Geisinger’s Aalpen Patel on Using AI for medical diagnosis to Reduce Diagnosis time
Episode Summary:
What if we could use machine learning to train software to read CT scans of patients with intracranial hemorrhaging? Time to diagnosis could be doubled, potentially saving lives. This week Harry discusses such questions with Dr. Aalpen Patel, a physician-engineer who chairs Geisinger’s department of radiology and directs is 3D imaging and printing laboratory. This episode is the first in a two-part series on getting AI for medical diagnosis, machine learning, and analytics working in the healthcare provider setting, recorded as part of the AI World conference produced by Cambridge Innovation Institute in Boston in December 2018.
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Transcript
EP20: Aalpen Patel and Using AI to Reduce Time-to-Diagnosis
(AI World Special Series Part 1)
Note: MoneyBall Medicine is produced for the ear and designed to be heard. If you are able, we strongly encourage you to listen to the audio, which includes emotion and emphasis that’s not on the page. Transcripts are generated using a combination of speech recognition software and human transcribers and may contain errors. Please check the corresponding audio before quoting in print.
Harry Glorikian: Welcome to this two-episode series of Moneyball medicine on AI for medical diagnosis, machine learning and analytics, focused on how we get these technologies working in the provider setting. This special series was recorded as part of the AI world conference produced by “Cambridge Innovation Institute” this last December in Boston Massachusetts, I am your host Harry Glorikian. In this series I will interview 2 of the speakers from the event and we will hear their experiences we will dive into the challenges and opportunities they’re facing looking at how we implement AI for medical diagnosis in machine learning to clinical practice.
Welcome to Moneyball medicine….
Intracranial hemorrhage or put in layman’s terms, internal bleeding inside the skull affects approximately 50,000 patients per year in the United States. With 47 percent of patients dying within 30 days, early and accurate diagnosis is critical for these patients. What if we can train computer systems to read these CT scans accurately and quickly, thereby reducing time to diagnose this by 96 percent essentially, doubling the speed at which we diagnose these patients. my guest today sees the smart use of machine learning technology to aid providers in delivering better and faster care especially in these areas where time is critical.
Dr. Aalpen Patel is a physician engineer, informatician, active teacher and researcher, he has a special interest in machine and deep learning, mathematical modeling of biological processes, informatics and 3D Printing. He is the chair of the department of radiology and the medical director for 3D Imaging and printing laboratory for Geisinger. Dr. Patel has published 90 research articles, review articles, textbook chapters and abstracts and has given several lectures at regional, national and international radiology meetings. Dr. Patel is an electrical engineer and clinically he is an interventional radiologist and his clinical research interests include dialysis access management and interventional oncology. He is trained at Temple University Hospital and M.D. Anderson Cancer Center, Dr. Patel began his career in 2001 as an assistant professor of radiology at the University of Pennsylvania School of Medicine and an interventional radiologist at hospital of the University of Pennsylvania. He has boarded in diagnostic radiology, interventional radiology and clinical informatics and a fellow at the Society of interventional radiology.
Harry Glorikian: Dr. Patel Welcome to the show.
Dr. Aalpen Patel: Thank you very much Harry for inviting me, it’s an honor.
Harry Glorikian: So Dr. Patel you and I had the great pleasure of speaking at AI world recently. Your talk was really interesting about how you’re utilizing AI for medical diagnosis in machine learning to speed up diagnosis of patients and therefore being able to get to them much faster in the interventions that they need but let’s step back a second, so you’re at a unique institution and I’ve had the pleasure of interviewing Dr. Glen Steele a few times in the past, tell the listeners a little bit about Geisinger and what you think makes it unique?
Dr. Aalpen Patel: Sure, so Geisinger is an outstanding integrated health care delivery network, we have about 13 hospitals in Pennsylvania and New Jersey, a medical school called Geisinger school of medicine and 2 research centers, so the Weis center and the Hood center, it has many arms, it has a clinical arm, an insurance arm and of course teaching and research arms. The insurance arm covers approximately 600,000 lives and we have about 30,000 employees serving our patients. So, it really is more of the few Institutions in the country which is able to take care of patients but also look at the cost of taking care of patients as well.
Harry Glorikian: So just out of curiosity, do you think that that makes a difference in how you might approach these sorts of technologies or take these chances to drive improvement in care?
What is medical diagnosis?
Medical diagnosis is the process of determining the cause of a patient’s symptoms or signs by evaluating their medical history, conducting physical examinations, and using various diagnostic tests and procedures. The goal of medical diagnosis is to identify the underlying condition that is responsible for the patient’s symptoms, and to provide an accurate and specific diagnosis that can guide the treatment and management of the condition.
The process of medical diagnosis involves a systematic evaluation of the patient’s symptoms, examination of the patient, and interpretation of the results of diagnostic tests. It is performed by healthcare professionals such as doctors, nurses, and diagnostic specialists, and may involve multiple visits and a series of tests and examinations to reach a final diagnosis.
Dr. Aalpen Patel: Yeah, I think it does because our goal is to provide the right care at the right time at the right level so it really makes us open to different ways of doing things, almost like a laboratory for innovation where you not only can innovate but also put it into effect much more readily than many other institutions which may have limitations and barriers to do so.
Harry Glorikian: Yeah, the reason I ask that is because I’ve always found conversations where somebody who’s has a 3rd party payer as opposed to it coming out of their own pocket, the implementation is just a little bit different or the enthusiasm is a little bit different.
Dr. Aalpen Patel: I agree, so I’ll given example so we have cases where a patient comes in with ataxia, that patient’s unsteady, one may order a C.T. scan of the brain and then get an M.R.I. to take a better look at the brain, but in our case what we really want to get to is the M.R.I. and eliminate the C.T. scans so that we can get the right care at the right time as opposed to costing more money by doing the additional C.T. scan. So those are the kind of things that we would have to look at. If you were paid by a 3rd party which in so many cases we are but we look at all our patients as one or one population as opposed to who pays what.
If you can eliminate the C.T. scan that makes a much bigger different when it comes to cost, so we haven’t compromise patient care, we actually have:
1: Expedite the care
2: It costs less, so those are the kind of things we look at.
Harry Glorikian: Interesting. So, you have a very interesting background actually, I’m not sure I’ve met too many people that are both an engineer and a physician, so how do you integrate those 2 disciplines in your daily work?
Dr. Aalpen Patel: So, before I talk about the daily work, I’m going to take a step back. So, the United States is one of the few countries where this can happen, so I’m not the 1st and I won’t be the last, I know many who have done this, so 1st of all it’s truly a privilege to be have been educating the United States. And of course, we recognize that there is synergy between engineering and medicine early on. At every stage in my career, I have used engineering to provide a unique perspective on medicine or to improve patient care.
So, from a mathematical modeling, a biological system in modeling of early tumor growth or to understand IVT flow through a filter, using complex fluid dynamic or more recently using machine learning to prepare. So, I think that’s where engineering and medicine really has to synergy, look at things differently than medicine is always looked at. Not only from small things but even bigger things like processes and so on, right? Industrial engineering and medicine are becoming very important, so just to look at processes and from a daily perspective, engineering 101 say how do I do things better? So, you have a process, you have something that you are doing and the 1st question and engineer is always asking is how do I do this better? How do make it better? And that’s a philosophy to help me every day, it has taught me that I don’t just think outside the box but just throw away the box.
Harry Glorikian: Well yeah, throwing away the box in medicine is not a trivial activity thus far [chuckle]. I’d like to be in the room when you say that to someone and see what happens. So tell us about your work at Geisinger? What sorts of projects are you working on that involve machine learning and AI for medical diagnosis or analytics?
I want to talk about what you’re doing but then also what the impact it has? What does the product do beyond how it’s done?
Dr. Aalpen Patel: So, I’ll talk about some of the published work that we have. So, we started probably about almost 4 years ago now. As a proof of concept, we asked the question that we have a lot of the data. So, in Geisinger we have massive amount of data, just from imaging perspective we have two petabytes of data we were writing customer number two for Epic and so we have accumulated a lot of the electronic health record data for the last 22 years and on top of that we have a lot of genomics data from our micro-project. About 92,000 patients or so from our exome sequencing. So, what do you do with all that data?
So, we asked the question how do you translate the data into patient care? So the 1st thing we have to do is liberate some of the data, once we have liberated the data we asked ourselves now we have this data but it’s not perfect data, do we leverage that data or do you look for perfect data and we decided that we’re going to go with something that is not perfect, a more acceptable data, so the proof of concept is we asked the questions of are those asked before, the can we detect bleeding in the brain automatically. As I said there are 2 different things, one is that we have a massive amount of data but also diversity of data was just as important if not more important than the volume of data. So, we decided early on to use clinical grade data as opposed to research grade data, so we got data from over 10 years from 13 different facilities and 17 different C.T. scanners and our goal was always translation of basic science.
So, for translation, diversity is needed. So I will give an example, if you were to teach a baby or an infant what a dog is and only show them a Dalmatian or show them a show them a German Shepherd they’re only going to learn that particular breed of dogs but if you were to give them a different kind of dog, the baby may not recognize that this is a dog, let alone what kind of dog. So, giving given the diversity of data those babies is important and similarly diversity of data for machines is important as well. So, if you were to teach the machine with perfect data, it will only do well with a narrow focus but anything different, it will not work. So that’s the 1st thing that that we did.
Harry Glorikian: Question Dr. Patel. So, what if it’s imperfect data in a sense of how was the scan done correctly and so forth do you have criteria, inclusion and exclusion criteria for what you feed the machine, or do you give it all?
Dr. Aalpen Patel: So, in real life you have all different variety of data right, so you have different protocols for different situations, you have different machines of different vantage at difference to institution, some may have attained the 30 slices, some may attain 25 slices, so those are the differences that you have and, to change all that, all the institutions in the United states, a particular protocol a particular machine is not going to happen anytime soon or if ever. So, our approach was that instead of saying that we’re going to change the way people do things, we’re going to use the data that is generated and use that data to teach the machine.
So, do we have exclusion criteria? So, if you have half a brain that was scanned for whatever reason, yeah, we didn’t use those kinds of things but if you had a full brain scan and then we use it no matter what the quality of the imaging was.
Harry Glorikian: That only works true, if the person actually had a whole brain, so I guess yes, I understand. So, tell me how has this affected care of patients, where have you seen either speed improvements or accuracy of diagnosis or caught something that somebody may not have seen? How does this affect care and then how does that drop down to the cost numbers because these are the 2 big drivers that we have in space?
Dr. Aalpen Patel: Yes, sure so the number one is that…
So, we in medicine take really good patients for high acuity patients, those who are in patients or those who are in the ED or ICU, because those studies are high priority and high acuity. The Lower clinical acuity patients who are out patients may take a longer time because from a triage perspective they’re a lower clinical acuity so they can wait a little longer but there are high acuity cases hiding within that population and we just don’t have eyes on them. From an imaging perspective we can put our eyes on it right after the study is done if can have the machine with reprioritize testing.
So that’s number one, so can we help this patient, that’s what we do with the ICA project, can you can take these outpatients whose images may not be looked at as fast as in patients or ED patients and provide “another pair of eyes” and reprioritize so that the human can look at it much faster and that’s what we did with the ICA part and it made a big difference. The other thing that was unexpected is that we actually had a few false positive looked at by some of our experts who are blinded to why we’re looking at it again and we found that on an initial go around there might be a few cases which are subtle and probably clinically insignificant were not reported, whether they saw it and did not report it because they were not significant or whether they were not reported at all is debatable but there were a handful of cases where we saw that the machine actually pick something up that humans did not. So that’s pretty interesting.
Harry Glorikian: So yeah, I would expect more and more of that over time as the machine gets smarter over time.
Dr. Aalpen Patel: Yeah.We use a data driven approach, so it’s even more amazing to me because we didn’t tell the machine where the bleeding was when we thought it, we just told it whether there’s bleeding or not, so it was able to pick up the signal without us telling it where the bleeding is. So, if you think about it that’s even more interesting to me.
Harry Glorikian: Yeah, that is interesting. So here’s 2 questions, one is what are the issues about putting this into clinical practice and then another question that I have would be I always find that people take the systems and try to stick them into an existing workflow and then I always wonder to myself because of the power of the system, does it make more sense to change the work floor around the system rather than take the system and shove it into the workflow? Do you see what I’m saying?
Dr. Aalpen Patel: Yes. No, I absolutely agree with you. So, there are 2 competing forces here, number one is that we are talking about thousands of health care workers with physicians, nurses and others who are used to have one particular kind of workflow, to change that is not going to happen fast, so but how do you make changes with AI for medical diagnosis without actually changing some of those workflows? So as an informaticist I want to change all those workflows because I think a lot of the process will be affected if you change the workflow but on the other hand when I talk to our physicians, they said you’re going to ask you to do one more thing, you’re going to ask you do things that is completely differently but not only in one place but in many places.
So, there is sort of a change fatigue. So I think we have to sort of pay attention to that as well and so one can ask that can I use that machine learning and AI for medical diagnosis techniques to actually obviate the need for work flow change and I think initially that has to be done so that we can actually improve patient care but eventually some of the workflows may be fixed as well.
Harry Glorikian: So, does that beg like a real change in medical school?
Dr. Aalpen Patel: Yes, I absolutely agree. Education will need to change from medical school to residency and beyond and perhaps as early as high school, if you’re interested in in medicine or other fields, you’re going to have to change apply to an industrial engineering method for process improvement, for workflow improvement and it has to be ingrained from the very beginning so that when these folks online…
Harry Glorikian: I’m laughing only because I mean from someone with my background, who comes from the commercial side, the industry side, you’re 6 sigma change, adjusting processes, I mean we’re trying to adjust all the time to get to the best outcome which is usually gross margin, profitability, quality of product, whichever your metric is, you’re constantly adapting. Medicine is not exactly where I would say that easily gets applied and because the consequences are significantly higher. So, it’s interesting to hear you say that.
Dr. Aalpen Patel: I mean I think that the point though. The consequences are of making a mistake are so high in medicine and that’s exactly the reasons we have to start early and this has to be quality improvement and process improvement, that workflow improvement needs to be ingrained very early on, so when a person becomes a physician or health care worker, this is in front of what they do and currently although there are many folks who work in quality and so on, not every single healthcare worker or physician is taught about quality and process improvement. So, I think it is going to take time and it’s going to have to start very early on.
Harry Glorikian: So how do you see this technology change in clinical practice?
Dr. Aalpen Patel: So, it’s clear to me that what physicians will do will change, there is no doubt in my mind and is already changing. So, we’re already helping to improve patient care, for the foreseeable future we’re not going to replace doctors, but it will help us do our jobs better. As our population ages, we will need more doctors. So, Washington Post recently stated that by 2025 we’ll have a shortage of our 90,000 physicians or AMC study recently showed that about 120,000 physician shortage will be there by 2030.
So, AI for medical diagnosis will initially help us improve and perhaps even be more efficient at our jobs and this will help us lessen some future shortages. We’re going to have to look at population management and so how do you take care of patients and look at patients in a different way and keep em out of the hospital, so we have about 20,000 heart failure patients, can we classify them and stratified them in a different way, so that we can say that this patient has a similar phenotype to this strata of patients and this is the treatment that works for them the best or understand that when a patient looks like this, the patient is going to get in trouble, so we got to get some help at home, so that we can take care the patient at home in the comfort of their home and prevent a very costly hospital admission. So, getting to know early on and I mean these are some great ideas but a Geisinger is beginning to put them into use.
Harry Glorikian: Right, I mean you guys are always on the cutting edge because whatever you implement if it benefits you, it sort of drops to the bottom line but now, let’s just step back for a second though because I always think to myself once you digitize something, the existing business model doesn’t necessarily have to be what it is. So, is there a shift to the model based on the digitization of these technologies? I mean I’m already seeing commercial organizations that have cloud based aggregated learning systems that can now benefit multiple institutions from a bigger N that it has to work off of which is one model, so you don’t need individual installations at each institution but are there other business model shifts that you see that can change either where care is delivered or how it’s delivered, etc.?
Dr. Aalpen Patel: So if you were to take out the legal impediments and so on, as well as competitive advantages I think it would just to work to think about patients and then you can remove all those barriers and say let’s aggregate all the data, learn from the data, so that every single person in the United States and even the world can be helped with this kind of data aggregation and learning from it, so yes I think that certainly can be done and it should be done, with the current infrastructure, with the current laws in place, data transfers and data aggregation is going to be difficult because there are silos.
Harry Glorikian: Well I guess what I’m thinking is do you really need everybody to come into the ivory tower? Can you do some of this work in a remote location depending on what the imaging modality is? I mean some of the modalities we’re talking about are big iron where you’ve got to have it installed in a location like yours but ultrasound imaging or these other modalities that might be more portable, that are backed by AI for medical diagnosis, do you see that that model is the same or do you see it being morphed in some way?
Dr. Aalpen Patel: So, I think it will be morphed, if there are health care companies that are taking a couple of approaches. So bear with me for a second, I’m going to talk about how we acquire data and then how to use the data, so one of the companies has ways of providing remote scanning for patients, that means that if you don’t have a very highly skilled population in a rural area, you can scan a patient from a remote the command center using the local technology and then upload that data to the cloud for interpretation elsewhere. So, in a similar fashion, you should be able to upload the data and be able to be looked at not only by machine but if you need to then by humans and almost democratize the availability of these resources.
So I agree with you and the reason I brought the technology being able to scan remotely, is it doesn’t have to be just ultrasound, there are other things that you can do too because although in these remote locations and rural locations, you may not have the ability to go beyond ultrasound in many cases, in some cases you may have the technology but you may not be able to use it effectively, because you don’t have local talent to actually to look through the scan. So, I think once you have the images you should be able to provide insight using machine learning. So, the short answer is yes.
Harry Glorikian: So, what are some of the key lessons you’ve learned when it comes to implementing this or trying to get this put into clinical practice, if you were talking to other institutions out there trying to look at these sorts of technologies?
Dr. Aalpen Patel: So, I think the data, the volume of data and diversity of data is very important. The literature is full of cases where they’ve done 70 patients, 200 patients, 400 the patients and they achieve area under the curve of 0.99 and when they put into effect it doesn’t work because you taught the machine on a wrong set of data. So, number one pay better attention to the data that you have, number two make sure that you have diversity of data. Diversity meaning that different scanners different types of protocols and also different ethnicity as well as different ages, so I think that if I can say one thing is that is absolutely critical, and I can’t over emphasize that.
Number 2 is don’t forget about how to actually…. once you have this model, how are you going to it into effect and so you’re going to have to 1st, what we have to do is we have to 1st, once we had liberated the data we had taught the machine from the data and then we have to put it back into the workflow and the way we have to do that is 1st is to design a dicom sniffer that says OK these CT should go to the machine and once the machines say yes or no, then put it back into the workflow and those are the things that one has to think about and that’s going to be different for every institution depending on what their informatics is.
Harry Glorikian: So did you have to spend any time convincing physicians or is this a complete transparent in the background and sort of..?
Dr. Aalpen Patel: So, in its current implementation, so our physicians know this is happening but in its current implementation, we’re not letting people know that this was look at by the machine and we are elevating it, it is populated in the flow and then they read it. So, there are 2 sides of thought. Number one is that if you tell people that hey machines see something, then it is going to be that people may find things that may or may not be there. So that’s just a human nature and on the other hand, the other side is that machine is so we saw the machines as beginning to see something that humans don’t see but once we validate that further than maybe it will change that to say a machine did see something, so look for more carefully.
Harry Glorikian: Interesting, yeah, I always think that technology is better when people don’t realize the technology is there. But on the other hand, these image analysis capabilities that I’m seeing are actually seeing things that the human eye can’t see as easily or can see it earlier than we might be able to see it. So, there’s almost a bifurcation at some point of what the machine can do versus what that the biological being that we are can do?
Dr. Aalpen Patel: Yeah, we as humans are going to be able to handle X Number of variables depending on who you are in your head to actually formulate a conclusion, the machines are able to look at many thousands of variables at a time to draw their conclusions. So that’s where we need to leverage them and because as more data is produced, we’re wasting the data and we are not able to leverage all the data.
Harry Glorikian: Well and also I’ve heard a lot of people talk about feeding the machine the raw data, things like where I was talking to the head of the Semeion Institute in Italy and he was talking about being able to see plaque in an artery where the human couldn’t see it but the machine could, or talking to the CEO of Icometrics about how they’re able to see incredibly minute changes in brain scans that are almost imperceptible to the human but that the machine will measure and quantify and be able to show you longitudinally that there’s been a change in that and I find those things fascinating because this vessel that we live in is only good to a certain degree and the machine will at some point just be more accurate in a sense.
Dr. Aalpen Patel: Well accurate and also, we really need help, right? Now if you just look at initially there is truly data overload as we do more scans and so as the population ages, our volumes are increasing and as our volumes are increasing and also with new techniques, the number of studies and the number of images we actually acquire is increasing. So, for example if you’re a busy outpatient neuroradiology practice, the neuroradiologist can look at up to one image every 1.5 seconds and you can’t look at this much faster than that because you’re pretty much reaching the limits of what humans can do. So that’s why we really need the machines to not only help us with the volume but also provide as insight into things like you are saying.
That we haven’t had before and the imaging is just beginning, right? If you were to look at imaging and E.M.R. data and genomics data and so on and you will get insight that you have never had the ability to see before. Can you correlate? So one of the one of the projects that worked on was to truly quantify from CT scans was the visceral fat as well as subcutaneous fat is. So instead of using B.M.I. or instead of using calipers and so on which you can see the overall understanding of what the body mass is or body fat is, it doesn’t segment out what the visceral fat and subcutaneous fat is.
But then once you are able to do that, it allows you to ask some questions that is cardiovascular Risk Factor, associated with visceral fat or are they truly associated with subcutaneous fat. Does it affect it worsening of asthma or somebody getting asthma as they gain weight and so on. So those are the questions that we have not been able to ask before effectively and now we can start getting insight into that and other comorbidity conditions as well.
Harry Glorikian: Yeah so, it’s interesting right, so I always think that in the next 3 to 5 years there are studies and things that we’re going to learn that I think are going to shake the foundations of modern medicine as we know it because cause and effect and this is a complex system. And so, I think we identify the low hanging fruit for the most part, but then much more complex diseases or cause and effect are bound to be uncovered that we never thought had an association before.
Dr. Aalpen Patel: Absolutely. And that’s where the exciting stuff is going to be, that we haven’t even had the ability to ask those questions but now we can because we have the technology to do so.
Harry Glorikian: Well Dr. Patel thank you so much for spending the time with me today and it’s great having you on the show and I look forward to staying in touch and continuing this conversation. I wish you great success not just for yourself but for all the patients that you’re managing.
Dr. Aalpen Patel: Well thank you very much Harry it has been a pleasure to have this conversation with you, let’s keep in touch.
Harry Glorikian: Thank you.
Dr. Aalpen Patel: Take care. Bye.
Harry Glorikian: And that’s it for this episode, join me for the next episode where I’ll speak to Samuel or Sandy Aronson, who is the executive director of Information Technology, Partners Health Care Personalized Medicine, we will be diving into a discussion around how AI for medical diagnosis is not the solution and that apps are not the solution, but that more effective care workflows, that apps and algorithms enabled are the solutions. If you enjoyed Moneyball medicine, please head over to iTunes to subscribe, rate and leave a review. It is greatly appreciated, hope you join us next time, until then farewell.
FAQs about using AI for medical diagnosis
What is the role of ai in healthcare diagnosis?
Artificial intelligence (AI) has the potential to play a significant role in healthcare diagnosis by improving the accuracy and efficiency of the diagnostic process. There are several ways in which AI can be used in healthcare diagnosis:
- Image analysis: AI for medical diagnosis can be used to analyze medical images, such as X-rays, CT scans, and MRI scans, to identify and diagnose conditions. AI algorithms can be trained to recognize patterns in medical images that are associated with specific diseases, and can provide a diagnosis based on the analysis of these patterns.
- Symptom analysis: AI can be used to analyze patient symptoms and medical history to provide a diagnosis. AI algorithms can be trained to recognize patterns in patient data that are associated with specific diseases, and can provide a diagnosis based on the analysis of these patterns.
- Laboratory test analysis: AI can be used to analyze laboratory test results, such as blood tests and urine tests, to identify and diagnose conditions. AI for medical diagnosis algorithms can be trained to recognize patterns in laboratory test results that are associated with specific diseases, and can provide a diagnosis based on the analysis of these patterns.
- Decision support: AI can be used to provide decision support to healthcare professionals by helping them interpret diagnostic test results and make more informed diagnoses. AI for medical diagnosis algorithms can be trained to analyze large amounts of data, including medical literature and previous patient cases, to provide recommendations and guidance to healthcare professionals.
Overall, the use of AI in healthcare diagnosis has the potential to improve the accuracy and efficiency of the diagnostic process, and to provide more individualized and effective care for patients. However, it is important to note that AI is not a replacement for human healthcare professionals, but rather a tool that can be used to support their decision-making and improve patient outcomes.
Can AI diagnose diseases?
Artificial intelligence (AI) has the potential to assist in the diagnosis of diseases, but it is not capable of making a diagnosis on its own. AI algorithms can be trained to recognize patterns in medical images, patient symptoms, and laboratory test results that are associated with specific diseases, and can provide a diagnosis based on the analysis of these patterns. However, AI is not capable of independently making a clinical judgment about the presence or absence of a disease.
AI is a tool that can be used to support healthcare professionals in their diagnostic decision-making. It can help healthcare professionals interpret diagnostic test results and provide recommendations based on the analysis of large amounts of data, including medical literature and previous patient cases. However, the final decision on a diagnosis must be made by a qualified healthcare professional, who takes into account the patient’s medical history, physical examination, and the results of diagnostic tests.
It is important to note that AI for medical diagnosis is still in the early stages of development in healthcare, and further research is needed to fully understand its potential and limitations in the diagnosis of diseases. Additionally, it is important to ensure that AI algorithms are validated and tested thoroughly before they are used in clinical practice.