The pharmaceutical industry has always been pro-technology and immediately instills new technologies to help deliver safe, reliable drugs to market. If last year’s pandemic has taught us anything, it’s that medicines and vaccines must be readily available for the masses in order to save lives. Artificial intelligence (AI) and machine learning (ML) have been playing a vital role in the pharmaceutical industry and the healthcare sector. There are several practical applications of AI, such as disease identification and diagnosis, helping identify patients for clinical trials, drug manufacturing, and predictive forecasting to name a few. AI is so flexible that it can be inserted in every aspect of the pharmaceutical industry, starting from drug discovery and development to manufacturing and marketing. By inserting AI in the core workflows, pharma companies can make their operations efficient, cost-effective, and hassle-free.
One of the best qualities of an AI is the fact it’s always improving, since it is trained to deliver better outcomes as they continually learn from new data and experience, it has become one of the most powerful tools used in the pharmaceutical industry.
Every pharma company that wishes to get ahead of its competition is using ML algorithms and AI-powered tools to simplify the drug discovery process. AI tools are trained to pinpoint intricate patterns in large datasets, and therefore can be used to solve challenges related to complicated biological networks.
This particular capability of AI tools is ideal for studying the patterns of various diseases and identifying which drug compositions would be most appropriate for treating specific traits of a particular disease.
AI is the key to improving the R&D process. It can assist in designing and discovering new molecules through target-based drug validation, AI can achieve it all. A study conducted by MIT concluded, only 13.8% of drugs are successful in passing clinical trials. On top of that, a pharma company must pay compensation between US$ 161 million to US$ 2 billion just to put a drug through clinical trials and get FDA approval. This is a major reason why pharma companies are opting for AI to improve the success rates of new drugs. This also creates the opportunity to develop more affordable drugs and therapies, and reduce operational costs.
Doctors have started using advanced Machine Learning systems in order to collect, process, and analyze the colossal volumes of patients’ healthcare data. Healthcare providers all over the globe are using ML technology to store sensitive patient data which is secure in a cloud-based centralized system. These are known as electronic medical records (EMRs).
Doctors usually refer to these records when they need to understand the hereditary issues or allergies to certain drugs that can treat a health condition. ML systems are used to store data in EMRs to create real-time predictions for diagnosis purposes and also have the capability to suggest proper treatment to patients.
Pharma companies have started using AI to potentially cure both known diseases like Alzheimer’s and Parkinson’s and other rare diseases. Usually, pharmaceutical companies do not use their resources on finding treatments for rare diseases since the ROI (return on investment) is very low and the cost it takes to develop drugs for treating rare diseases is high. According to Global Genes, almost 95% of rare diseases do not have FDA-approved treatments or cures. However, ever since AI and ML’s have come into the picture and they use innovative tools, the scenario is evolving for the better.
AI and ML are almost becoming a standard with pharma companies to monitor and forecast epidemic outbreaks across the globe. The data which is gathered from disparate sources on the Web are used to feed the AI to study the connection of various geological, environmental, and biological factors on the health of the population of separate geographical locations. Then further try to connect the dots between these circumstances and previous epidemic outbreaks. Such AI/ML models become especially essential for underdeveloped economies that do not have the medical infrastructure and financial framework to deal with an epidemic outbreak.
A great example of an AI application is the ML-based Malaria Outbreak Prediction Model which works as a warning tool to predict any possible malaria outbreak and assist healthcare providers in taking the best course of action to fight it.
AI tools can inspect the past marketing campaigns and collate the results to pinpoint which campaigns remained the most beneficial. This gives companies a bar to design and present marketing campaigns accordingly, while also reducing time and saving money. AI can assist in preventing diseases, in research and development, diagnosis, prevention, predicting, and potentially curing diseases. It is not an understatement to say AI has already become a lifesaver and a solid aid for the healthcare and pharmaceutical industry.
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