What impact will big data and AI have on the pharmaceutical industry?

Analyzing big data and utilizing it for business operations is becoming popular in all industries. In this article, in addition to basic information such as the definition and overview of big data and its relationship with AI (artificial intelligence), we will also introduce the benefits of using big data in the pharmaceutical industry , specific examples of its use, and future challenges.

What impact will big data and AI have on the pharmaceutical industry?

Table of contents

  1. What is big data?
  2. Relationship between big data and AI
  3. Expectations for big data x AI utilization in the pharmaceutical industry
  4. Development of new drugs
  5. Significant reduction in development costs
  6. Improving efficiency and productivity
  7. There are still challenges for practical application
  8. Privacy protection
  9. Cooperation between companies is essential for data collection
  10. System construction requires manpower and costs
  11. summary

What is big data?

“Big data” simply means “a huge group of data.” Specifically, data recorded moment by moment by various sensors such as temperature and humidity, data such as stock price fluctuations in the financial market, content posted on various SNS, videos and photos saved on the internet, etc. The collection of all data groups can be said to be big data.

Characteristics of big data that are not present in previous generations of data are often cited as “huge amount of data”, “wide variety of data types”, and “ability to generate and update data’  . The three V’s are “high frequency (velocity)”. The rapid development of ICT has made it possible to analyze vast amounts of data that could not be processed using conventional wisdom. The results of big data analysis are now being used in a variety of fields, including product development and marketing.

The development of AI (artificial intelligence) technology was a major turning point in the use of big data.

Relationship between big data and AI

The use of big data is closely related to the development of AI (artificial intelligence).

First of all, the power of AI is essential for analyzing big data, which is a huge collection of data. With the spread of IoT (Internet of Things), which connects devices equipped with various sensors to the Internet, it is impossible for humans alone to analyze the large amounts of data that can be collected and stored in a wide range of fields . The use of high-performance AI has become essential. In particular, AI has made remarkable progress in analyzing data such as images and sounds, and is being widely used in the medical field for image diagnosis and other purposes.

On the other hand, big data is also indispensable for AI itself. In other words, AI can be described as “a computer technology that analyzes large amounts of data to predict things and find ways to solve problems.” In order for AI to demonstrate its analytical ability, it requires a large amount of data. It is necessary to “study” using .

What triggered the dramatic leap in AI performance was the introduction of “deep learning,” in which computers discover and learn patterns and rules from data on their own, into machine learning. Deep learning works by incorporating large amounts of data and accumulating learning to improve prediction accuracy, so the more data used for learning, the better. Big data is the source of the large amount of training data that AI requires.
In this way, the use of big data and the development of AI are inseparable.

Expectations for big data x AI utilization in the pharmaceutical industry

So how are big data and AI being used in the pharmaceutical industry? Here I will explain this using an example.

Development of new drugs

The use of big data and AI is also changing the process of developing new drugs. In particular, it plays a major role in the field of cancer treatment, where symptoms and the degree of progression vary greatly depending on the individual patient.

Since cancer is a disease caused by some kind of genetic abnormality, modern treatments are attempting to identify the cause of cancer at the genetic level and provide the optimal medical care for each individual. Masu. To achieve this, it is necessary to analyze the sequence of all human genetic information (human genome), which consists of 3 billion base pairs, for each individual patient. It is believed that it will be possible to develop drugs tailored to specific medical conditions.

Significant reduction in development costs

A major challenge in drug discovery is increasing the probability of successful development. One of the key elements to this end is to unravel vast amounts of data and select targets with high precision.

First, AI is playing a major role in simulations used to search lists for compounds that are candidates for new drugs. Using AI, it is possible to make binding predictions by comparing data on existing and unknown compounds with biological proteins and data on the human genome, which consists of 3 billion pairs, at low cost and in a short period of time. Now it’s possible.

In addition, by having AI analyze various data and using it to design clinical trials (trials) in new drug development and narrow down candidate candidates, it is also possible to significantly reduce research costs. .

Improving efficiency and productivity

Big data and AI are also being used to streamline business flows and improve productivity in the pharmaceutical industry. In particular, routine tasks are thought to be an area that would greatly benefit from automation and efficiency improvements through AI.

Additionally, customer centers are increasingly using chatbots that incorporate AI. By letting AI learn data related to questions and accumulating data, it is now possible to entrust most of the tasks that previously relied on human operators to AI responses. By switching to an operator only when a question cannot be answered by AI, it is now possible to maintain the quality of response and improve the productivity of the entire organization. In addition, as a sales support for MRs, we utilize analysis using big data and AI to analyze and visualize trends in doctors’ prescriptions, changes in prescription contents, and the activities of each MR member, so that we can find the most suitable partner for the most suitable partner . It is now possible to aim for “overall optimization” that provides the most appropriate information at the right time.

There are still challenges for practical application

There are still issues that need to be resolved before the full-scale practical application of big data and AI.

Privacy protection

Medical data is the most sensitive part of personal information, so it must be handled with care when used. The key point is to mask and utilize information that identifies individuals. In the future, it will be necessary to establish technologies and rules that ensure both privacy considerations and further utilization of data, such as the use of blockchain (distributed ledger technology) that combines encryption and authentication functions. .

Cooperation between companies is essential for data collection

In order to collect sufficient data in both quantity and quantity for medical use, it is important for medical institutions and pharmaceutical companies to collaborate and create a system that allows mutual use of their respective data. Even company-owned data that is considered to have no value on its own can become valuable when combined with other government-released data.

However, due to reasons such as the data format differing from institution to institution, even if there is useful data, it is difficult to utilize it as training data for AI or data to be analyzed by AI.
There are great expectations for the creation of a system that allows organizations to share and use the data they possess.

System construction requires manpower and costs

Building an ICT system is essential to utilizing big data and AI. In order to make high-performance AI work, capital investments such as servers and data centers with appropriate capabilities are required. In addition, building a network that can connect outside the company will further increase costs. These initial costs and running costs may be reduced by using cloud services specialized for core business systems, marketing, and drug discovery. In order to build a system that will be useful in the actual field, it is important to proceed with the plan while taking into consideration costs and other factors and deciding on pitfalls.


The pharmaceutical industry is also moving towards the introduction of AI and the use of big data in development and sales. While the use of technology has benefits such as cost reduction, there are still many issues that need to be overcome, such as data linkage that transcends corporate boundaries and consideration for privacy. The methods each company uses to lead to future growth are attracting attention.


Please enter your comment!
Please enter your name here