IQVIA Podcast, Dynamic reports and dashboards, powered by AI and Machine Learning can provide faster answers to questions that go beyond the …

Clinical research and drug development professionals are largely aware of artificial intelligence (AI), machine learning (ML), and other advanced analytical tools. However, many are not yet aware of their full potential, or how to best put such tools to work. Lucas Glass, vice president of the IQVIA Analytics Center of Excellence, spoke with Outsourcing-Pharma about how the adoption of AI/ML is evolving, what people in the field need to understand, and what might lie ahead. OSP: Could you please tell us what the biggest challenge has been facing professionals in your corner of the life-sciences industry? LG: The biggest challenge facing the industry is user empathy between the technologists and the clinical trial professionals.

After doubling down on digital with decentralized, or siteless, clinical trials during the COVID-19 pandemic, the contract research service provider has added AI to its contact center team to help human agents respond to inquiries. IQVIA unveiled the tool Wednesday. IQVIA's medical information contact center team responds to inquiries from consumers, patients and healthcare professionals in 50 languages across more than 170 countries 24/7, and it needs some help. In step AI-powered virtual agents, which will aid humans in triaging and answering questions about new products and related therapies. The team also monitors product quality and safety by capturing information on adverse events and other product complaints.

The days of the blockbuster drug might not be over, but they are dwindling. As markets become more crowded, pharmaceutical companies are focusing their drug development strategies on specialized populations, such as individuals with rare diseases or those with genetic subsets of chronic conditions like cancer and diabetes and cardiovascular diseases. Identifying a specific subgroup of patients from a niche population poses challenges for many companies. The integration of artificial intelligence (AI) and machine learning (ML) in a drug development and launch playbook offers new possibilities to find the right subpopulation(s) for product targeting as a way to ensure a successful launch. With the growth of big data and exponential advancements in technology, we are seeing more opportunities to apply artificial intelligence and machine learning in decision-making for drug development.

Artificial intelligence and machine learning tools are transforming how clinical development occurs by delivering significant time and cost efficiencies while providing better and faster insights to inform decision-making. Advances in analytics technology coupled with the availability and integration of vast amounts of healthcare data have already helped automate processes and improve data quality across dozens of clinical development efforts. As these tools evolve, new opportunities will continue to emerge that drive further benefits to the clinical research landscape. Applications of AI and ML in healthcare are expected to grow to nearly $8 billion by 2022, up from $667.1 million in 2016, and almost half of global life science professionals say they are either using or interested in using AI in their research.[1] Despite this growth, the industry continues to struggle with what these technologies are and how they work.

Artificial intelligence and machine learning tools are transforming how clinical development occurs by delivering significant time and cost efficiencies while providing better and faster insights to inform decision-making. Advances in analytics technology coupled with the availability and integration of vast amounts of healthcare data have already helped automate processes and improve data quality across dozens of clinical development efforts. As these tools evolve, new opportunities will continue to emerge that drive further benefits to the clinical research landscape. Applications of AI and ML in healthcare are expected to grow to nearly $8 billion by 2022, up from $667.1 million in 2016, and almost half of global life science professionals say they are either using or interested in using AI in their research.[1] Despite this growth, the industry continues to struggle with what these technologies are and how they work.