Artificial intelligence (AI) has the potential to generate $100 billion across the healthcare and pharmaceutical industry, according to Mckinsey. The use of artificial intelligence in drug manufacturing, cell culture process development, and research improves efficiency, cost-effectiveness, and decision-making. At present, Artificial intelligence is helping the following corner of the pharmaceutical industry to stay competitive.

1.   Drug Discovery Process:

Drug discovery is an expensive and competitive process. Pharma companies are using artificial intelligence to their advantage for staying in the competition. AI has the ability to recognize patterns in large data collection and it also helps in establishing the best drug compositions for diseases. Top pharmaceutical industry players are using the MIT-machine learning consortium to increase the efficiency of their drug discovery process. The players include Pfizer, Bayer, Lilly, and Novartis. This consortium called Machine Learning for Pharmaceutical Discovery and Synthesis is also working on insights for the optimization and designing of drug synthesis.

2.   Effective Drug Development:

According to research, only 14% of the drugs are approved by the FDA. It is a very small percentage of the total drugs that formed in a year. Each newly formed drug costs around 1 billion in production and approval costs. AI can play an important role in improving the quality of drugs and hence increase the approval percentage of drugs. It saves a lot of money and effort. AI improves success rates by:

• Ensuring high-quality
• Increased automation
• Reducing the wastage of materials
• Fixing supply chains
• Increasing reuse value
• Reducing operational costs
• Forecasting demands

3.  Personalized treatments:

The personalized treatment is based on real-time data of the specific patient. AI solutions can help with real-time data in the following ways:

• Setting up an electronic medical record system
• Drive customized treatment options by providing real-time data to the doctors about the patient’s history
• Successfully diagnosing the diseases
• Remote patient monitoring via AI wearables.

Remote patient Monitoring (RPM) is projected to reach 117 billion dollars by 2025.

4.   Prediction of the Epidemic Outbreak:

Machine learning models are now being used to predict the onset of epidemics. For example, a data mining algorithm, SVM, successfully predicted the onset of the malaria epidemic with a low error rate. AI can also assist in the prevention of an epidemic once it is predicted. Here is how:

• AI tools work on collecting real-time information from various sources available online.
• The predictive tools study multiple environmental, behavioral, and other factors that influence the epidemic.
• AI provides relevant trends, solutions, and patterns.

Hence, AI can be used to warn people about any impending health conditions and give them a guideline for prevention and safety.

5.   Marketing:

The Pharma industry is driven by sales. AI can be used by pharmaceutical companies to approach targeted and personalized marketing strategies that might help them in penetrating the market deeply. AI tools can be used to

• Collect customer data and analyze his needs.
• Innovate marketing strategies that are aligned with customers’ needs
• Analyze the performance of marketing campaigns
• Perform a competitive analysis of previous marketing campaigns.
• Prediction of the success rate of various marketing campaigns.