Artificial Intelligence (AI) has picked up pace massively in the past few years, with multitudes of industries adopting and incorporating it into their processes – thus improving efficiency exponentially. Touted as the Fourth Industrial Revolution, or Industry 4.0, automation of work by machines programmed to do human-like tasks has become the norm.

The pharma industry is no exception to this trend; the stats prove so. A survey by Precedence Research shows that the global AI in the pharma market is predicted to grow from USD 905.91 million in 2021 to USD 9.24 billion by 2030. As we approach 2023, let’s take a closer look at how AI in the pharmaceutical industry is used to mine insights from multiple data sets and make it interoperable across R&D, clinical research, and the supply chain and distribution. 

R&D 

The central goal of drug discovery research is to create quality drug candidates with a short turnaround time with a high probability to make it to clinical development. One of the major roadblocks to that is the astounding scale of the chemical space. It is where the pharma industry can harness the power of AI. Its unparalleled data processing capabilities can help improve the efficiency of research and clinical trials, foster innovation, and enhance decision-making skills – all of which will ensure faster production of life-saving drugs. 

Clinical Research And Trials 

The COVID-19 pandemic turned out to be a catalyst for the rise of AI investments in the pharma industry, and the results have been promising. Clinical research benefited greatly from AI. The automation of clinical trials helps bring down the cycle times and costs while improving efficiency. Given the vast amounts of data available on research, trends, and treatments online, clinicians may find it overwhelming to find the information they need. The Natural Language Processing (NLP) and text mining capabilities that AI and machine learning (ML) can clean, aggregate, and store data in an organized manner. 

A high rate of clinical trial errors has been prolonged since it has accepted as the norm. AI is poised to change that in the coming years. Now, it is starting to develop the analytical skills to identify patients who fit the clinical trial requirements based on their genetic information and predictive analysis. Also, AI can determine the ideal sample size for the trials. The outcome? More efficient and shorter clinical trial lengths than the traditional methods. As such, several pharma companies have tied up with AI service companies. 

Supply Chain And Distribution 

Considering Industry 4.0 is the in-thing now and here to stay, modern technologies like the Internet of Things, AI, and ML will revolutionize how the supply chain and distribution of pharma works. It would not just be for the information flow but also the physical flow throughout the process. The visibility and use of all these data and the underlying information across the entire supply chain will further the optimization of the inventory, minimize excesses and shortages, and reduce drug cycle times to enable real-time decision-making to react to last-minute changes in demands. 

For example, historical big data sets can be used by AI to forecast future trends, indicating everything from the sales volume of the products to market demands and seasonal fluctuations. The data can help companies determine if they have the right amount of raw material for production. And finally, AI can be leveraged by transportation companies to reduce costs associated with poor planning or delays caused by bad weather, traffic jams, etc. In addition, they could increase customer satisfaction levels and optimize inventory management, resulting in more profit from sales in general. 

Closing Thoughts 

Undoubtedly, AI in the pharma industry is here to stay, furthering the development of drug discovery, trials, manufacturing, and distribution processes. Considering the benefits, including cost-effectiveness, improved patient care, and increased profitability along the value chain with AI, it is the future. 

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