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AI Business Case
Artificial Intelligence (AI) is no longer a futuristic tool that only appears in science fiction movies. The future is happening right now, and companies in the pharmaceutical industry must meet increasingly stringent requirements (high quality standards, changing market demand, etc.)
The only way to meet those requirements is to integrate pharmaceutical laboratories with the new capabilities and functionalities of AI systems.
Keep digging with us and find out everything you need to know about AI applications in business.
Breaking down the concept of AI
The most common and globally accepted definition of Artificial Intelligence tells us that “AI is the result of the combination of algorithms for data processing. Pursuing the goal of trying to imitate human reasoning.”
However, in reality, today’s AI is much bigger than that definition. As time goes by, the world of AI continues to evolve, encompassing new methodologies and tools such as: Machine Learning, Deep Learning, Data Science or Big Data.
Subsets of Artificial Intelligence: Machine Learning
Machine Learning (ML) is broadly defined as a subset of Artificial Intelligence that is basically trained to recognize patterns based on data and experience to mimic human behavior. Its goal is to perform complex tasks in a similar way to how humans solve problems.
Inside Machine Learning: Deep Learning
Deep learning is a subset of machine learning that uses artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data to mimic the learning process of the human brain.
Teach computers to do what comes naturally to humans: learn by example.
The impact of Artificial Intelligence in figures
The global Artificial Intelligence market revenue is estimated to grow significantly from 2018 to 2030. Market research firm IDC projected that the global AI market will reach a size of more than half a trillion US dollars by 2024. The precedent research suggests that the market will grow to over US$1.5 trillion by 2030.
Improving quality control and supervision are among the most important use cases for AI in the pharmaceutical industry. In addition, it will soon be possible to use AI in this industry to offer patients personalized preventive risk screenings, opening up possibilities to find the most suitable options for patients.
Artificial Intelligence Applications
Design and discovery of new drugs
The use of AI for drug design is increasing rapidly and plays a prominent role in the identification and validation of drug targets.
Investigation and development
Many large pharmaceutical companies are currently using advanced AI-based systems and tools along with machine learning algorithms to smooth and boost drug research and development processes.
Disease prevention
Pharmaceutical organizations can also use AI solutions to develop drugs and treatments for rare diseases like Alzheimer’s and Parkinson’s.
Identifying clinical trials
Identifying drug candidates that are in final clinical trials is one of the most exciting uses of AI for pharmaceutical companies. Help companies test thousands of samples faster and automatically discover how patients are responding during clinical trials.
Artificial Intelligence systems could also bring many opportunities and advantages at a commercial level for pharmaceutical companies. Some of the most interesting applications to consider for the future of the industry are:
Predictive forecasting
AI systems have the ability to predict future behavior patterns based on analysis of historical data. Offer laboratories the opportunity to improve their multichannel strategies and actions, creating a culture of good practices in the commercial area to gain commercial efficiency.
Identify better sales networks
Another interesting use of AI capabilities in the commercial area of a pharmaceutical company is in the definition and management of networks and sales routes.
Thanks to AI optimization methods, companies can articulate more precise and optimal definitions and adaptations of territories. Helping laboratories reduce costs.
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