How Artificial Intelligence Can Transform Clinical Trials

1. How Artificial Intelligence Can Transform Clinical Trials The clinical development market has become more competitive; with stricter regulatory standards and a greater focus on trial oversight and patient safety. The stake is more important than ever in clinical research. The clinical trials industry needs disruption more than ever. This is where artificial intelligence (AI) and new technologies such as cloud and data lakes come in. By reducing costs, improving data quality, and shortening test times, the artificial intelligence, machine learning and deep learning techniques increase the efficiency of clinical trials and optimize end-to-end clinical trial processes, and help pharmaceutical companies bring new drugs and therapies to market faster. AI has a number of important applications that can disrupt any other phase of the clinical development process - from finding biomarkers and genetic signatures that cause disease to introducing new diagnostic tools and treatments for incurable diseases. By developing and supporting human intelligence, exploiting data and predicting clinical trials to identify trends, risks and outcomes, artificial intelligence combined with big data has the potential to solve even more of the world's most challenging challenges reviews of today's clinical trials. How Artificial Intelligence Can Transform Clinical Trials Make clinical trials smart: AI-based protocol designs based on AI algorithms and deep learning techniques can make clinical trials smarter in a number of ways (see figure). Additionally, AI can provide pharmaceutical researchers with additional predictive data that can be used to determine whether taking a drug leads to a positive or negative outcome, whether patients drop out of studies, and whether or not studies are successful. Using predictive algorithms and AI techniques, trial risks can be mitigated quickly before they become major issues, helping to keep trials on track and on budget. Optimize clinical study processes: With nearly half of all study sites under- enrolled and around 30% of patients dropping out of clinical trials, recruiting and retaining patients is one of the biggest challenges facing the clinical trials industry today. AI can help the pharmaceutical industry meet both of these challenges. By extracting relevant information from the EMR, researching physician notes, reading binary data from medical images and scans, and comparing it to a study's inclusion and exclusion criteria, the AI can more

2. effectively identify patients who may be included in a clinical study. During trials, AI can help by using Real World Evidence (RWE) from medical, lab, and prescription data to predict which patients are at risk of dropping out. Learn more about smarter decision making: The promise of recognizing patterns using AI, machine learning, and deep learning techniques when large amounts of clinical research data is managed and used to accelerate drug discovery is compelling. AI can capture millions of words from texts, molecules, genomic sequences and images in minutes, aggregating data and making hypotheses beyond human capabilities. Using these tools, biotechnology or pharmaceutical companies could combine all the preliminary data to determine in which indication a drug is more likely to be successful. Conclusion It's not uncommon for researchers to think, "Why change when it works?" However, this is not the case in clinical development. Less than 10% of studies are completed on time and the cost of developing new drugs is skyrocketing. However, AI can help reverse these trends and allow sponsors to optimize clinical trials and accelerate new product development. From maximizing patient recruitment and retention to improving data collection and risk monitoring, artificial intelligence can disrupt any phase of the clinical trial process. Researchers using these new technologies can dramatically reduce the time to market for life-saving drugs and deliver huge benefits to patients who need them most.