Chapter 799 A little future shock
The first is to reduce costs.
In terms of medical care itself, there are two important reasons for high medical costs.
First, the development cycle of drugs is long and the cost is too high;
Second, the cost of training medical personnel is too high.
Now, most of the world’s new drugs come from the United States.
It takes 20 years for a new drug in the United States to be developed from development to launch, requiring an investment of US$2 billion in the process.
As for US patent law, the entire patent period is only 20 years from the date of application.
But the application for a patent does not start counting on the day the drug is launched, but more than ten years before the drug is launched.
That is to say, after a drug is put on the market, it is protected by patent for only a few years.
Through our comprehensive investigation of pharmaceutical companies such as Johnson & Johnson, Roche, and Ruihui, we have found that the time a drug can enjoy patent protection is usually only 7 years.
In other words, if the new drug can be successfully developed, it will only have seven years of exclusive sales to earn back the cost.
So, every new drug is very expensive.
It takes 13 years to train a specialist in the United States.
Becoming an attending physician in China takes 10 years for college and undergraduate students, 9 years for a master of medicine, and 8 years for a doctor of medicine.
From the perspective of return on investment, since the investment of time and money is so huge, they must have high income to be worthwhile.
So how do we use artificial intelligence to change the medical industry?
Give an example.
We naturally believe that to see a doctor, we need to find an experienced doctor.
The accumulation of their experience is a process of learning through cases, and no matter how fast people learn, they cannot learn faster than computers.
It is difficult for a radiologist to read and study more than 100,000 cases in his lifetime.
But computers can easily learn from millions of cases.
Compared with doctors, computers have three major advantages in diagnosis and surgery.
First of all, their probability of missed diagnosis and error is very low, which means that they can successfully detect some conditions that doctors ignore.
Secondly, their accuracy is very high, and it improves very quickly as the amount of data (cases) increases.
Finally, and what humans do not possess, these intelligent programs are very stable and will not be affected by emotions like humans.
The cost of these intelligent programs is usually less than one percent of labor.
With powerful medical artificial intelligence programs and medical robots, patients in remote counties can enjoy top-notch medical services.
Thus solving the problem of imbalance of medical resources.
Finally, let’s talk about the changes artificial intelligence has brought to the pharmaceutical industry.
Today, more money is invested in cancer than in the Apollo moon landings or speech recognition.
But why is cancer still so difficult to cure?
Because cancer cells are animals and people’s own cells that have genetic errors during the replication process, not from outside the body.
That is to say, cancer is a genetic disease, not a virus.
The most effective way to treat cancer today is to use anti-cancer drugs developed through genetic technology.
Mechanistically speaking, it is to find the diseased gene and kill the corresponding cancer cells.
However.
Even if different people get the same type of cancer, the genes responsible for the disease in their cancer cells may not be the same.
So an anticancer drug may work for some patients but not others.
In fact, when most doctors give drugs to cancer patients, they need to conduct genetic comparisons on the patients to determine whether a certain anti-cancer drug can be used.
The second and most fundamental difficulty in treating cancer is that the replication of cancer cells themselves can also go wrong.
This is not difficult to understand. If a gene makes an error once during the replication process, it will happen a second time.
As a result, anti-cancer drugs that were originally effective become ineffective.
When anticancer drugs kill cancer cells, they may not kill all of them.
Even if there is only one cancer cell left that has not been killed, it can still multiply rapidly and new genetic mutations may occur.
So we usually hear this type of story.
A relative or friend suffering from cancer had the disease under control for a long time, but suddenly relapsed overnight, and the medicine did not work, and he died soon after.
The reason is that genetic changes make the original anti-cancer drugs ineffective.
Because the mutation of cancer cell genes is related to people, and it may change again and again.
Therefore, if we want to completely solve the problem, we need to design specific anti-cancer drugs for different patients, and develop new drugs based on every new change in the patient's cancer cells.
In other words, as long as the speed of developing new drugs can keep up with the changes in cancer cells.
So even if all cancer cells cannot be completely killed, patients can still coexist with cancer for a long time.
Theoretically, this approach is feasible.
But the cost of doing so is too high.
First of all, there must be a dedicated R&D team to develop drugs around each patient, and the R&D speed must be fast enough.
Second, it costs at least $1 billion per person.
So this seemingly possible method has no promotion significance. So where is the way out? ”
Three words were displayed on the projection screen:
Big data.
"Currently we know that the various genetic errors that may cause tumors are only on the order of 'ten thousand', while the known cancers are only on the order of 'hundred'.
In other words, even if all possibilities are taken into account The number of malignant gene copy errors and various cancer combinations is only a few million to tens of millions.
This order of magnitude is very small in the IT field, but it is almost infinite in the medical field. >
If we can use big data technology to find various combinations that actually cause cancer among no more than tens of millions of combinations, and find corresponding drugs for each of these combinations, then it will be possible for everyone All lesions can be treated
For different lesions of different people, just choose a medicine from the drug library.
In this way, cancer can be controlled.
Although the total research and development cost of thousands of drugs is not low, if it is spread to every cancer patient in the world, it will not be that high.
The same principle applies to other diseases. ”
After hearing his words, the eyes in the audience that were still somewhat expectant quickly dimmed.
They are all smart people. Although they think Xu Liang’s strategy is feasible, the real establishment of Such a database does not yet know the year of the monkey and the month of the horse
Even the day of their death may not come.
So the anticipation quickly dissipated.
Xu Liang saw it and didn’t explain much.
He didn’t see this day before he was reborn, so he couldn’t explain it at all.
But he is here to talk about big data.
As long as the big guys understand the importance of big data, the goal will be achieved.
Everything else is secondary.
Of course, if the big guys in the audience take the initiative and take the initiative to build this database, then the people across the country will enjoy it.
His surname is Xu, he must be a helper.
But there is a high probability that it is impossible.
Big bosses also focus on efficiency and return on investment.
“According to data surveys by Hanhua and Hongmeng Bing, only about one-seventh of the drugs in the United States that have been clinically proven to be effective can eventually pass the full approval process of the Food and Drug Administration and be finally launched on the market.
The remaining six-sevenths of the drugs, although they do have good effects on some patients when used on a small scale,
but when used on a large number of patients, they have an average effect. The effect was not significant, so it was rejected by the 'Food and Drug Administration'
So, if we can find a specific group of people, we can reuse these 'waste drugs'
In the future, a disease may be treated with different drugs, and different people may have different specific drugs.
But to achieve this goal, the state needs to step forward and establish a nationwide drug database.
I believe that when this database is actually completed, my country's medical expenditure will be significantly reduced and the average life expectancy will be greatly improved.
Even cancer is no longer a terminal illness. ”
Looking at the distracted bosses in the audience, Xu Liang was ready to give them a little future shock.
"Speaking of medical treatment, let's digress a little bit.
Can humans live forever?"
As soon as these words came out, the faces of the big guys changed instantly.
Looked over directly.
Standing on the stage, Xu Liang could clearly see their dilated pupils and feel their breathing that suddenly became rapid.
The higher the status, the more wealth people have, the more they desire longevity.
This is also the reason why so many emperors in ancient times pursued immortality.
“We talked about cancer before. Even if humans solve cancer, it will only extend the average life span by 4 years.
The significance of treating cancer is not as great as the public imagines.
The biggest challenge facing human longevity is aging
As long as people live long enough, they will eventually face the troubles of Alzheimer's disease, without exception.
Death rates from cancer, AIDS, heart disease and stroke have declined worldwide over the past decade, but deaths from Alzheimer's have increased, according to MIT research 40%.
So the key to extending life is to find the aging gene.
How to find it?
The most fundamental solution to big data and artificial intelligence.
We can use the methods used to solve cancer to study aging genes, and after finding the real pathogenic mechanism, we can change it through gene repair and editing technology.
Although it seems that this method is not of practical significance yet.
But I believe that with the development of big data and artificial intelligence, this goal will be closer and closer to us. "
Paused.
"That's it for today's speech. Thank you very much for listening. Thank you. ”
(End of this chapter)