Artificial intelligence (AI) is rapidly evolving and poised to significantly impact various industries, including clinical research. In just months, AI models have progressed from basic algorithms to complex neural networks, and AI now spans numerous applications across industries.
At YPrime’s 2023 Innovation Network Gathering, David Sjolin, Vice President of Machine Learning at YPrime Labs, explored the impact of AI on society and highlighted the three fundamental levels of the technology, which include:
- Basic Research—Focuses on theoretical foundations and algorithm development within academia and big tech companies
- Applied Research—Uses discoveries to solve real-world problems like drug discovery and autonomous driving
- Foundational Models—General-purpose AI systems that require minimal data and specialized skills, becoming increasingly accessible despite some challenges
Data is the backbone of clinical research, but managing large amounts of structured and unstructured data from multiple sources while ensuring data security is a massive undertaking. AI advances can significantly help researchers extract meaningful insights efficiently and accurately. AI-powered predictive models use machine learning algorithms to identify patterns and predict outcomes based on historical data—accelerating the drug development process.
As we work to reduce drug time to market, ensuring that AI is implemented in a safe and ethical manner remains crucial. The FDA advocates for Good Machine Learning Practice (GMLP) to ensure AI systems function with appropriate human oversight. While AI can offer valuable suggestions, it falls upon us—the human experts—to ultimately make the final decisions, especially in sensitive domains such as clinical trials.
AI has already impacted clinical research in drug discovery and patient recruitment. Foundational models can streamline various business functions, from project management to data analysis, enhancing productivity and decision-making processes. However, AI should not be viewed as a ‘one-size-fits-all’ approach. It’s essential to understand specific problems and tailor the solution accordingly.
AI can enable countless benefits by automating processes, including identifying potential patients for clinical trials. AI can save time and resources—while improving accuracy. It also enables real-time patient data monitoring, allowing researchers to recognize trends and adapt protocols quickly. The future of AI depends on having the right data and the right partners. Training employees on prompt engineering and other AI-related skills can significantly enhance productivity. Organizations must identify appropriate tools for different roles and ensure compliance with safety standards.
Engaging in discussions about AI’s societal impact and addressing potential issues is crucial for maximizing its benefits. Many people assume that AI is too expensive and data-intensive to be practical, but that could not be further from the truth. AI is here, and forward-thinking companies are already using it to improve efficiency and maintain a competitive edge. As we explore the future of AI in society—and in particular, clinical research—focusing on safety, transparency, and ethics will remain key.
Watch a video of David’s talk below.
Innovation and quality are at the core of everything we do as an industry to improve clinical trials. That’s why every year, we host the Innovation Network Gathering—a forum for brilliant minds from various backgrounds to come together to engage in thought-provoking discourse that sparks new opportunities for growth and change.
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