Artificial intelligence will change the features of medicine if this obstacle is removed

The leakage of the cerebrospinal fluid, caused by the occurrence of rifts or holes in the spinal cord, is a rare matter and is difficult to discover, and patients can spend years without a correct diagnosis due to the common symptoms, which include nausea, neck pain and exhausted headaches when it stands, and a number of them said. With the increase in the number of medical fields, artificial intelligence can radically change the way to discover and treat these diseases by increasing accuracy, providing expenses, and in some cases through a significant improvement in the lives of patients. Despite the diagnostic nature of most devices based on artificial intelligence approved by the regulatory authorities in the United States, there are many possible uses of this technology in the healthcare sector, from automation of monotheistic administrative work to the discovery of medicine, and some estimates suggest that the acceptance of artificial intelligence can save. Artificial intelligence develops radiology devices that can provide a glittering leak of a look to the future, although MRI may show changes in the brain that indicate that the leakage of the machines has been running for decades. However, this situation changes in the current period, as it is used that are called ‘CT scans by counting photons’ artificial intelligence and semi -advanced conductors -to discover the spine leak that was not visible in the preceding, which enables treatment, and in most cases leads to complete recovery. Patients have described this technology as a radical change. Far of neuroscience, this kind of radiology can discover minor imbalances before it becomes serious health risks, from non -action -vascular expansion to dangerous degrees of deposits in the veins, and the ability to investigate cardiovascular diseases can be a radical change in preventive care, which is one of the largest causes of death in the world. Data deficit on artificial intelligence training is one of the most important challenges for artificial intelligence -based medical devices, including radiology and surgical robots and automatic discussion of conversations, in the amount of data needed to train artificial intelligence models. In the United States, this information is usually stored in databases of healthcare services and hospital service providers, and despite the fact that the government invests billions of dollars to encourage the share of data, he indicated, he indicated that he indicated, indicated, indicated, indicated. More than 60% of hospitals have at least one basic barrier to data exchange, while about 70% still use fax machines. The accuracy and advantage of the use of artificial intelligence models should give preference to improving this situation, and it depends on the possibility that developers reach large amounts of data, and it is better to have a number of healthcare systems and countries in different formulas and languages. The encouraging thing is that the private sector is currently in the early stages of developing other artificial intelligence instruments that can process “non -structured” data. But despite the expression of government health bodies in the United States, these products have not fully adopted for organizational reasons. The amendment of these organizational rules is a first important step in using technology on a larger scale. Likewise, lawmakers must also play their role in this regard. In light of providing the US Congress, government agencies can work together to develop a large basis of high quality patient data without identifying, which can be used to train artificial intelligence, thus training for models on “regulatory category -data” – as described by the former US food and drug administration, Scott Gottlib will increase their accuracy. Other challenges before technology The Food and Drug Administration and the Ministry of Affairs of the Veterans announced a plan of this type this month, which is a general laboratory for artificial intelligence, which aims to test artificial intelligence instruments using the data of old warriors. The narrowing of the task of food and drug management, by focusing on ensuring the quality and accuracy of data and prevention of prejudice, can also improve the use of limited resources. However, relying on artificial intelligence in diagnosis and preventative examination is not a path without obstacles. In addition to the cost, the risk of unnecessary or harmful interventions exists. The rays that are made faster and more accurate will eventually be affordable and easy to procedure, especially for patients who are most vulnerable to health risks. It is noteworthy that the initial doubt in the performance of preventative CT scans on smokers quickly disappeared after research showed that the examination operations significantly reduced the risk of lung cancer. The ability of artificial intelligence to improve the lives of patients is no longer theoretical. On the contrary, it can become a standard of care amid increasing access to data and artificial intelligence -based treatment. In short, the leakage of the cerebrospinal fluid is a rare condition due to tears or holes in the spinal cord, and several symptoms such as nausea and neck pain occur frequently. These common symptoms with many other causes make the exact diagnosis a difficult process, which can lead to wrong diagnoses such as allergies. Artificial intelligence makes a revolution in how to discover and treat these diseases, as it can improve accuracy, reduce costs and significantly improve the quality of patients’ lives as it is used in advanced techniques such as CT scans to discover previously invisible leaks, and in many cases to provide effective treatment and complete recovery. However, artificial intelligence applications in medicine have major challenges, especially the lack of data needed to train models effectively. The available data is often stored in separate databases and is not effective between medical institutions, which limit the ability of models to learn and develop. This necessitates the need for restructuring and exchange methods of data collection to improve the capabilities of artificial intelligence in the field of diagnosis and treatment, ensuring the greatest benefit of this advanced technology in healthcare.