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Why is medical data analysis important? Introducing case studies

As the use of big data progresses in various industries, the importance of data analysis and data utilization is increasing in the medical field as well. Therefore, in this article, we will explain how medical data analysis is used and why it is important, while introducing specific examples.

What is medical data analysis?

Medical data analysis is the analysis of case data acquired and accumulated by various means such as CT and X-ray. The use of medical data analysis has advanced along with the development of AI technology.

AI is computer software that reproduces a part of human intellectual ability, and it is translated as artificial intelligence in Japanese. AI could only demonstrate limited capabilities at first, but the development of a new machine learning technology called “deep learning” has dramatically improved its performance. It takes a lot of time and effort to analyze a huge amount of medical data by hand, but with AI, we can take advantage of the information processing capabilities unique to computers and analyze them more efficiently in a shorter time.

AI-based medical data analysis can be used in a wide range of fields. For example, in clinical settings, diagnostic imaging technology is used to detect diseases and prevent symptoms from being overlooked. In addition, medical data analysis is used in all healthcare fields, from research fields such as the analysis of the human genome, pharmaceuticals that develop therapeutic drugs, and daily health management. By utilizing medical data analysis, we can expect to implement appropriate medical care and improve patient happiness.

Importance of medical data analysis

What can medical data analytics help with? Next, in order to confirm the importance of medical data analysis, we will look at what kind of medical field medical data analysis can contribute to.

Facilitate early detection of disease

The first advantage of medical data analysis is that it facilitates early detection of diseases. AI is also suitable for detecting extremely small lesions that might be overlooked by the naked eye, so it is highly effective in diagnostic imaging. Therefore, by acquiring and accumulating patient biometric data through medical equipment such as CTs and X-rays, or wearable devices such as smartwatches, and analyzing such data with AI, daily health management and early detection of illness can be achieved. , or preventive medicine. In the future, as the declining birthrate and aging society become more serious, and the maintenance of the social security system is becoming a concern, efforts to detect illness early while it is minor and enable rapid recovery are becoming extremely important. .

Objective judgment possible

One of the advantages of medical data analysis is the ability to make objective judgments. Human doctors differ in their abilities and skills according to their individual experience and qualities. Also, even if it is limited to one doctor, it is inevitable that there will be some unevenness in performance due to fatigue and poor physical condition. In that respect, unlike humans, AI can always maintain a certain level of performance and make objective decisions. In other words, AI will help eliminate the dependency on individuality in the medical field. These effects will correct regional disparities in medical care, leading to a future in which uniform medical services can be provided to people living in any region.

Expected to improve patient well-being

The use of medical data analysis is expected to contribute to improving patient well-being. As I mentioned earlier, Japan is now facing a super-aging society, which is said to be an era of 100-year lifespans. And maintaining good health is very important for the elderly to live a comfortable life. In that respect, the early detection of diseases and the provision of preventive medicine using AI will contribute to the extension of healthy life expectancy. of life)) or increase happiness.

Accurate diagnosis is possible with more data

In data analysis using AI, the more medical data such as CT and X-rays are available, the more accurate diagnosis becomes possible. This is a characteristic of AI machine learning or deep learning. In recent years, as symbolized by wearable devices such as smartwatches, the development of IoT technology has made it possible to acquire medical big data in various ways. As the amount of data on diseases and patients increases, AI can identify signs of disease and patterns of people who are susceptible to specific diseases from that data, demonstrating high accuracy in diagnosing diseases and analyzing preventive medicine. You can expect to

Possible to reduce medical costs

The development of medical data analysis and medical AI can also be expected to reduce medical costs. If the accuracy of AI diagnosis becomes higher in the future, doctors will not necessarily need to be in charge of examination, so it will be possible to reduce medical costs while ensuring the accuracy of diagnosis. Of course, at present, AI is only an auxiliary tool for doctors, but even so, by utilizing the diagnostic imaging technology mentioned above and by automating or streamlining processes such as receipts, the burden on doctors can be reduced and operations can run smoothly. contribute to The harsh working environment of medical professionals has been a long-standing issue in the Japanese medical industry, so there are high expectations for AI-based work support. Alternatively, hospital management can use data analysis to improve the efficiency of hospital management and medical services. You can use it for improvement.

Examples of Utilization and Efforts of Medical Data Analysis

What are some examples of actual utilization and initiatives for medical data analysis? Below, as a reference example, we introduce the efforts of the Graduate School of Osaka University and the US Genome Center.

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