8 Important Applications of AI in Pharma Industry in 2020

Thanks to Data Science, we have amidst us such innovations that were once the components of science fiction. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the industry and led to the invention of things like virtual assistants, self-driving cars, smart homes, chatbots, surgical bots, and so much more.

According to Tractica, the global artificial intelligence software market is forecast to grow from $10.1 billion in 2018 to $126 billion by 2025. In a data-driven age where companies across all parallels of the industry are adopting Big Data and Artificial intelligence technologies, the pharmaceutical industry is no exception.

When it comes to the pharmaceutical industry, AI presents an ocean of untapped opportunities for business transformation. Big Data, along with AI-powered analytics, has brought about a radical shift in the innovation paradigm of the pharma sector.

Artificial Intelligence has the potential to foster innovation while simultaneously improving productivity and delivering better outcomes across the value chain. AI can significantly improve the value proposition of pharma companies by driving innovation and the creation of new business models.

AI can be implemented in almost every aspect of the pharmaceutical industry, right from drug discovery and development to manufacturing and marketing. By leveraging and implementing AI systems in the core workflows, pharma companies can make all business operations efficient, cost-effective, and hassle-free.

The best part is that since AI systems are designed to deliver better outcomes as they continually learn from new data and experience, they can be a powerful tool in the research and development wing of the pharmaceutical industry.

Let’s look at some of the most mention-worthy applications of Artificial Intelligence in the pharmaceutical industry:

Pharma companies around the world are leveraging advanced ML algorithms and AI-powered tools to streamline the drug discovery process. These intelligent tools are designed to identify intricate patterns in large datasets, and hence, they can be used to solve challenges associated with complicated biological networks.

This capability is excellent for studying the patterns of various diseases and recognizing which drug compositions would be best suited for treating specific traits of a particular disease. Pharma companies can accordingly invest in the R&D of such drugs that have the highest chances of successfully treating a disease or medical condition.

AI holds the potential to improve the R&D process. From designing and identifying new molecules to target-based drug validation and discoveries, AI can do it all.

According to an MIT study, only 13.8% of drugs are successful in passing clinical trials. To top that, a pharma company has to pay anywhere between US$ 161 million to US$ 2 billion for a drug to get through the complete process of clinical trial and get FDA approval. These are the two main reasons why pharma companies are increasingly adopting AI to improve the success rates of new drugs, create more affordable drugs ad therapies, and, most importantly, reduce operational costs.

Doctors can use advanced Machine Learning systems to collect, process, and analyze vast volumes of patients’ healthcare data. Healthcare providers around the world are using ML technology to store sensitive patient data securely in the cloud or a centralized storage system. This is known as electronic medical records (EMRs).

Doctors can refer to these records as and when they need to understand the impact of a specific genetic trait on a patient’s health or how a particular drug can treat a health condition. ML systems can use the data stored in EMRs to make real-time predictions for diagnosis purposes and suggest proper treatment to patients.

Since ML technologies possess the ability to process and analyze massive amounts of data quickly, they can help quicken the diagnosis process, thereby helping save millions of lives.

Pharma companies can use AI to develop cures for both known diseases like Alzheimer’s and Parkinson’s and rare diseases. Generally, pharmaceutical companies do not spend their time and resources on finding treatments for rare diseases since the ROI is very low compared to the time and cost it takes to develop drugs for treating rare diseases.

According to Global Genes, nearly 95% of rare diseases don’t have FDA approved treatments or cures. However, thanks to AI and ML’s innovative abilities, the scenario is rapidly changing for the better.

AI and ML are already used by many pharma companies and healthcare providers to monitor and forecast epidemic outbreaks across the globe. These technologies feed on the data gathered from disparate sources in the Web, study the connection of various geological, environmental, and biological factors on the health of the population of different geographical locations, and try to connect the dots between these factors and previous epidemic outbreaks. Such AI/ML models become especially useful for underdeveloped economies that lack the medical infrastructure and financial framework to deal with an epidemic outbreak.

A good example of this AI application is the ML-based Malaria Outbreak Prediction Model that functions as a warning tool predicting any possible malaria outbreak and aid healthcare providers in taking the best course of action to combat it.

Remote monitoring is a breakthrough in the pharma and healthcare sectors. Many pharma companies have already developed wearables powered by AI algorithms that can remotely monitor patients suffering from life-threatening diseases.

For instance, Tencent Holdings has collaborated with Medopad to develop an AI technology that can remotely monitor patients with Parkinson’s disease and reducing the time taken to perform a motor function assessment from 30 minutes to three minutes. By integrating this AI technology with smartphone apps, it is possible to monitor the opening and closing motions of the hands of a patient from a remote location.

On detecting hand movement, the smartphone camera will capture it to determine the severity of the symptoms (Parkinson’s). The frequency and amplitude of the movement will determine the severity score of the patient’s condition, thereby allowing doctors to change the drugs as well as the drug doses remotely.

In case the conditions become worse demanding a treatment upgrade, the AI will send an alert to the doctor and arrange a checkup. Remote setups like these help eliminate the need to travel back and forth to the doctor’s clinic, saving patients the hassle of traveling and waiting.

Pharma companies can implement AI in the manufacturing process for higher productivity, improved efficiency, and faster production of life-saving drugs. AI can be used to manage and improve all aspects of the manufacturing process, including:

AI can replace the time-consuming conventional manufacturing techniques, thereby helping pharma companies to launch drugs in the market much faster and at cheaper rates as well. Apart from increasing their ROI substantially by limiting the human intervention in the manufacturing process, AI would also eliminate any scope for human error.

Given the fact that the pharmaceutical industry is a sales-driven sector, AI can be a handy tool in pharma marketing. With AI, pharma companies can explore and develop unique marketing strategies that promise high revenues and brand awareness.

AI can help to map the customer journey, thereby allowing companies to see which marketing technique led visitors to their site (lead conversion) and ultimately pushed the converted visitors to purchase from them. In this way, pharma companies can focus more on those marketing strategies that lead to most conversions and increase revenues.

AI tools can analyze past marketing campaigns and compare the results to identify which campaigns remained the most profitable. This allows companies to design the present marketing campaigns accordingly, while also reducing time and saving money. Furthermore, AI systems can even accurately predict the success or failure rate of marketing campaigns.

Although AI is rapidly finding applications in the pharma industry, the process of transformation is not without challenges. Usually, the current IT infrastructure of most pharma companies is based on legacy systems that aren’t optimized for AI.

Moreover, the integration and adoption of AI demand industry expertise and skills, something that is still not readily available. However, the process of AI adoption in the pharma sector can be made easy by taking these steps:

To conclude, the scope of AI in the pharmaceutical industry looks highly promising. As an increasing number of pharma companies adopt AI and ML technologies, it will lead to the democratization of these advanced technologies, thereby making it more accessible for small and medium-sized pharma companies as well.

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