Artificial Intelligence in the Pharmaceutical and Healthcare Industry. Employment of AI within Bayer Pharmaceuticals

Term Paper , 2021 , 10 Pages , Grade: 1,0

Computer Sciences - Artificial Intelligence

During the past years, a continuously increasing number of corporations have decided to adopt Artificial Intelligence (AI) systems to improve their efficiency and reduce costs. This research paper aims to analyse the rise, applications, and developments of AI in the pharmaceutical and healthcare industry. Specifically, the employment of AI within the German multinational Bayer Pharmaceuticals is discussed, and further implementation suggestions are outlined.

Furthermore, current opportunities, such as epidemic outbreak predictions and image recognition platforms for medication ingestion tracking, and challenges, for example legal issues and ethical challenges, are examined. The future role of AI systems in the pharmaceutical and healthcare industry can hardly be anticipated. Nevertheless, thus far these systems have represented a unique and tremendous asset within societies and companies, and it is most likely that in the course of time their role will be of always greater importance.

Nowadays, organizations are defined by higher complexity, changing client demands, increased competition and adoption of new technologies. On the path towards an increased digitalization of economies and societies, the management of information and of knowledge, and the way people connect, collaborate and learn are drastically changing. As more and more possibilities for disruptive change are offered by digitalization, individuals and organizations need to redesign their leadership, innovation, knowledge processes and information practices.

Table of Contents

1 Introduction

2 Artificial Intelligence

3 Bayer Pharmaceuticals

4 Analysis
4.1 Implementation of Artificial Intelligence Systems at Bayer Pharmaceuticals
4.2 Opportunities
4.3 Challenges

5 Conclusion and Outlook

List of References

Abstract

During the past years, a continuously increasing number of corporations have decided to adopt Artificial Intelligence (AI) systems in order to improve their efficiency and reduce costs. This research paper aims to analyze the rise, applications and developments of AI in the pharmaceutical and healthcare industry. Specifically, the employment of AI within the German multinational Bayer Pharmaceuticals is discussed, and further implementation suggestions are outlined. Furthermore, current opportunities, such as epidemic outbreak predictions and image recognition platforms for medication ingestion tracking, and challenges, for example legal issues and ethical challenges, are examined. The future role of AI systems in the pharmaceutical and healthcare industry can hardly be anticipated. Nevertheless, thus far these systems have represented a unique and tremendous asset within societies and companies, and it is most likely that in the course of time their role will be of always greater importance.

Keywords

Artificial Intelligence, Bayer Pharmaceuticals, Research and Development

1 Introduction

Nowadays, organizations are defined by higher complexity, changing client demands, increased competition and adoption of new technologies. On the path towards an increased digitalization of economies and societies, the management of information and of knowledge, and the way people connect, collaborate and learn are drastically changing. As more and more possibilities for disruptive change are offered by digitalization, individuals and organizations need to redesign their leadership, innovation, knowledge processes and information practices (North & Kumta, 2018). As a result of its versatility, reliability, and cost-effectiveness (Chowdhury & Sadek, 2012), Artificial Intelligence (AI) has already been adopted in numerous fields, including medicine and healthcare. More and more pharmaceutical and biotech firms embrace automated processes, in which data-driven decisions and predictive analytics tools are integrated (McLeod, 2020). The German multinational Bayer Pharmaceuticals has already conducted several pilot projects and additionally started partnerships with a number of AI focused firms. To name a few: Cyclica, a biotech company reinventing drug discovery and providing pharmaceutical companies with augmented and cloud-based platforms (Bulgaru, 2020) and Exscientia Ltd., an AI-driven drug discovery company (Bayer Website, 2020).

The aim of this paper is to analyze the implementation of artificial intelligence at Bayer Pharmaceuticals as well as its advantages and disadvantages. Firstly, artificial intelligence will be defined, and the multinational corporation Bayer, as well as its information management systems, will be introduced. Then, the adoption of artificial intelligence systems at Bayer Pharmaceuticals will be analyzed. Finally, the potential developments of systems along with the advantages and disadvantages will be discussed.

2 Artificial Intelligence

Popenici and Kerr (2017) have defined AI as “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks”. AI technologies include deep learning, machine learning, non-linear grid systems, chatbots, and self-modifying graph systems (Stefanini Group, 2020).

In the pharmaceutical and healthcare industry, due to its versatility and efficiency, AI can be adopted in most departments to enhance data processing, accelerate procedures, as well as create new knowledge (Tierney, 2020). Specifically, in the pharmaceutical industry, AI can be adopted in the development of new drugs, improvement of existing ones, patient diagnosis as well as patient care (Bulgaru, 2020).

3 Bayer Pharmaceuticals

Bayer is one of the world’s leading multinational corporations. The company was founded in 1863 in Leverkusen, Germany, by Friedrich Bayer and Johann Friedrich Weskott. Bayer is a life science company, and its goal is: “Science for a better life”. The corporation comprehends the following divisions: Pharmaceuticals, Consumer Health and Crop Science. Pharmaceuticals is the largest segment as regards sales and, as stated on the corporate website, its focus is on “researching, developing and marketing specialty-focused innovative medicines that provide significant clinical benefit and value” (Bayer Website, n.d.). Furthermore, the firm operates in the therapeutic areas of cardiology, oncology, gynecology, hematology, women’s healthcare, ophthalmology and radiology (Bayer Website, n.d.).

4 Analysis

Chapter 4 examines the implementation of artificial intelligence systems at Bayer Pharmaceuticals as well as the correlated opportunities and challenges.

4.1 Implementation of Artificial Intelligence Systems at Bayer Pharmaceuticals

As previously mentioned, Bayer Pharmaceuticals has already initiated a number of pilot projects in the field of AI. The firm has recently also been designated the FDA approval for an AI software, developed in partnership with Merck, for the pattern recognition of the rare chronic thromboembolic pulmonary hypertension (CTEPH). The AI software confronts pictures of pulmonary vessels, lung perfusion, cardiac check-ups and patients’

clinical histories, and radiologists are then able to rapidly analyze the images and identify patients with CTEPH earlier in time (Bulgaru, 2020).

The areas in which Bayer Pharmaceuticals could certainly employ AI are drug discovery, drug improvement, rare disease discovery and medication tracking. Firstly, Bayer Pharmaceuticals could adopt AI systems in order to uncover new drugs. Through AI, the multinational would be able to analyze hundreds of genes simultaneously and based on the results, identify and create new cures. The company Verge Genomics, for example, has already employed AI for drug discovery and was able to build solutions to reduce the risk of Alzheimer’s (Stefanini Group, 2020). Following the discovery of new drugs, AI could also be employed in the creation of the latter. In fact, AI makes the design of drugs more efficient and it improves both the drug quality and the personalization during the R&D process (Bulgaru, 2020). AI could also support the improvement of existing drugs or be employed to find faster methods to treat diseases. In addition, the discovery of rare diseases could benefit from the use of AI; through the creation of AI-powered platforms, Bayer Pharmaceuticals could enhance and accelerate research processes. Finally, as with Exscientia, Bayer Pharmaceuticals could create partnerships with several other AI-focused companies. The firm AiCure, for example, has developed an image recognition platform which tracks and confirms medication ingestion. The platform would not only allow healthcare providers to focus on high-risk patients and cure them with individualized medicines, but also receive high-quality data about patients being treated (AiCure Website, n.d.).

4.2 Opportunities

As stated by Bayer Pharmaceuticals (2020):

“There are significant opportunities in the field of artificial intelligence to transform the business – from the way we develop drugs to the way we engage with our stakeholders. Artificial intelligence holds the potential to drive unprecedented productivity improvements and better outcomes across the value chain as well as to improve our value proposition in creating new offerings and business models”.

AI is considered amongst the most prominent digital transformation technologies, and while it may remind many of a science fiction movie, its potential and advantages should not be underrated.

Today, the costs associated with the development of a new drug amount to approximately $2 billion (Palmer & Gillet, 2020) and the largest pharmaceutical companies spend approximately $70 billion annually on drug development. Often these costs are then transmitted to patients, many of whom are in fact not able to pay for medicines or therapies (Bulgaru, 2020). With the adoption of AI systems, the industry costs would be reduced by 30% (Palmer & Gillet, 2020). At Novartis, perceptive images and machine learning are used to predict whether untested compounds are worth testing or not. This results in further cost and time savings. Knowing beforehand if compounds should be tested and researched allows researchers to focus only on those compounds which will surely result in new or improved drugs. Furthermore, on average, pharmaceutical companies require 10 to 12 years to bring a new medication on to the market. Though, as stated by Palmer and Gillet (2020) AI “can get new drugs to clinical trials 5x faster”. This has been proven by the first drug entirely designed using AI, which entered human clinical trial in less than one year. Not only does AI shorten the trial conduction time, it also reduces the approval time, which results in drugs being introduced to the market in notably shorter periods of time as well as an increase in treatment options and in more affordable therapies (Tierney, 2020). AI has also made it easier to collect, access and track an increasingly higher amount of data (Stefanini Group, 2020). As more and more patients’ data are generated, it is estimated that 750 thousand terabytes of healthcare data are produced daily (Bayer Website, 2020), AI systems offer the potential to support Bayer Pharmaceuticals not only in reaching conclusions faster, but also in achieving results which human minds would not or would unlikely achieve, as well as in creating novel knowledge. Furthermore, as more and more data are analyzed by AI systems, the faster and more efficient AI systems become (Tierney, 2020). This process determines a continuous optimization of the pharmaceutical industry and the advancement of systems which in turn results in the development of improved pharmaceuticals within continuously shorter timeframes.

Finally, the COVID-19 pandemic has proved how much societies require rapid and efficient systems and especially the strengths of AI-powered approaches compared to traditional systems (Zhavoronkov, 2020). In the future, AI systems could also be employed in tracking and predicting epidemic outbreaks by analyzing all of the latest information from satellites to social data (Stefanini Group, 2020).

4.3 Challenges

AI offers extensive opportunities for the pharmaceutical industry. However, it presents various challenges too. In the first place, AI systems are still relatively new and therefore unknown and unfamiliar. Also, most pharmaceutical companies lack an adequate IT infrastructure or possess an infrastructure which was not designed in consideration of AI. In both cases, companies would have to spend a significant amount of money to create a valid IT infrastructure. Further, most of the data is available in a free text format and pharmaceutical companies would have to excel in collecting and sorting the data in a comprehensive and analyzable manner (Bulgaru, 2020). Finally, legal issues and ethical challenges associated with AI must be identified and mitigated. Medical malpractice and product liability are the most relevant legal challenges rising with the use of algorithms which do not provide logical explanations. On the other hand, amongst the most common ethical challenges emerge patient preference, safety, privacy and confidentiality (Rigby, 2019). As these issues have only been partly discussed by the medical community, in order for AI to succeed in the pharmaceutical industry and provide the best support to patients, they must be urgently discussed, and policy recommendations should be offered.

5 Conclusion and Outlook

Predicting the role which AI will have in the future can be difficult. Though, it can be affirmed that up to now, AI has proved to be an effective support within societies and companies. Although the advantages and opportunities which AI offers are undisputable, the shift from traditional systems to AI systems could take longer than expected. As discussed in the previous chapter, Bayer Pharmaceuticals, and in general all companies which adopt AI systems, must carefully analyze the legal and ethical issues in order to avoid complications.

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• Grade: 1,0
• Author: Francesca Bradaschia (Author)
• Publication Year: 2021
• Pages: 10
• Catalog Number: V1012601
• ISBN (eBook): 9783346409379
• Language: English
• Tags: artificial, intelligence, pharmaceutical, healthcare, industry, employment, pharmaceuticals