Satyakam Mohanty


Today, AI is being implemented in every industry that we can think of. Some use it as a buzzword to generate hype while others truly understand the full potential of the value it brings.

To keep up with the 21st century, the pharmaceutical industry is constantly on research mode and AI is drawing plenty of attention in the industry. The widespread success of AI in several industries has led many big pharma companies to invest in it.

AI leaves a deep impact on pharma and will positively affect drug invention and development—especially cost—in the long-run. There are several modified applications that apply AI across the pharmaceutical business model. AI is also used to envisage the curative properties of new drugs before the drug is tested. Well, the pharma world is certainly, though gradually, getting more dependent on AI, and here is a closer look at it.

Expectations from AI in Pharma?

AI assists and provides solutions to researchers to study vast amounts of raw data, process them, and even find solutions that are a perfect fit for patients. AI has also made inroads into how these companies market their products and how they use data science to give informed actions to customers. AI contributes in improving the speed and drug discovery processes in pharma. Experts believe that AI will assist in better decision making, improve delivery and make the pharma industry more efficient than ever.

Areas where AI will impact Pharma:

Competitive Intelligence: AI helps pharma companies to go above and beyond in terms of data interpretation and information on competition as well. The generation of in-depth information allows big pharma companies to identify and analyze their core strength. It will also aid in unearthing new needs, which will guide the R&D process accordingly. Competitive intelligence is also beneficial for internal assessments. It also serves as a base to set priorities, benchmark and eventually fulfill market needs.

Disease Diagnosis: With the help of AI, it becomes easier to run clinical tests, diagnose diseases and provide the most effective treatment for a particular disease. As it can interpret test results, AI can also look through various sources including publications to correctly diagnose critical ailments.

Drug Discovery: With the industry spending well over 1 billion for a new drug, the integration of AI helps to reduce the time taken for drug generation. AI helps in predicting the efficacy and safety of a molecular form. It does this by providing mechanistic insight into the disease and can also identify possible targets. It gives researchers a much broader chemical pallet so they can select the best molecules for drug testing and delivery.

Sales Planning and Growth: With AI, Customer Engagement is better than ever before with applications focusing on multichannel engagement. They can predict the best channels, best message and the best timing for every single customer. AI helps in customer segmentation and boosts the productivity of sales reps through planogram solutions. Above all, it helps the companies to better assess their performance through benchmarks.

Drug Repurposing: This is another place where AI has a lot of impact. With millions of clinical data points already available, AI algorithms find out suitable drugs, which can be repurposed for use as a medicine for an alternative disease.

Outbreak prediction: With its vast data processing capabilities, AI can be used to predict outbreaks by using historical data, geographical data, social cause, and search information.

Running clinical trials: Predictive analytics can be used to help find candidates for clinical trials. It can combine elementary data, genetic information, e-records and even social media data to analyze a candidate accurately. Furthermore, it is useful for remote monitoring and real-time data retrieval of the candidates.

AI has the potential to revolutionize the pharmaceutical industry, leading to quicker decision making across processes from drug discovery, launch effectiveness, sales planning to competitive intelligence.


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