to integrate these algorithms, in order to derive value in an equitable and fair way for all of these patients.

DR. STRIKER:

Well, it's certainly a significant issue, because it certainly goes to the heart of what generates the AI models. On our next episode of covering artificial intelligence, we'll go into that a little deeper. But given the time constraints, I do want to ask you about the algorithms in general as they become a greater part of care. How important is it for us to understand how they're integrated and how to use them? And maybe, if you don't mind, give our listeners a few tips, if you have any, on how to stay on top of all this evolution.

DR. KOVACHEVA:

Yeah, I agree with you. That has been a lot of publications in the field. And I think as an anesthesiologist, it is important for us to stay up to date on this new technology that is arriving to our operating room. Um, maybe the best approach for each of us is to follow the literature and to understand what are the advantages and the limitations of this technology. And I think that we can consider AI just as any other technology that comes into our daily practice. Um, it is new. It requires more research, and it certainly needs to be used with caution. But at the same time, if it is used well, it can provide significant benefits for our patients. So I think that just as we are considering a new device or a new medications, we have to think about it as what are the advantages, what are the indications when we should use it and also when we should not use it. And knowing those limitations would allow us to again personalize the care of our patients so that the groups for which it will be beneficial get the algorithms that are most appropriate for their care. And this allows us to take the best decisions for them. And then hopefully as an anesthesiologist, we can participate in different quality improvement initiative for the research or sometimes just share with colleagues our experience so that we can harness this technology in such a way that it will be beneficial both for us and our patients, and lead to better safety and better outcomes.

DR. STRIKER:

Well, Dr. Kovacheva, thank you very much for for all the time and your insight and expertise. We'll look forward to delving into this a little more.

DR. KOVACHEVA:

Thank you so much.

DR. STRIKER:

Next, to learn about AI and remote monitoring, we turn to Dr. Kent Berg. Dr. Berg, can you give us a quick primer on AI and remote patient monitoring? For instance, how is it being used in and beyond the hospital and where is it making the greatest impact?

DR. KENT BERG:

Thanks, Dr. Striker for having me here today. Um, first let me offer a brief definition. Remote patient monitoring, first of all, is a type of telehealth in which health care providers monitor patients outside the traditional care setting using digital or internet connected medical devices such as weight scales, blood pressure monitors, pulse oximeters, blood glucose monitors, and wearables. These devices then electronically transmit that data to health care surveillance applications or directly to providers, and these workflows can then generate automated feedback or alerts for out-of-range values. So clearly, RPM has undergone substantial evolution in the last 10 to 15 years, but most notably, telemedicine and wearables and RPM technologies became really significantly more popular during the worldwide Covid 19 pandemic between 2020 and 2022. And now machine learning and AI algorithms are being deployed as part of these RPM technologies to enhance optimization and surveillance efforts before surgery, during a patient's hospitalization, and even after they return to their own home. And you know, I'll add that there are a growing number of articles on machine learning and AI in anesthesiology, but one of the best articles out there is by Max Feinstein called Remote Monitoring and Artificial Intelligence Outlook for 2050. It was published in ANA in 2023, and a key point in this article is that future iterations of systems based on AI will not replace the anesthesiologist, but rather, free them to focus on more cognitively intense tasks.

DR. STRIKER:

Well, interesting. We all tend to think of physicians being able to interpret data and draw conclusions about a patient. Give an example of how it would free up a clinician to focus on a more cognitive task.

DR. BERG:

Sure. For example, an AI based monitoring algorithm might alert an anesthesiology of a predicted cardiac event, and the anesthesia provider will put this alert in the context of the patient, of the space they're in, of the stage of the surgery they're in, and then that anesthesiologist may decide to intervene or not. The AI enhanced algorithm is a tool, but it's not the decision-making performer, if you will. And you know, another example is that the same could be applied to a pre-hospital or post-discharge settings for more complex patients. In the O.R., for example, there's this device called the Edwards Hypotension Prediction Index, which is already available today, and it is used to predict when a patient is likely to have a significantly low blood pressure, even before it happens in the operating room.

DR. STRIKER:

Well, let's turn to the marketing aspect. Talk a little bit about what the market overview looks like when it comes to these specific tools.

DR. BERG:

Yeah. So this is a really exciting piece of this conversation. Frankly, you know, with with the onset of Covid and the aftermath of it, the global remote patient monitoring market is expanding just in a crazy fashion. According to some 2023 research, the global RPM market was valued at $4.4 billion at the end of 2022. That's almost three years ago now, And estimating a compound annual growth rate of 18.5% that was, you know, reported in this study, uh, this group expects the worldwide RPM market to be worth 16.9 billion in 2030. And, you know, an important piece of this also is that they predicted that more than 70 to 80 million US citizens will be using some form of remote monitoring by the end of 2025, which is now this year. Right? And considering that the management of chronic diseases represents 90%, 90% of the US healthcare costs. Remote patient monitoring offers substantial potential to improve lives by identifying early warnings and track progress of adherence to patient specific medical plans.

DR. STRIKER:

Well, obviously there are concerns with any new technology. Let's go through a few of them when it comes to remote patient monitoring, if you don't mind.

DR. BERG:

Sure. And I agree with you. Although the promise of AI enhanced RPM is tremendous, you know, there are several issues that that need to to be understood or discussed. And specific to the practice of anesthesiology and our care team model--that's something that's been in the headlines a lot lately—AI enhanced remote patient monitoring systems in the perioperative setting may enable different types of care team models. One is an example of an anesthesia control tower model of supervision, where an anesthesia provider does not need to be physically present in the room for minor procedures, but may still have supervisory capabilities from a remote location like a control tower. And under today's billing regulations, anesthesia providers are not able to bill for remote anesthesia monitoring, so those laws would have to be changed in a major way in order to be accepted in the US healthcare market.

Now, from a legal perspective, that's a whole nother can of worms. If an AI algorithm makes a mistake, like it does not predict an adverse event, or if it falsely predicts an event which ended in an unnecessary intervention, is the provider or the AI remote patient monitoring company liable for this error? Right? This is an example where the technology has already outpaced the litigation laws in the field of medicine. And regarding the technology, what if the patient misuses the RPM device and has a bad outcome with or without AI? What are the standards for educating the patient and maintenance of the monitor and the associated hardware? Let's say that there is an alarm that's triggered by their data. What is the timeframe in which a provider should reach out to those patients?

And I'll just briefly touch on the ethical concerns. You know, should the government step in and make this type of future monitoring a publicly available standard? If so, who would pay for this? And the consent for receiving medical care by a provider or in a healthcare system that uses AI enhanced remote patient monitoring, what exactly does that consent cover? Does it does it cover the data that would be shared with the providers? Is that data sellable to an insurance company or other research firms? Who owns the data? And once it's captured in the monitoring software or database. And then, you know, in order to scale this type of technology, the integration of AI enhanced remote monitors calls for a strategic approach, one that's mindful of patient specific needs while ensuring that that these new technologies are in harmony with existing IT frameworks.

DR. STRIKER:

And finally, before I let you go, what are the promises of AI in remote patient monitoring?

DR. BERG:

Well, I think there are several that are very exciting at this time. I think AI based RPM technologies may allow for better preparation and risk stratification of patients in the preoperative period. I think it has implications to potentially change the care team model. Medical early warning systems, or MUSE, will likely enhance our care in the PACU and the ICU settings. You know, I think that it will allow us to predict when patients are going to decline in those areas and also reduce the false alarms of those predictive algorithms. So, for example, imagine sitting at your PACU control desk or performing ICU rounds with a head mounted display. If you can get over what the weight of the headset, or it might look a little odd with you walking around with a headset, this AI powered augmented reality headset could show the patient electronic health record data, vital sign trends, predicted risk scores, or even computer assisted interpretation of radiology studies while you're making decisions about patient care in real time. I think that's just extremely awesome. But it also has the potential for reducing hospital bouncebacks due to complications that occur in the patient's own home after surgery. An interesting fact that I, that I uncovered recently was that, you know, despite the implementation of enhanced recovery after surgery protocols or the perioperative surgical home, some sources quote that as many as 5 to 10% of patients worldwide actually die within the first 30 days after surgery. As ambulatory surgery becomes more and more common for for increasingly complex patients, the demand for high quality and safe monitoring at home is expected to increase dramatically, and RPM really paves the way for that to happen. Enhancement of AI algorithms in in RPM systems has tremendous potential to improve the care of patients in their home environments, and it's an exciting time to be in healthcare. But it's also humbling to realize the responsibility these new technologies bring with them.

DR. STRIKER:

Well, Dr. Berg, thanks very much for your time and sharing your insights. And we'll we'll have to check out that article and also keep a close eye on on the technology as it proceeds.

DR. BERG:

Sounds good. Thank you, Dr. Striker.

DR. STRIKER:

Finally, Dr. Vikas O’Reilly-Shah shared his expertise about AI as it relates to academic and subspecialty applications. Dr. O'Riley Shah, talk a little bit about the academic applications of AI. Just generally, how is it being used in academic medicine?

DR. VIKAS O’REILLY-SHAH:

Sure. I think that there's a lot of really exciting opportunities for using these kinds of tools in academic medicine specifically. I know you've talked a lot about other use cases as well. Um, but for the academicians in the crowd, we're using this to summarize the literature, potentially to write manuscripts and grants, to peer review manuscripts, abstracts in appropriate use cases, um, to develop, study design, even to write code and perform statistical analyses. And then as well as for comprehensive, efficient communication, writing letters, things like that. So amongst those, I think that there's a lot of wide use cases for these kinds of tools.

DR. STRIKER:

Now to use these tools responsibly, do you think that we all need special kind of education or is that something that needs some more examination?

DR. O’REILLY-SHAH:

I definitely think that this is another area where the development and deployment of these tools has really outpaced the training and the awareness of the kind of ethical and responsible uses of them that are attended to bringing them into our own arenas. So I think I would reemphasize what others have probably said in your podcast series, which is that regardless of whether it's a clinical decision or whether you're writing a research paper, AI itself just needs to be seen as a tool. It's really the combination of the subject matter plus the AI that's fruitful, and using it as a standalone tool, or using it to replace your own judgment carries some risks. For example, there is that lawyer who submitted a brief from an AI with a bunch of made up citations and really, you know, had their license put into jeopardy because of that. And so the risk of hallucinations, very seriously does remain high. And and so it's really important to fact check and verify what's, what's coming out of these tools. I'd also mention that there are policies about the appropriateness of the use of these tools by organizations that are being rapidly developed, and that should always be verified prior to any particular use. One thing that I might specifically point out is if you're going to use it in the context of a peer review or an abstract review, you would want to verify that the organization is okay with that. If you're using a publicly available AI tool, those organizations hang on to that data. And because they do hang on to that data, you might be taking something that's confidential and giving it away, essentially. So that's something to check. And then in terms of manuscript writing and grant writing, you really want to make sure that you disclose the use of these tools, because there are a lot of organizations that want to make sure that they understand that the language that's coming out of these tools is disclosed, and it's understood that people are using these tools and that they're responsible for the content that they're putting into their abstracts and manuscripts and grants.

DR. STRIKER:

Yeah, I anticipate this is going to be one of the most watched aspects of AI when it comes to academic medicine, just given what you have have outlined. It'll definitely be an interesting facet as we move forward, but certainly something that we all need to keep a close eye on because of how powerful this technology can be.

DR. O’REILLY-SHAH:

Absolutely.

DR. STRIKER:

Well, let's turn to the subspecialties. Let's talk about the difference in applications between the anesthesiology subspecialties like cardiac versus pediatric, for example, or even how does it apply to, let's say, the performance of regional anesthesia versus general anesthesia?

DR. O’REILLY-SHAH:

Yeah, absolutely. I think that the use cases are going to vary, of course, depending on the kinds of things that you're doing day to day in your own clinical practice. So a regional anesthesiologist might benefit from the use of these tools with real time identification of structures on their ultrasound images, needle guidance, optimization of the placement of the block, the visualization of, say, the spread of the local anesthetic when it's being injected, things like that. Whereas a cardiac anesthesiologist may use the processing capabilities of AI in very different ways. For example, immediate identification of and calculation of an ejection fraction, or assisting with cardiac anesthesiologists in obtaining the the best view in order to determine the pathology that they're trying to identify. A pediatric anesthesiologist might make use of these tools in terms of things like risk prediction for the patient who presents with an upper respiratory infection, or with summarizing patient who presents after, say, you know, hundreds of surgeries, which we all have patients that are coming in after voluminous episodes of care. And there are things that obviously can be missed from a patient who has a very, very substantial cosmetical history. And so we can use the summarization aspects of these tools to great effect in order to help us to identify all of the key and salient elements of a past medical history. And I think that that's something that really any aspect or any specialty and general practitioner of anaesthesia can benefit from.

DR. STRIKER:

Well, it's certainly exciting times when you think about the possibilities of what this technology is capable of. Let's broaden it out just a little bit. What do you see is on the horizon with artificial intelligence? Where do you see all this going? Or is there something specific you'd like to see accomplished in the not-too-distant future?

DR. O’REILLY-SHAH:

Yeah, absolutely. I mean, obviously there is the hype and the hope, and then there's the reality. And I think that we haven't quite crossed that chasm yet. But I think that in terms of areas where I'm really hopeful. So one is really the crossing the Quality Chasm identifies as the Institute of Medicine report that it takes 17 years for a piece of evidence to become applied in clinical practice. And I really think that AI has the promise of helping to shorten that gap by one helping those of us who are looking at the evidence to identify quickly what pieces of evidence are most salient for a specific patient that's in front of us right now, and as well as to help to deploy the tools in that evidence base in the context of just in time, real time information delivery at the bedside. I also think that these tools will help us with the clinical information, connect the pieces, in ways that a certain kinds of constellations of signs and patterns and help to suggest interventions. I think that AI tools might help us to more rapidly identify when patients are having increased risks in the moment, at a specific moment in time, as well as to translate the voluminous data that we're generating in the context of clinical care into quality and research efforts that can then spur the next generation of improvements to patient care.

DR. STRIKER:

Well, exciting times indeed. Dr. O'Reilly-Shah, thank you so much for your time and your insight. And it'll be interesting to to watch as this unfolds.

DR. O’REILLY-SHAH:

Thank you doctor. Appreciate it.

DR. STRIKER:

Well, thanks to all of our listeners for joining us for this special episode of Central Line. This is a large topic, and as this technology unfolds, as we see it more and more in our practices, as issues arise, we will certainly continue to cover it on the podcast and delve into more specific issues as they come up. So thanks again for listening and please tune in again next time.

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