Looking Ahead to 2024 – The Impact of Artificial Intelligence

The Growing Influence of AI (Artificial Intelligence) and ML (Machine Learning) in Life Sciences: A Shift in Industry Priorities

Like all industries, the life sciences space is seeing tremendous shifts in how we work against a backdrop of rapid technological advancement. Currently, we are experiencing change at a scale not seen since the advent of the Internet itself, all due to the proliferation of solutions powered by artificial intelligence (AI) and machine learning (ML). The latest eClinical Solutions Industry Outlook survey confirms that interest in AI/ML is surging among clinical research leaders, with most respondents pointing to it as the most impactful trend poised to shape research in 2024 and beyond.

Spiking Interest in AI/ML Aligns with Need for Better, Faster Data Management

The increased interest in AI/ML noted by survey respondents may reflect recent hype and conversations around these topics, especially much-discussed generative AI tools like ChatGPT that have gained substantial attention in and beyond the life science sector. It is unlikely, though, that this is the primary reason.

Instead, this shift can be attributed to the increasing complexity of clinical trials coupled with the burgeoning volume and diversity of data types. As a result, there is an ever-growing need for sophisticated analytics to manage this complexity and data variety effectively. At the same time, our industry is undergoing a transformation from traditional clinical data management mindsets to approaches grounded in broader clinical data science. This evolution shows an awareness that there is more data out there, all with the potential to help us run more efficient clinical trials and produce safer, more effective treatments faster. Alongside this, there has emerged a growing industry-wide recognition that we need to adopt innovative data management strategies and technologies to derive meaning and insights from these increasing volumes of data.

Broad Interest in AI/ML Capabilities

Enthusiasm was high among respondents regarding specific applications of AI/ML. There are many ways in which AI/ML can impact how we work with clinical trial data, and the research professionals we heard from had broad interest in various AI functionalities, including study design, real-time monitoring, anomaly detection, predictive modeling for recruitment, risk assessment, and data transformation. That said, the majority also reported being very much in the exploratory stages of planning how they wanted to get started with AI/ML. In fact, many of the conversations we had at our latest Engage conference were about how eClinical Solutions can help researchers begin their AI/ML journey in meaningful, measurable ways.

Bridging the Gap: From Exploration to Realization

Despite the high interest, there is a discernible gap between exploring AI/ML’s potential and harnessing its full value. Reported hurdles to the successful integration of AI/ML are manifold, ranging from data accessibility, concerns over job security and role changes, to uncertainties about quantifying return on investment. This makes sense, as the full potential of AI/ML is still largely unrealized, and it can be overwhelming to think about applying it at scale. This is why it is so important for researchers to begin by applying AI/ML to challenges with direct results, such as anomaly detection, data review, data transformation and formatting, or real-time risk assessments, among others.

In this context, a scalable and interoperable clinical data infrastructure, embedded with AI and other advanced capabilities, becomes crucial. Here, eClinical emerges as a key player, poised to support sponsors who are either beginning or advancing their journey with AI. The elluminate platform is built to make these integrations simple, so that researchers can test out AI/ML capabilities and see what works best. At the same time, the eClinical Solutions Biometrics Services group is adopting a range of artificial intelligence and machine learning use cases to support the data requirements of increasingly complex clinical trials.

Conclusion

The growing interest in AI/ML in the clinical research industry tells us that the field is now ready to be more active in terms of implementing new technologies to help drive trial efficiency. While the journey from exploring the potential of AI/ML to fully realizing its benefits is not without its challenges, the overall direction is clear and promising. With the right infrastructure, support, and innovative mindset, the research space stands on the cusp of a new era of data-driven, AI-powered breakthroughs that could redefine how we approach the development of new therapies. The enthusiasm and forward-looking attitude of industry professionals, as reflected in the survey, are driving forces behind this transformative journey.

For more insights on the most pressing clinical data trends, challenges and opportunities from our 2024 Industry Outlook , view the full report.

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