 Welcome, everyone, to the MetTech session of Entrepreneur India Summit 2020. I am Saurav Kumar, leader of Special Projects, Entrepreneur India, moderator for the session. While a lot of us may not realize so, but deep-take based technology, technological applications have a profound impact on everybody's life from financial services solutions to medical equipment. These technologies improve efficiency. For blockchain, for instance, is not just limited to cryptocurrencies anymore, but it is used for mobile applications and banking services. So these technologies are transforming the future through new drug discoveries, genome editing, and disease eradication, and of course telemedicine is perfect. To know more about the role of technology in healthcare for a transforming future, we have a great panel today. I'll introduce my panel first before we move to questions and answers. We have with us Prashant Warrior, co-founder and CEO, cure.ai, Ajit Narayanan, co-founder and CTO M5, Dr. Geetha Manchunath, CEO and CTO Neera Mai, Ryan Collins, managing director, Master Mutual Ventures, and Dr. Vedam Bramprasad, India CEO for Medjana. Thank you everyone for being here. So I'll just very quickly request everyone to tell our audience the basic technologies that you are using in your business very quickly, and then we can go ahead with the question and answer. So Prashant, if I can start with you. Certainly. So I'll share my... If you can have the speech for him, Prashant. Can you see my screen, everyone? Yes, we can. So we are cure.ai. We use AI to interpret radiology images. And what you see on the screen is a suite of our solutions. I'll quickly talk about what we do. We are using deep learning algorithms to automatically interpret chest x-rays, for example, or head CT scans. And as of today, we are in about 28 countries impacting around 10,000 plus lives on a daily basis. We are processing about 10,000 plus scans daily. And quickly talking about... So I mean, we receive an x-ray and the output that we produce is something like this. I just wanted to bring this about. So we are able to identify abnormalities. This is a chest x-ray solution. We're able to identify abnormalities on a chest x-ray. We can detect... I mean, we added a COVID risk, COVID finding recently. So we can detect COVID risk. We can identify where the abnormalities are, what percentage of the lung is affected. We can detect about 20 different abnormalities across the lungs, pleura, mediastinum, heart, and so on. And we can detect... We can triage for COVID and we can screen for tuberculosis. And what you see on the right side is an automated report that is created by the algorithm. So this is what goes in and what goes in is only the x-ray. And what comes out within a minute is this whole report, which can be run on the cloud, on premise. And it can integrate with the device, with the software systems, whatever. I mean, we have various ways of integrating with the healthcare system. Now, this is being used in various locations. We are using this for tuberculosis screening in Philippines. So there, quickly, I'll talk about the challenge there is that they have these mobile vans which go into the city, which basically start from Manila. They go into basically some parts of the city or they will go into rural areas. And they would screen about 200 people every day using just x-ray. So these vans have an x-ray unit. But unfortunately, they don't have a radiologist to read the x-ray. So what we are doing is we are automating the read of the x-ray using AI. Both when there is internet connection available, it goes to the cloud and gets processed. If there is no internet connection, it gets processed locally. And by that, I mean, we are able to reduce the time for tuberculosis diagnosis from somewhere like three weeks to a minute, two minutes. So that's the improvement, I mean, which reduces the number of people who are lost to follow up, basically people who come in for the x-ray. But then by the time their x-ray is read, they are lost. We are not able to find them. And also the algorithm is able to detect more TB cases. A similar thing, I mean, we have done this in many parts of the world, but also did that for COVID in Mumbai. We are working with the Municipal Corporation of Greater Mumbai, where x-rays cannot really screen for COVID-19. But what you can do is, I mean, what this bus, this is a COVID bus which is going into Dharavi and it is going into Burmikoli, Vada, various parts of the city where you don't have access to any x-rays or any even basic healthcare. And what this bus is doing is they will take SPO2, they will take an x-ray, they will look at the symptoms of the patient. And based upon all of that, now you can triage who needs to be swabbed. So a PCR test is not available for everybody. You cannot PCR test thousands of people every day, but you can x-ray thousands of people every day. You can do an SPO2 test for many people. So this is a screening or a triaging mechanism that is being used in Mumbai right now. This has also been used in other parts of the world. Just talking about ways our algorithm is being used. Again, because we can mark out where the abnormalities are on the lung, you can use that to measure progression of COVID-19. This is being used in Europe, in multiple sites, UK, Italy and so on. So this is the kind of report that our algorithm generates, all automatically created within a minute. And this is also an interesting use case. Talking about what we do. I mean if you look at chest x-rays out of 100 chest x-rays, about 60, 70, maybe 80 are normal. And the rest are abnormal. And if you can automatically, so the normal x-rays will always have the same report. I mean all the different parts of the x-ray are normal, your lung is normal, your bones are normal, ribs are normal and so on. So this is a radiology customer in Southeast Asia. And what we are doing is we are reporting all the normal cases for them, which can then be quickly reported by their radiology technician. So out of 100, we are able to identify about 68.4% as normal. And that is auto-reported by the algorithm without a lot of work from the radiology team. So and these reports can also be released very quickly. So within one minute, we can report and then within five minutes, they are able to report these scans. So substantially speeding up reporting. And there are places where we are completely auto-reporting chest x-rays, where this is a fully automated report created by the algorithm, which is pending radiology signature. They can edit this report and then give it out to people. So we can create a fully automated text report for patients. The other solution that I wanted to speak about is our head CT scan solution, which can detect bleeds, fractures, stroke, basically trauma and stroke, bleeds and stroke. And this is deployed in several sites. I'm talking about one site in Kerala where they don't have radiologists throughout the day. So what we do is we receive these scans automatically from the CT scanner. Whenever we have a critical scan, we will receive these scans on our cloud server and we process them within a minute. And if we identify a scan which has got a critical abnormality, that will be sent to the radiologist phone as a telegram message here but also as a WhatsApp message. And they can report on that immediately saving probably hours of time for a patient which could mean life and death for that patient. If you're able to report on a stroke or a trauma case, half an hour earlier that is life and death for a trauma or stroke patient. So that's what we do. I mean, we are in about 110 plus sites in about 28 countries. As of today, we are crossing on an average about 10,000 plus cases on a daily basis. So quick summary of your... Thank you so much for the presentation. A quick request to our attendees who are here. If you have any questions for our panelists, you can post it through the discussion box on the right-hand side of your panel and we'll take up the questions post the... Can we have all of us on the stage please? And if I can request, Ajit, if you can please tell us the innovations, technological innovations that you have done to provide... I didn't find that you would win. Sure, absolutely. If you can give the presentation please. Can you see my screen? Yes, please. Okay, so my name is Ajit. I'm the founding member and CTO of MFINE. We are a primary healthcare company. Our goal is to provide quality healthcare on demand. So that's what we are set out to do. What are we trying to solve? If you look at primary and secondary healthcare today in India, the delivery of it, I think there are two fundamental problems with it. One is that quality care and quality doctors are very far in view. There is one specialist doctor for around 5,000 people. There are the reach of these doctors. The good doctors are limited to their geographies and you have pretty long travel times and wait times if you want to get to see a doctor. And if you talk about specialists, that gets even more harder. The delivery process itself is completely broken. So if you look at it, quality care itself is a questionable thing. There are bad health decisions being done like self-medication, substandard treatments. There are no protocols that sort of exist and have been followed across the entire country. There is no text standardization at all to deliver quality at scale. All of this, right? So these are two core problems that actually dog the Indian healthcare system. And we wanted to come and solve that. So our solution is the following. So we sort of build the network of the best care providers in the country. So if you look at the top doctors, how do you scale them infinitely to actually to serve all the demand of the country is what we are looking at. So you take the best of the best doctors. You use their consultations and whatever they do as a forms to form a data engine. Meaning you learn from these good doctors. You replicate that using sort of formulating a virtual doctor. In some sense, you create a digital twin of these great doctors. And then you scale that over mobile so that it's accessible to most of the country. So that's what we're trying to do in M-Find. So we are an AI-driven virtual hospital. So we aggregate the best doctors and the best care providers of the country. So for consumers, it becomes instant virtual visit to the hospital from the comfort of their homes. They have access to almost 30 specialties on their phone. We offer at home services for diagnostics and medicines and so on. And of course, our AI helps in giving them digital tools for them to stay healthy. And for hospitals, of course, it's a no capital, easy scale mechanism for them to actually reach out to maximum number of patients as possible who need that care. Our core product is built on AI. So we have a learning system which understands deep medical knowledge. So it understands the semantics across these 30 specialties that are talked about. We have treatment protocols that is built and diagnosis algorithms that we have learned from these doctors over a period of time, right? In the three years that existed in the industry today. And it manifests to the consumers an extremely fast, efficient way to meet the doctors and it manifests to the doctors in a way that it makes them extremely efficient on the platform. So what used to take around 30, 40 minutes in a face-to-face visit should not take more than five minutes because the system captures all of that information, is able to break it down, do a triaging, do emergency analysis, define the treatment plans and so on and so forth that doctors can easily make those decisions, right? So that's what we are building out. So today we cover close to around, you know, thousand-dots symptoms and findings that we can do automated. We diagnose around 1200 conditions with close to around 85, 90% accuracy between that and we have hundreds of templates that we have learned from these doctors that makes the whole process very fast. Just to remind you, this AI is assistive in nature. It is not directly to consumer, of course, right? It is helping the doctors to become extremely efficient and that's how we bridge the scale difference between the number of patients in the country and the very fragile and few, you know, the care ecosystem that exists in the country and that's the goal that we are after. That's it from me. Yeah, thank you Ajit. Thank you. That was really insightful. If I can ask Dr. Geetha to explain what Mirama is actually doing in terms of technology and if you can give her the speech, please. Sure. Can you see my screen? Yes, we can see yours. Hi all. This is great to be here. I'm basically going to talk about a bit about what we are doing at our start-up for Mirama. Mirama stands for being healthy in Sanskrit. That's what it means. And it also expands non-invasive risk assessment through machine artificial intelligence. That's exactly what we do. We use AI to protect health risks in a non-invasive way. And, you know, just to sort of give the context of where we started and, you know, given the COVID screen and just give you a very simple and brief glimpse of what we're doing using AI for fighting cancer. That's where we started off with basically half a million people every year due to risk cancer. And this is due to a lot of issues that exist today. There is no good test for women in the 45 years of age. There's what's called dense breast. There are more than 50% of the people, even above 45 years, you know, are not able to detect early cancer using mammography, which is the de facto standard and so on, so forth. Of course, affordable access to the experimentative care, early detection, very, very difficult in countries like India. So this is our whole scheme of things in the context. What we came up with is a new solution for protecting breast cancer to start with. So this is basically a privacy aware early stage breast cancer detection solution where we just use a thermal imaging device, measure the temperature variations on the chest and then use machine learning and AI algorithms to detect and generate a report. You know, like I mentioned, it is for a doctor to sort of review it and then make a final decision. But yes, we do this completely automatically. And of course, when we go to rural areas and all that when doctors are not around, this can be used as a triaging device. Now what does this do and what are the advantages? Very quickly, first and foremost, most people detect breast cancer using hand, which is accidentally feeding a lump, which is about 25 millimeters in size, which is stage 3 or stage 4. We are able to detect 4 and 5 mm of cancer, you know, much, much before a lump is felt. So many, many years before. And so early detection has better treatment efficacy, as you know, and also most importantly, even after a surgery, the lady can live 30, 40 years without much recurrence because it's very, very early sort of nipping the, but kind of a, you know, situation that we bring in. Another most important part of this is because it's completely non-contact, non-invasive. We say it is no touch, no seed. That is, it's really like a changing room experience. The lady comes in and she goes out into a small, she goes into a boot for, she stays there for 10 minutes and she goes out, her report will be ready. So basically nobody would have seen her, nobody would have touched her, completely sort of, you know, automated environment around her, which again, of course, it's controlled from outside, but still, you know, she feels really private and nice and more women really love this to actually get her breast training done. And of course, there are a lot of other advantages which we'll talk about later. The key technology, the key technology that we have developed here is what we call as thermolytex, which is a combination of thermography or what's called as using infrared imaging with machine learning. So essentially, this is about deep learning and machine learning and all of these wonderful stuff which basically crunch the 400,000 temperature points per person that we measure and then generate a full-fledged 3-page PDF report with all of these explainable features, explainable, you know, like a buy-right scoring, which is what is very commonly used by radiologists and so on, so forth. All of this is done within, like, you know, testing the preparation, everything is done within 15 minutes and this is what is the value that people are seeing and this can be done not just in hospital, it can be done outside and many other places. And of course, this is innovative, proven by our nine granted US patents so far in this technology alone. Now, of course, it's a computer-aided diagnostics and then when we face this situation of COVID which never ever anyone on this world or planet has actually seen such a, you know, challenge and response to the world, we said, what can, as technologies, as innovators, what can we do to this, right, to save this? And then we said, we're using thermal imaging and what can we do differently? So, given that there is this huge lockdown of India and people are coming back and we all know that at least, you know, we need to catch symptoms like fever and respiratory abnormalities before a person gets into a community kind of a setting. So we've come up with another solution very recently called, you know, my fever test, which is a fully automated community screening solution. You know, we can talk about this a lot more. There's interest in contact, but basically the idea is as in when people come in, there's like face detection, you know, several other features, you know, it enables you to accurately determine the temperature of every person and do a log and also be able to detect respiratory abnormalities and all this while the person is just walking by and obviously enables compliance as well. So, you know, this, of course, has been used in the entrance of buildings and what's interesting is that like 600 people were screened in a mass screening like this as well and the same technology has also been integrated into Mitra Robo from Invento. So many of you would have seen this, you know, viral video where it was installed in Fortes and you know, this is Robo which does the full screening and the complete fever test is integrated into it. There's a lot more we want to talk about, but you know, we have received excellent, you know, information and support from media and other, you know, ecosystems such as yourself and what I want to live with you is that NERMA is the only Indian startup in the top 100 AI startups in the world and we are very proud to put India on the AI world map, okay? Thanks a lot and happy to answer any questions. Thank you, Geetha and we really appreciate, you know, what NERMA is doing. It's a very common thing that, you know, we don't know but we need to be very aware about it. Thank you so much. Dr. Redham, if I can come to you and, you know, if I can ask you that if you can explain to our audience the technologies that are being used at Mitra. Just before you start, I'd like to request our attendees to, you know, post their questions if there are any for the panelists. If it is directed to someone who mentioned the question we'll take them up post the presentation. Thank you so much. Go ahead, Dr. Redham. Can we give the stage to Dr. Redham? So we are basically at the intersection of genomics and data and we try to use the genomic data using various algorithms, bioinformatics pipelines and make interpretations. So I'll try to make this very simple here. So every living organism has a source code. The source code is either in the form of what we call DNA or RNA. So whether it is a small virus or complex living beings like humans, we all have cells inside the body. We are made of three trillion cells and each cell has a nucleus. Inside that there is a source code which we call DNA. And this DNA has some specific chemical entities which we call nucleotides. It could be a denying time in cytosynagionin. So this source code dictates the whole of life as to how whether the individual succumbs to a disease or is susceptible to a disease or how his health is, what is his longevity. Every aspect of the disease starting from pure genetic diseases to as complex diseases like COVID infection whether an individual will get a severe infection or a mild asymptomatic infection are driven deep rooted into our genes and the source code. So what we do at Medginome is our daily job is to sequence these genes most of the times in humans, individuals in the context of diseases. It could be a rare inherited disease or it could be a cancer. It could be breast cancer, colorectal cancer or any other tumor. It could be leukemias or it could be even bacteria or viruses and sequencing trying to understand them. So this source code is not small. For example, if you take humans, we have three billion letters in each cell. In each cell has three gigabases of data. Today we talk biology in terms of data. So we have three billion letters sequencing from each individual which when we sequence several number of times it turns out to be about anywhere between 150 to 200 gigs of data from each human sample that is sequenced here. Once we sequence, there's a huge amount of information. We have to correlate in the context of disease, specific disease as to why this individual is getting this disease or in the context of treatment and management as to whether this particular situation or a disease or a tumor can be treated with specific drugs or can be managed in slightly different ways. The applications are huge as far as the medical genomics is concerned. It can be prediction. It can be prevention. It can be treatment management starting from all stages of life starts from what we call, if you are trying to do a population surveillance as to in India how many individuals are susceptible for certain rare diseases to have simple trivial things like whether this particular COVID virus in an infected individual is coming from Europe or coming from China. So you can trail them. You can do a lot of surveillance. You can do phylogenetic analysis. You can identify individuals as to what exact precise disease they have, solve the diagnostic or disease. We can give clinicians valuable information day-to-day basis as to how they can treat an individual much precisely and much better than the conventional method that has been there. So this whole thing, what I summarized so far, happens every day, every week, 24 by 7 almost. From the time we sequence the data from a biological entity, it could be a blood sample. It could be a saliva. It could be a tumor. It could be any other sample. From that, we sequence this data. And we have complex algorithms, bioinformatics pipelines. Many of them are proprietary. A couple of them are patented. And we also use a lot of public tools. We have a large team of about 120 bioinformatics experts who piece out this data. And finally, the source code of that individual is given to our analysts. These analysts will piece out and do the interpretation, which is the most challenging thing. Use a couple of AI tools and machine learning tools. And finally, identify that particular defect, one specific or two specific defects in the source code as to why an individual has this disease, a particular disease and help the clinician with the diagnostics and also help them in adding in better treatment and management. So this is what we do as a part of it. But apart from this, we also discover new biology, new entities, health, various pharma in early discovery of drug molecules, targets. And we also work as a clinical research organization with various entities trying to help them solve their problems. So we are there again to come back. We are at the intersection of genomics data and big data biology. Thank you, Dr. Wiedem. Thank you so much. Just to follow on, can you, for the benefit of our audience, you can just tell us one use case that we all would know, may not know, but you know, which, you know, your kind of solutions actually has helped, you know, the novel thing, ordinary combination. Yeah. So I can give you two examples. One, I will take a non-oncology disease. For example, there are several rare inherited diseases which happen in populations. And especially the demography of India is pretty large. India and China, we have several, about 60 to 70 million new cases every year with rare inherited genetic diseases. And most of these diseases do not have any treatment. They have high morbidity. Some of them die in the early part of their life and they cause both social and mental burden for the families. So, and another challenge is in countries like India, especially if you take the southern part of the country, there is a practice of what we call consignuity which increases the probability of rare inherited diseases. So we have a solution. We, what we call it as a carrier testing where we sequence a couple before marriage or before conception. And most of the times these families or these couples either had a disease burden in their family already or their first kid already had a genetic disease. So they don't know why this has come, what is the problem, exactly what is the mutation and what is the disease and how they can preemptively know beforehand whether the next kid is going to get this disease or not. So in these situations what we do is we screen the couple. We sequence the genomes basically and slice and dice with the data and there are a lot of clinical information that gets into this and we identify one particular defective gene which is basically causing this disease because in resistive inherited diseases that terminology we call where the couple have 50% of the genetic defect but they don't manifest the disease but the kids when they get 100% defect they get a very nasty disease. So we identify this 50% of the disease in the kids. We had several individuals in fact every day we do anywhere between 150 to 200 patients or couples we screen there identify that particular mutation which is causing this disease and inform their clinical practitioner and also do a post test counseling so that when the couple go for a next child we can get the fetal sample and sequence this fetal sample and identify whether that particular mutation is there present in that fetus to be born kid and if it is there depending upon the severity of the disease they opt for selective medical termination of pregnancies or if it is a mild disease they get up for the better management treatment the moment the baby is born. So this is something one of the common ones that we do. The second example which I would like to give you is a more common oncology setup is today lung cancers in India and in the rest of the world also no medical oncology treats the lung cancers without doing the genetic testing or what we call molecular testing because 80% of the therapies today are what we call targeted therapies and very personalized precision medicine where an individual's genetic profile needs to be known or a tumor profile needs to be known before giving a treatment so if gene A is mutated in that particular lung cancer tumor then they give a drug A if gene A B is mutated they give a drug B and the response to the overall survival the prognosis is completely different compared to not doing a genetic testing and giving a targeted therapies so today it has become a well established norm across the governing bodies or you take societies or the lung cancer societies that every medical oncologist who treats a lung cancer especially the non-small cell lung cancer have to test for at least three to four important genes in the tumor before which it is not considered as a basic norm to start a treatment so this is creeping into practice of medicine very fast and certain specialties it has become as a standard becomes something like MRI or PET scan you do routinely if you take pediatric neurology or if you take even some of the oncology setups like lung, breast, ovarian it's becoming very common only one thing is the awareness is still low even in the clinical specialties I think it's growing fast because more and more actionable things are happening based on this information we hope in the next couple of two to three years it's going to be at massive proportions but people will use this technology for the better time Thank you Dr. Widman, thank you that is really insightful things which otherwise people do not know so I'll now request Ryan if you can come and we can give the stage to Ryan quickly take a minute to explain to let us know audience what you do and since we have questions coming in already so I think I'll also pass on one question that is coming from Neha from our audience and she says that in the healthcare industry we are seeing increasing adoption of technology whether AI robotics, blockchain that many of companies are adopting so what is your opinion in terms of the longevity or the future of such technologies in a post pandemic world where social distance distancing norms may eventually fade can we have Ryan on the stage please Do I need to do something? Yeah please so if you can just start off with what investments you do and introduce yourself and then as I said we have a question which has come in so I thought I'll just pass it on to you so Neha has asked what is your opinion in terms of the longevity or the future of adoption of technologies in a post pandemic world where social distancing norms may eventually fade Yeah I mean so we think and we certainly hope given that this is one of our investment focus areas that the technologies that we're seeing being developed now whether actually already in development that the adoption of which is being accelerated during this crisis out of necessity continues and that it really just accelerates the adoption of these things so you know tele-consultation is a good example that we were talking about a little bit earlier and that it's been around for a while and had seen varying degrees of success around the world but now I think in the last few months certainly I've used tele-consultation a few times and have had medicine delivered to my house and lots of people are feeling that is quite a benefit so I think I mean even I'm sitting in Singapore I'm not very far from a doctor and it doesn't take me very long to get there have the appointment get my medicine and leave and I didn't really use tele-consultation platforms before for that reason but now having been basically forced to use it while we've been on lockdown then I would certainly use that again in the future what's you know there's no need for me to physically go and see a doctor you know if they're able to deal with something over a video call if they're not and they request that I go in then obviously you have to go in but I think we'll see that hopefully play out across the whole ecosystem in digital health where you've got everything that we've looked at today whether it's diagnostics, remote patient monitoring, digital therapeutics I mean these are all things that hopefully unfortunately it's a shame it's taken this current situation to see these things getting adopted but if we see those things being adopted faster at the end of the day they help provide better access to and quality of treatment for patients and it saves time for clinicians and it saves the cost to whether it's the employer or the patient or insurance companies paying the bill that just seems to be something that's irreversible Okay, okay So I would request to put everyone in the state so we have a lot of questions coming in so you know I would really want to pick up from there so you know so as I see that there are a lot of you know that is happening and you know this is who is of course the customer to the users of course so there's a question by Dr. Avi Naha he wants to know that what is the response of medical consultants when these technologies are introduced to them you know he also specifically mentions that while we are struggling with basic healthcare infrastructure in India how adaptive there's a medical fraternity to the esoteric and special such as genomics so you know Ajit you can start with you and then who are those I think in some sense there is the answer also in the question the point is today in India the healthcare system is broken right and in some sense these technologies actually come to help that very situation so if you just look at it even in covid if you just look at how you know even in advanced countries what field was the centralized healthcare system right lack of beds lack of you know the experts at the points where things had to break apart right I mean if you saw in Italy or the US and so on and so forth so I think the centralized ecosystems and healthcare you know will have a breaking point and in India well that's a even worser problem I mean we don't have access to healthcare in many cases and access to good doctors and so on and so forth so I believe the technologies that we are all building and I think everybody in the panel here helps to ease the burden on the healthcare system already broken and fragile systems is what we are helping to solve where it is a case of M fine where we are saying look at the time that a doctor needs to spend to really come to a conclusion on something is reduced because a lot of technologies want to help you in that or whether it is automated ways in which you know a chest can be screened or the breast scans can be done these are technologies that are helping the system as it is today so I think they will see increased adoption I think in some sense the situation today has brought that to light and very clearly at least in our case we can see a lot of acceptance to this especially if it is accessible in nature I think we have seen doctors and consumers lap it up you know very well that is my observation Ryan how is it what has been your observation you know in terms of medical consultants you know accepting these new technologies newer innovations that are happening yeah I think I guess a little bit similar to the comment I had to the person who asked the question we are seeing an openness both from consumers and clinicians and frankly everybody across the healthcare value chain to adopt these technologies because as I just said the burden on the system is too much for anybody so it is kind of being forced through the current situation but I think ultimately will result in much better outcomes and we have seen that again through everything from just sort of relatively straightforward tele-consultation to the diagnostic solutions and remote monitoring things that you have heard from some of the other panel members okay okay so you know we have we are not out of time but we have a couple of questions so I am not letting them go before I will let you answer this question Abhishek Thakur asks that how do you see the future of personalized medicine affordability Prashant if I can come to you you know you providing these X-rays and everything so what happens to affordability because there is a cost attached to what you are doing so I mean in reality I mean what we are doing is reducing cost substantially I mean and lots of places we are improving the productivity of radiologists by 30-40% and if you look at radiology salaries across the world I mean in the US an average radiologist makes about close to half a million dollars I mean 400-500k a year and the cost of radiologists can just X-rays is about 15-20 dollars, HAT is about 50 dollars and that I mean of course the numbers are smaller in India but the scale is very similar and even in India radiology is one of the most wanted I mean I think doctors who do the MDs prefer radiology because that is a well-paid profession right so what we are doing is I mean making it cheaper for individuals I mean as we are reading this improving productivity, improving accuracy and making radiology more affordable and accessible and affordability is one part the other part is that we can bring it to any part of the country right we have gone to really really small towns in Malawi I mean which is we could not have had radiology access we have provided that even in India we have gone to rural areas like Baran where quality radiology access is always a problem and we can bring that so my feeling is that AI and these deep technologies will only improve affordability and accessibility and they are not going to increase the cost for patients Okay Dr. Gita do you think it holds true for across not only radiology but others wherever there is technology coming in of course there is a cost attached to it there is will it personalize medical treatment for the country yeah so Prashant already talked about the radiology part from a personalized medicine I would say although I am not an expert but clearly what we should see is the value that it actually brings right by personalizing medicine to a particular user you are instead of trying like three four different treatments right you are just saying instead of the three treatments we are telling you which among the three is most useful for you and then doing that first right so if you look at the long life cycle of the treatment itself it is actually saving cost for them at the end of the day even if you are paying a little bit more for the analysis of figuring out which treatment is and also it is all about you know there is so much data available and so the doctors are also overwhelmed with this sort of huge data that is available with them so making the right decision for the first time like which treatment that needs to be done you know requires this crunching you know on a second type of thing and that is again where technology can help and enable the doctor to make the right kind of decisions for example treatment planning is one such application that one could be where you would say okay for this treatment what is the probability of success for this treatment what it is for this particular person knowing all the personalized data about the person right so that definitely helps in two ways though there is a small markup probably for the additional analysis that is done in the long run it saves the you know person from trying different treatments just from an economic perspective that itself is a huge benefit in addition obviously it has better treatment efficacy that's the reason why we are personalizing it so I think all these technologies that we are talking about that we talked about earlier also are going towards you know in some way or the other making you know affordable high quality healthcare possible at scale right you know to all you know thank you thank you Dr. Gita so we've run out of time and I have to give the attendees time to take the networking that we have followed so thank you everyone so much it was it was insightful for me as well you to know that you know there are so many things that you know when we when we you know subscribe to our service you can understand that what was behind it to make that bring us and what's the what are the intricacies that have been used by startups and doctors and investors so thank you everyone so much and wish that we had more time to continue this session but I love to give attendees a little thank you so much I hope to see you all again sometime and for attendees this is our networking art so you know we assume our stage 3 at 1.45 p.m. in the time so please feel free to contact other panelists and whoever is available on the stages on different stages that means you thank you so much