In recent years, the use of artificial intelligence (AI) in the pharmaceutical and biomedical industries has shifted from science fiction to science reality. More and more pharmaceutical and biotech companies are adopting more efficient automated processes, incorporating data-driven decisions, and using predictive analytics tools. The next evolution of this advanced data analysis approach combines artificial intelligence and machine learning.
The goal of this type of AI technology is to find hidden patterns and gather insights from huge amounts of data in a way that no human can. The use of AI for data discovery and analysis has been transforming many industries including Pharma Commercial Insights. Its uses range from drug discovery to manufacturing process automation to clinical applications (such as medical imaging and surgical robotics).
Most AI solutions used in healthcare today are based on human-generated data science algorithms. This type of AI uses multivariate data analytics backed by evidence of past experience. For example, it can combine population-based treatment outcomes with individual patient clinical and medical data to create alternative treatments and drug combination recommendations.
Another level of AI is machine learning, which is based on so-called neural networks that mimic the way the human brain works, but are capable of making decisions much faster and more accurately. Machine learning uses data-driven algorithms that enable software applications to become highly accurate in predicting outcomes without explicit programming.
From early-stage drug discovery to prescribing treatment options, the use of AI continues to grow in the biopharmaceutical industry, with market volume expected to reach $10 billion by 2024 ( including AI-based medical imaging, diagnostics, AI personal assistants, drug discovery, and genomics).
In addition to helping to understand clinical trial data, another application of artificial intelligence in the Pharmaceutical Industry is finding patients participating in a trial. Using advanced predictive analytics, AI can analyse genetic information to identify the right group of patients for the test and determine the optimal sample size. Some AI technologies can read free-form text entered by patients in clinical trial applications, as well as unstructured data such as physician notes and admission documents.
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