From enhanced patient care in healthcare to automated deliveries in logistics, artificial intelligence (AI) is everywhere. As AI transforms every industry, the pharmaceutical sector is no exception. The global market for AI in pharma is projected to grow from $700 million in 2020 to over $9 billion by 2030 (See Figure 1).
In the rapidly growing market, learning about AI and how it can enhance business operations has become vital for organizations to digitally evolve.
Figure 1. AI pharma market growth from 2020 to 2030
This article explores the top 4 use cases and examples of AI in the pharmaceutical sector to better prepare business leaders for their AI investments.
Drug Discovery
AI models can be taught biochemistry to help in discovering new drugs and making the drug discovery process more efficient. Benefits of using AI in drug discovery include:
- AI does not rely on predetermined targets in drug discovery, making it subjectively unbiased.
- AI uses state-of-the-art algorithms created by combining biology and computer processing power.
- Through AI, drug screening can be done virtually, saving a significant amount of time and resources.
- AI-enabled computer vision models can also help accurately analyze patient reports to help physicians create personalized treatment options.
See how AstraZeneca uses AI and Machine Learning to elevate drug discovery:
You can also check our list of drug discovery software to find the option that best suits your needs.
Computer vision for drug manufacturing
AI-based computer vision systems have various implications in drug manufacturing.
While producing drugs, the quality assurance of each drug unit can be a time-consuming and tedious task if done manually.
Computer vision-enabled systems use image processing to examine drugs on conveyor belts to detect defects (differences in shape and color) with much higher speed and accuracy. They can also detect anomalies in the packaging of the drugs. This also allows pharma companies to eliminate possible contaminations caused by the human touch.
The image below is an AI-powered computer vision system detecting defective medicine as they move on the conveyor belt.
Sponsored
A high-performance computer vision system for drug quality assurance requires high-quality data annotation because errors in the system can have dire consequences. Ango.AI specializes in medical data annotation and can help provide that quality. They offer an all-in-one data labeling platform called Ango Hub, which offers cloud and on-premise work options. They also offer Ango Services, delivered by their specialized team with regular quality checks and data protection.
Predictive forecasting
One of the prominent uses of artificial intelligence in the pharma sector is predicting pandemics and seasonal illnesses. This helps pharmaceutical companies to prepare their supply chains to eliminate volatility and easily match demand with supply.
See how Emory University and Google use AI to predict sepsis:
To improve your pharma supply chain planning, you can check our drug inventory management software list to find the option that best suits your business.
Clinical trials for drugs
Here are some ways AI can help improve clinical trials:
- Candidate recruitment: AI can identify the appropriate candidates for drug trials based on historical records, diseases, and demographic data.
- Trial design: With the ability to analyze and organize a vast amount of data on previous clinical trials, AI can help extract meaningful insight to design effective clinical trials.
- Trial monitoring: AI can also help monitor the patients while they are being treated. AI combined with IoT-enabled wearable devices can help integrate this data to provide insights on the effectiveness of the treatment.
Further reading
If you have more questions, feel free to contact us:
From enhanced patient care in healthcare to automated deliveries in logistics, artificial intelligence (AI) is everywhere. As AI transforms every industry, the pharmaceutical sector is no exception. The global market for AI in pharma is projected to grow from $700 million in 2020 to over $9 billion by 2030 (See Figure 1).
In the rapidly growing market, learning about AI and how it can enhance business operations has become vital for organizations to digitally evolve.
Figure 1. AI pharma market growth from 2020 to 2030
This article explores the top 4 use cases and examples of AI in the pharmaceutical sector to better prepare business leaders for their AI investments.
Drug Discovery
AI models can be taught biochemistry to help in discovering new drugs and making the drug discovery process more efficient. Benefits of using AI in drug discovery include:
- AI does not rely on predetermined targets in drug discovery, making it subjectively unbiased.
- AI uses state-of-the-art algorithms created by combining biology and computer processing power.
- Through AI, drug screening can be done virtually, saving a significant amount of time and resources.
- AI-enabled computer vision models can also help accurately analyze patient reports to help physicians create personalized treatment options.
See how AstraZeneca uses AI and Machine Learning to elevate drug discovery:
You can also check our list of drug discovery software to find the option that best suits your needs.
Computer vision for drug manufacturing
AI-based computer vision systems have various implications in drug manufacturing.
While producing drugs, the quality assurance of each drug unit can be a time-consuming and tedious task if done manually.
Computer vision-enabled systems use image processing to examine drugs on conveyor belts to detect defects (differences in shape and color) with much higher speed and accuracy. They can also detect anomalies in the packaging of the drugs. This also allows pharma companies to eliminate possible contaminations caused by the human touch.
The image below is an AI-powered computer vision system detecting defective medicine as they move on the conveyor belt.
Sponsored
A high-performance computer vision system for drug quality assurance requires high-quality data annotation because errors in the system can have dire consequences. Ango.AI specializes in medical data annotation and can help provide that quality. They offer an all-in-one data labeling platform called Ango Hub, which offers cloud and on-premise work options. They also offer Ango Services, delivered by their specialized team with regular quality checks and data protection.
Predictive forecasting
One of the prominent uses of artificial intelligence in the pharma sector is predicting pandemics and seasonal illnesses. This helps pharmaceutical companies to prepare their supply chains to eliminate volatility and easily match demand with supply.
See how Emory University and Google use AI to predict sepsis:
To improve your pharma supply chain planning, you can check our drug inventory management software list to find the option that best suits your business.
Clinical trials for drugs
Here are some ways AI can help improve clinical trials:
- Candidate recruitment: AI can identify the appropriate candidates for drug trials based on historical records, diseases, and demographic data.
- Trial design: With the ability to analyze and organize a vast amount of data on previous clinical trials, AI can help extract meaningful insight to design effective clinical trials.
- Trial monitoring: AI can also help monitor the patients while they are being treated. AI combined with IoT-enabled wearable devices can help integrate this data to provide insights on the effectiveness of the treatment.
Further reading
If you have more questions, feel free to contact us: