ERR — broadcast
20260920 173000 UTC
502 transcript segments
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[00:00:17] take, we'll put electrodes on, yes, how
[00:00:21] is modern cutting-edge science done? The most
[00:00:24] accurate answer to this question is to try it out yourself, everyone knows.
[00:00:28] järele proovida, kõik teab.
[00:00:34] At the Institute, in these laboratories,
[00:00:36] the smartest solutions are being developed on how to use
[00:00:40] smart devices and the data
[00:00:42] contained in them in medicine. We are testing one such today.
[00:00:45] The purpose of this study is
[00:00:49] to validate these smartwatches and
[00:00:52] three parameters: pulse frequency,
[00:00:56] number of steps, and energy expenditure. How accurately these watches measure these.
[00:01:00] parameters, do I understand correctly that you suspect that these
[00:01:04] watches are inaccurate? No, we don't suspect they are inaccurate, we
[00:01:07] actually want to know how accurate they are. Smartwatch manufacturers
[00:01:11] are very different and we are not quite sure how
[00:01:14] reliable one or another watch is,
[00:01:18] so here we take watches from different companies,
[00:01:21] we put
[00:01:24] them on the test subject, on their arm, and then
[00:01:28] either within a couple of hours or...
[00:01:30] or in some cases within 24 hours, we collect
[00:01:33] the data that comes
[00:01:37] from the watch and then compare it with our so-called
[00:01:40] gold standard, i.e., steps taken on a treadmill and heart rate
[00:01:44] measured by ECG. Wires
[00:01:47] attached, oxygen mask on, and we begin
[00:01:51] the scientific experiment. So, do you feel
[00:01:53] normal? As much as one can feel
[00:01:57] normal in this situation, yes. Following
[00:02:00] the instructions of Institute researcher Kristjan Pilt and his colleagues, I perform
[00:02:04] routine activities, I walk, I run,
[00:02:07] I lie down, I ride a bike, I solve
[00:02:10] puzzles,
[00:02:12] mhm, so that was a three-year-old's, yeah,
[00:02:16] yeah, yeah,
[00:02:18] mhm, and I even clean, and we have
[00:02:22] here on the floor a bit of
[00:02:25] paper from a paper shredder, which needs to be swept up with a broom,
[00:02:29] and then if... once you've swept it up with a broom, then you have to sweep it apart again
[00:02:32] and then sweep it together again. This is indeed
[00:02:35] such a fruitless task, but the important thing is the process you're
[00:02:39] doing, so it took two minutes again, okay. At the same
[00:02:42] time, the smartwatch records various data
[00:02:46] about my health.
[00:02:48] What is the bigger goal of this? Why are you
[00:02:52] doing this? The bigger goal would be that
[00:02:55] people, who daily wear their
[00:02:59] pulse. so already collect health data
[00:03:02] about themselves, that they would be able to
[00:03:05] transmit it to the health portal,
[00:03:08] and if they then, for example, go to a family doctor or
[00:03:12] to an occupational health doctor, then these
[00:03:15] doctors would already be able to foresee what the
[00:03:19] dynamics in these parameters have been,
[00:03:22] and these doctors would not then have to
[00:03:25] send these people for initial examinations, but in fact, the examination would already have been performed by these
[00:03:29] people themselves. One could say the philosophical
[00:03:31] background
[00:03:34] in the medical sector or healthcare sector is very similar
[00:03:38] to other service sectors, and
[00:03:40] if we think back twenty years ago, that
[00:03:44] when I went to the shop and asked from behind the counter, "Give
[00:03:47] me that bag of sugar and that pack of
[00:03:50] butter," then that time is irreversibly gone. Today, I'm
[00:03:54] on an airplane, I've had to buy the tickets myself, I
[00:03:58] have to check in my own luggage... No one helps
[00:04:01] me anymore, and the same trend
[00:04:04] is in healthcare, that we have to give
[00:04:07] people more tasks to deal with their
[00:04:10] own health. Professor Peeter Ross explains that in the
[00:04:14] future, our smartwatches will be connected to Estonia's national
[00:04:17] health information system. If this succeeds, it will save
[00:04:20] a huge amount of resources. We can
[00:04:24] collect data now
[00:04:26] in the smart age, not once a day and not
[00:04:30] once an hour, but practically once
[00:04:32] a second, which means that we
[00:04:36] get a broad
[00:04:38] and comprehensive overview
[00:04:42] of a person's health, and we can also
[00:04:46] get this data throughout their life, and not only when
[00:04:49] I go to the
[00:04:53] doctor, who has a white coat on, and my
[00:04:56] blood pressure is already rising and my blood pressure is already rising. The point
[00:05:00] is that if currently repeated specialist visits burden
[00:05:03] the medical system, then in the future
[00:05:07] some appointments could be canceled and instead, people would monitor
[00:05:11] themselves at home. themselves, or rather, the smartwatch
[00:05:15] would do it for them. This way, doctors' valuable
[00:05:18] working time can be saved, and in total, millions
[00:05:21] of euros. Do you mean that this very smartwatch on
[00:05:25] my wrist, that this device will diagnose me
[00:05:28] in the future and tell me when I should go to the
[00:05:32] doctor? I would be very cautious with the word
[00:05:35] "diagnose," but it will give advice. It will certainly give
[00:05:38] advice. Well, I think a lot of
[00:05:42] it already gives advice, that if by the end of the day
[00:05:46] you've only taken 500 steps, then you should go out
[00:05:50] for a walk, and that is the simplest
[00:05:53] example. But what we do with these
[00:05:57] studies is we look at these daily patterns,
[00:06:00] how the pulse quickens, how much a person walks at different times during the day,
[00:06:04] and based on that, over a relatively
[00:06:07] longer period of time, we hope
[00:06:11] to be able to predict when... when a person's health
[00:06:14] starts to deteriorate, not specifically to diagnose, but
[00:06:18] then, if activity decreases,
[00:06:21] for some reason the pulse quickens in unusual situations,
[00:06:25] then there would be advice through the
[00:06:29] smartphone, through an avatar, that you should go
[00:06:33] see a family nurse or an occupational health doctor, or
[00:06:36] such prevention is the goal.
[00:06:39] What diseases the smartwatch literally
[00:06:42] detected in such a way in the early stages in practice in the future?
[00:06:46] What is the challenge of our society
[00:06:50] is obesity and lack of movement,
[00:06:54] and from this non-communicable
[00:06:58] diseases arise,
[00:07:01] which are a great burden both for individuals and society, namely cardiovascular
[00:07:05] diseases, diabetes, various mental health disorders,
[00:07:09] so the range is very wide, just like I..."
[00:07:12] "have been tested today by more than 80
[00:07:15] test subjects. Although a larger analysis is still ahead, major
[00:07:19] and minor deviations have already been identified, where
[00:07:22] makes a mistake and for example
[00:07:25] miscalculates the amount of energy expended, but other
[00:07:29] things have also emerged during the studies, yes, we had various
[00:07:33] things happen here, that for some
[00:07:37] subjects we detected arrhythmias from the ECG signal and we
[00:07:40] advised them to contact..." their family doctor so that they could
[00:07:44] get advice and see a cardiologist and
[00:07:48] see what was wrong with their heart
[00:07:51] function, and some subjects also
[00:07:54] left some activities unfinished which were simply a little
[00:07:58] too overwhelming for them, that there's nothing to do, our goal was
[00:08:02] not to complete the performance, so to speak, but to still monitor
[00:08:06] your body and stop at the moment when
[00:08:09] your feeling tells you to.
[00:08:29] Institute of Molecular and Cell Biology show how
[00:08:33] many hard drives worth of information are needed to conduct
[00:08:36] one modern scientific study. Tartu
[00:08:39] University researchers, led by bioinformatics associate
[00:08:42] professor Kaur Alasoo, are mapping, with the help of data from more than half
[00:08:46] a million people, how genes affect our
[00:08:49] metabolic processes. Our main interest is
[00:08:53] how we can generally develop better,
[00:08:56] faster, and more effective medicines. What is the current problem?
[00:09:00] Why does drug development often fail? Firstly, it's because
[00:09:03] pharmaceutical companies are very good at making these small molecules, but then they go
[00:09:07] into clinical trials, and it turns out that the molecule works well, but the protein was
[00:09:10] wrong, meaning the target. The drug's target that we want to influence was wrong,
[00:09:14] and that's why it doesn't work, for example. Or the other problem is
[00:09:18] that it might work, but it has many other
[00:09:21] side effects, it's not safe. Often
[00:09:25] it is believed that every gene in our body performs only one specific
[00:09:28] task. Alasoo and his colleagues' study, however, proved
[00:09:32] that one gene can simultaneously affect hundreds of compounds and
[00:09:35] molecules. In practice,
[00:09:38] this means that if you try to suppress only one indicator with a drug at a time,
[00:09:42] the person may still not get well. What do you
[00:09:46] do then? We try to find these
[00:09:49] so-called targets within cells, to find targets that
[00:09:52] can be targeted with drugs. A human cell is like a Lego house, which
[00:09:55] consists of blocks, the blocks are so-called proteins, and then
[00:09:59] different blocks have different functions, that is, some are made like
[00:10:02] walls, some might correspond to then some are made into windows,
[00:10:06] doors, a roof, and so on. For example, if we want to make it so that there is more
[00:10:10] light in the house, that if our problem is... that the disease is that it's too dark
[00:10:14] in the room, then how to get more light, then we would try
[00:10:17] to find which proteins we should influence so that there is more
[00:10:21] light, if we think about that house, no matter which block we remove,
[00:10:25] then as a result there is more light in the house, and there is most light when we
[00:10:29] only remove the roof, then there is a tremendous amount of
[00:10:32] light, but now rain comes in, for example, yes, so
[00:10:36] that is precisely the problem, isn't it, that we get a side effect, that there is a lot
[00:10:39] of light, but rain comes in and it's cold too, that is,
[00:10:43] maybe medicating, it's not such a good idea to target
[00:10:46] the roof. Tartu researchers studied the genetic
[00:10:49] data of more than 600,000 people
[00:10:53] based on information from the Estonian Gene Bank and the UK
[00:10:56] Biobank, and compiled, figuratively speaking, the necessary
[00:11:00] instructions for assembling a Lego house, which helps
[00:11:03] to distinguish the true causes of diseases from accompanying
[00:11:06] symptoms. Where does artificial intelligence come into play? We
[00:11:09] actually would like
[00:11:12] to have an artificial intelligence model that could
[00:11:16] predict for us what
[00:11:19] a specific genetic variant
[00:11:22] does, if we change it. I don't know, on the first
[00:11:25] chromosome, position 1 million 321 to a
[00:11:29] C, firstly, if anything happens at all, and if
[00:11:32] it does, what happens, that is,
[00:11:36] we would like the model to be able to predict this for us, not that we have to do an experiment and measure
[00:11:40] every time we want to know it. Firstly, the model predicts, and secondly, the model can also
[00:11:44] tell us why it predicts, meaning the model basically explains
[00:11:47] biology to us. What help is this to an ordinary person?
[00:11:50] How does this help me if we have such good
[00:11:54] models? We can, as a whole society, develop
[00:11:58] medicines more quickly and efficiently,
[00:12:01] and then with that, either treat or
[00:12:04] prevent various diseases. On the one hand, it helps to make drug development
[00:12:08] significantly faster, cheaper and more efficient. On the other
[00:12:12] hand, it would create an opportunity to focus on new diseases,
[00:12:15] the development of drugs for which has not previously been considered
[00:12:19] reasonable for economic reasons. For example, rare diseases, or especially
[00:12:22] rare diseases, where drug development
[00:12:25] starts from the actual needs of each patient,
[00:12:28] we need to find that needle in the haystack, how to find that one change among
[00:12:32] the three billion. In reality, the difference between two people is a million, we have to find
[00:12:36] the one that causes the disease, and that is where, if we have very good
[00:12:40] models that can predict what each
[00:12:43] genetic difference does, then it is significantly easier to find
[00:12:46] these variants, and then we can also think about what the therapy could
[00:12:50] be.
[00:12:53] In fact, in the Estonian medical system, there are several
[00:12:56] areas where artificial intelligence has already gained
[00:12:59] a foothold. In Estonia's largest North Estonian
[00:13:02] Regional Hospital, intelligent machines work together with doctors
[00:13:06] on a daily basis. It
[00:13:09] is warm at first, don't be scared, you can close your
[00:13:11] eyes, it's more comfortable. We'll close
[00:13:15] the clips and I'll make breathing free immediately.
[00:13:19] Mhm. Okay, I'll come closer with my fingers.
[00:13:21] ligi.
[00:13:26] One place where artificial intelligence assists doctors
[00:13:29] is radiation therapy. The artificial intelligence tool precisely
[00:13:33] marks the contours on the image, i.e., where the disease is located and where
[00:13:36] the healthy organs are. This way,
[00:13:40] doctors can direct the treatment to the right place and better
[00:13:44] protect healthy body parts.
[00:13:47] Here you can see, well, I think there are a couple of hundred
[00:13:51] contours here, which are all
[00:13:55] structured. Of course, not everything is always needed, it depends on where
[00:13:59] the tumor is located. So in that area,
[00:14:02] it is already known what the risk organs are, then
[00:14:05] the radiologist technician must contour them before
[00:14:08] the doctor starts contouring
[00:14:11] the tumor volume. He has already done that work, yes, he has
[00:14:15] done that work. How fast is it? It takes a few minutes, so
[00:14:19] not long. For example, here there are 380
[00:14:23] layers, so it's... layers taken from the body in axial
[00:14:27] sections. Here you can also see them in other planes, here
[00:14:30] these layers are in the coronal
[00:14:34] plane. Simply put, if it's the prostate area, then we need to
[00:14:38] contour the rectum,
[00:14:41] the sigmoid colon, then we need to do
[00:14:44] the femoral heads, the bladder,
[00:14:47] and sometimes also the bowel bag, that is, the area that includes all the bowels,
[00:14:51] like basically here, you know. So
[00:14:54] everything that is in the human body can be
[00:14:56] contoured.
[00:14:59] How big is the role of this artificial intelligence in your work today,
[00:15:03] that it is basically a full-time
[00:15:06] assistant for you. Yes, it is daily, that
[00:15:09] basically all patients who come to us for a
[00:15:13] CT scan, who come to us for treatment, we use
[00:15:16] AI for all of them, so it's a standard that we
[00:15:20] take these AI contours and we
[00:15:22] then correct those contours. according to what we
[00:15:26] need, if it has done something a little wrong somewhere, then we correct
[00:15:30] it, we cannot leave
[00:15:33] it so that we don't check a single contour, we still check all contours
[00:15:36] in the relevant area. How much does AI make mistakes,
[00:15:40] how much does it give results that are not true? I can't
[00:15:44] really say numerically,
[00:15:47] but as for contours, there are contours that I
[00:15:51] practically don't have to correct, but there
[00:15:53] are contours that I have to correct a lot, or then do
[00:15:57] them myself from start to finish, perhaps, because it might be more convenient and faster for me
[00:16:01] to do it myself than if AI has done it. It
[00:16:04] all depends on the patient's anatomy. If
[00:16:08] the anatomy is unusual, for example, if the patient has been operated on,
[00:16:12] then the anatomy has already changed due to that, and
[00:16:15] AI may not
[00:16:17] detect it as well.
[00:16:19] Have you measured how great the effect of this artificial intelligence
[00:16:23] is, how much time you save or how much human work you save
[00:16:27] with it? I can confidently say that it saves at least
[00:16:30] about 50% of time on
[00:16:33] the contouring part. Generally, I can say that once...
[00:16:38] if the images, i.e., the CT scans, are taken with a very thin layer,
[00:16:41] then it could take three to four hours
[00:16:45] to correct or contour all the images
[00:16:48] of one patient, now I think we can
[00:16:52] do it with AI in about an hour, so that's a very
[00:16:55] big time saving,
[00:16:58] wow, that's still quite a lot, basically you can see four patients
[00:17:02] instead of one, yes,
[00:17:03] basically.
[00:17:07] Moving from radiation therapy to the radiology center, where
[00:17:11] artificial intelligence literally saves lives. If we now
[00:17:14] imagine that we have a patient who is developing
[00:17:18] a cerebral infarction, then
[00:17:21] their brain must be treated as quickly as possible to save
[00:17:25] as much brain as possible. So we are talking about minutes, basically? Well, the
[00:17:28] faster, the better, so Estonia has a very good
[00:17:31] ambulance service that brings patients to the hospital very quickly, and
[00:17:35] then all this process that we are talking about
[00:17:39] now, until the patient
[00:17:41] receives treatment, can take less than an
[00:17:44] hour, well, in an ideal situation,
[00:17:47] right? Which means that the patient comes to our hospital, a neurologist
[00:17:51] examines them and decides that they need a study, then they
[00:17:55] come to us for the study, then the study is done, which looks exactly like what is on
[00:17:59] the screen, that is, such a
[00:18:02] boring black and white image for the ordinary eye, but
[00:18:05] a radiologist sees a lot here. Where
[00:18:08] AI now comes to help is that it helps the human
[00:18:12] eye to make this thing
[00:18:15] perhaps easier to follow, meaning if we look at this colored
[00:18:19] image, then
[00:18:22] the computer tells us to
[00:18:25] look here, it makes it red for us, basically,
[00:18:28] it clearly indicates, it clearly says
[00:18:31] that the blood flow to the brain has decreased, so this is the place where
[00:18:35] an infarction starts to form. And then what else we do
[00:18:39] is such an angiography, meaning we inject
[00:18:42] a contrast agent into the patient which starts circulating in their blood vessels,
[00:18:46] and if there is a clot in the blood vessel somewhere, which is
[00:18:50] the cause of this infarction, then it is also able to
[00:18:53] mark that place for us, meaning we can quickly
[00:18:57] tell the neurologist, "There is a blockage in this
[00:19:01] cerebral artery. Due to this blockage
[00:19:03] of the artery, the patient has suffered
[00:19:06] a cerebral infarction. Such and such an amount of the brain
[00:19:10] is damaged, such and such an amount is still
[00:19:13] salvageable." And then the person is called
[00:19:16] who will immediately remove that clot from the cerebral
[00:19:20] artery. Do you notice that artificial intelligence in radiology is perhaps the most
[00:19:23] advanced, or do you have any idea compared to other
[00:19:27] fields? In radiology, artificial intelligence has perhaps
[00:19:30] been used the most, perhaps the longest. In some European
[00:19:34] countries, for example, AI already does
[00:19:38] the first reading of screenings, which means that a human doesn't
[00:19:41] look at normal images anymore, AI selects
[00:19:45] those that need human attention, and
[00:19:49] some are then left out of the pot, meaning this saves
[00:19:53] our resources, that we don't have to review all these things. In Estonia, we are
[00:19:57] not yet that far, mhm, in Estonia, every
[00:20:00] patient is still examined by a doctor. Yes, and in the case of screening, the important
[00:20:04] nuance is that it must be done by two people. Completely
[00:20:07] healthy people come to the examination, and two radiologists look at
[00:20:11] the image, one looks and the other looks. Let's talk about the other side too, how
[00:20:15] much AI, artificial intelligence, makes mistakes?
[00:20:19] It varies greatly by product,
[00:20:21] and it depends on how sensitive we make the AI. If
[00:20:25] we make it very sensitive, then it makes more
[00:20:28] mistakes. If we make it less sensitive, then it makes
[00:20:32] fewer mistakes, but then it misses
[00:20:35] things. What is being worked on today is precisely to find
[00:20:38] that golden mean, where it would find all those things, but
[00:20:42] not create too much noise. Where could radiology
[00:20:45] develop with artificial intelligence in the next 50
[00:20:49] years? Do you have any idea of what doesn't exist
[00:20:53] yet but you might dream of? Speech recognition is one thing
[00:20:56] that is elsewhere,
[00:20:59] outside Estonia, very normal in the context of radiology,
[00:21:02] that everyone dictates radiological answers, but since
[00:21:06] Estonian is so specific, there hasn't been very good
[00:21:10] speech recognition in Estonia until now. How would that work in practice, if that
[00:21:13] speech recognition was in Estonian, what could you do with it then?
[00:21:17] Most of the time I'm looking at an image, mhm, and at the same time. While I'm looking at this image,
[00:21:20] I start describing that
[00:21:24] the cortical gyri are widened, cerebral atrophy
[00:21:28] has developed, here he has a lesion, in the region of the
[00:21:32] basal ganglia on the left,
[00:21:34] so I look at the image, I can already speak, and the text then
[00:21:38] goes automatically, it goes automatically to you, but since we don't have that right now, then
[00:21:41] it's like this: I look, I write, I look, I write, I look, I write,
[00:21:45] and that's what actually takes up my time, exactly that.
[00:21:49] How to take doctors'. overburdening bureaucracy and
[00:21:52] paperwork away and give time for direct communication with patients
[00:21:56] will be the greatest charm and challenge of artificial intelligence
[00:21:59] in the coming years. Professor of Practical Ethics Kadri
[00:22:02] Simm sees great advantages in the application of artificial
[00:22:06] intelligence in medicine, especially considering that the financial
[00:22:09] resources of the healthcare system are increasingly constrained. However, according to her,
[00:22:12] the dangers and risks that have accompanied
[00:22:15] new technologies throughout history must be considered.
[00:22:19] Gunpowder can be used to make bombs and fireworks, and
[00:22:23] in the case of medical technologies, we also have many examples of
[00:22:26] how whether its effects are good or bad does not actually depend
[00:22:30] so much on the technology as on the context in which it
[00:22:33] is applied. A good example, this is not AI, but
[00:22:37] it's ultrasound technology, which is actually a relatively cheap technology, used for a very
[00:22:41] long time in pregnancy monitoring.
[00:22:44] And as a result, we now
[00:22:47] have a situation where, you know, there are many
[00:22:50] countries and nations where there are many more
[00:22:53] boys than girls, because during pregnancy monitoring
[00:22:56] with ultrasound, it is possible to determine the sex of the child. And if the
[00:23:00] cultural context and social context, you know,
[00:23:03] supports some visions that boys are better than
[00:23:06] girls, then the consequence of this is now that we are missing millions of girls
[00:23:10] and millions of women, and well, basically a social catastrophe
[00:23:14] because these women were never born. And yet the same
[00:23:18] technology here in Estonia has not caused anything like that, right?
[00:23:22] So when talking about the impact of technology, it is always really important
[00:23:25] to consider the society, the culture, or
[00:23:29] perhaps the specific medical field that influences it.
[00:23:31] In the case of artificial intelligence, Simm primarily sees a danger in the
[00:23:35] increase of inequality.
[00:23:38] how AI is trained
[00:23:41] on certain data, and that data comes from somewhere.
[00:23:44] In today's world, it's mostly English and Chinese
[00:23:48] data, which represents populations, or even more precisely,
[00:23:52] not the entire population. For
[00:23:55] example, in America, only those who have money to go to the doctor and who make it there,
[00:23:59] and then data is generated about them, right? And
[00:24:02] one concern with this is that, yes, that data is biased and the application
[00:24:06] of algorithms developed on the basis of these data
[00:24:10] in other societies,
[00:24:13] on different populations, may cause such problems. Simm
[00:24:17] predicts that as artificial intelligence grows,
[00:24:21] in the treatment of patients, doctors' technological
[00:24:23] awareness will become increasingly important.
[00:24:27] Of course, it is important that doctors trust and know
[00:24:31] how artificial intelligence works in their field. And, well,
[00:24:35] this is related to the broader question, isn't it, of medical education
[00:24:38] in general today, what should be paid attention to there first
[00:24:42] and how the advent of artificial intelligence affects how this
[00:24:45] medical education is given, what is generally taught,
[00:24:48] because data comes in so terribly much
[00:24:51] all the time. And people are never equal
[00:24:54] in their ability to analyze that amount of data compared
[00:24:58] to artificial intelligence, so something else is what
[00:25:01] that doctor should be doing there, so that doctors,
[00:25:04] and also other medical workers who are dealing
[00:25:08] with diagnosis using artificial intelligence,
[00:25:11] should know how
[00:25:13] it works. Now, a more complex problem is that
[00:25:16] often the developers themselves don't know how that answer
[00:25:20] comes out from the other end, but it's also interesting
[00:25:24] that, well, we have different legal frameworks, don't we?
[00:25:27] We have the United States, which is influential, we have
[00:25:31] the European Union, which is influential, we have our own Estonia,
[00:25:35] right, that in different legal frameworks,
[00:25:38] there are different opinions on this, for example,
[00:25:41] resuscitation, a resuscitation decision,
[00:25:45] right, that if there are no doctors nearby,
[00:25:48] no medical workers nearby, the question is whether to resuscitate
[00:25:52] or not. Again, if artificial intelligence
[00:25:55] or that system has the information that this person
[00:25:58] in no case wishes to be resuscitated, well, then
[00:26:02] it can be said that if it is not resuscitated,
[00:26:06] if artificial intelligence makes such a decision, then
[00:26:10] it is the right decision, or vice versa, right? Well,
[00:26:13] as long as a human is still ultimately responsible
[00:26:16] in some sense, that the doctor has put it into
[00:26:20] the system.
[00:26:21] I think that
[00:26:25] there is no clear black and white line
[00:26:29] between what artificial intelligence is allowed
[00:26:32] to decide and what it is not, if the final
[00:26:35] word does not remain with it.
[00:26:38] It is clear that we are living through a time of great
[00:26:42] changes and it is probably
[00:27:33] the main role in its development. The technological readiness
[00:27:36] is already there today. The question is rather,
[00:27:39] are you ready?
[00:27:42] The extensive news program of the end of the week
[00:27:45] will be on air in just a moment and after
[00:27:49] that at 10 o'clock a new episode
[00:27:52] of the crime series Professor will begin.
[00:27:55] "The criminology professor with a peculiar nature
[00:27:58] has found himself in a situation at the apex
[00:28:02] of an unpredictable chain of events.
[00:28:05] But the investigation of new cases continues
[00:28:09] with all its might anyway.
[00:28:12] The stylish British crime series Professor
[00:28:15] T. New season. Tonight
[00:28:18] at 10 pm on Estonian Television.
[00:28:21] Or watch it immediately on Jupiter.
[00:28:24] Rocket Junior is back. "Are there brave and clever
[00:28:26] experimenters in your class?
[00:28:32] Rocket Junior invites students from fourth
[00:28:35] to sixth grade to a science competition,
[00:28:39] where success is primarily brought by good teamwork
[00:28:42] and thinking outside the box.
[00:28:45] Winners will be flown
[00:28:48] to one of Europe's most exciting science
[00:28:51] centers. Put your students to the test
[00:28:55] and register for the competition today.
[00:28:58] More information can be
[00:29:38] found on the website raketjuunior.ee.
[00:29:42] I'll put it down before, I didn't want to break
[00:29:45] that big root. Tree
[00:29:47] roots grow directly under the eyes of scientists,
[00:29:51] well, that's something that a person usually
[00:29:54] never sees in their life.
[00:29:58] Pärnu Bay is the most important
[00:30:01] fishing ground for Estonian coastal fishermen.
[00:30:05] In order to see how the fish in Pärnu Bay
[00:30:33] are doing, we have come trawling
[00:30:35] with scientists. In this episode, we
[00:30:39] will meet American soldiers who are testing
[00:30:42] their tanks in the forests of Võrumaa.
[00:30:45] How do the distant comrades-in-arms cope
[00:30:48] with homesickness?
[00:30:51] We also talk to local entrepreneurs. They have
[00:30:54] a very big appetite, so very large quantities are always ordered. And we will find out what the residents of Võru think about it. The question is whether they can be trusted. Don't miss the show "Comrades-in-arms" on Monday evening at 8 PM on Estonian Television. Estonian heart health is one of the worst in the European Union, whatever we can come up with to make youth overweight and lack of movement a smaller problem than it is now. On the last day of the Russian elections, Moscow was hit by a large-scale Ukrainian drone attack. The rising price of car fuel makes filling the fuel tank more meager. The heating period is coming.