How Artificial Intelligence (AI) Is Transforming Life Sciences
Written by: Will Mandis, SVP and General Manager, Veradigm, and Cheryl Reifsnyder, PhD
Artificial intelligence (AI) is emerging as a game-changer in life sciences, transforming how we discover new therapies, conduct clinical trials, monitor drug safety, and deliver personalized medicine. Biopharmaceutical companies are increasingly leveraging AI techniques, including machine learning, deep learning, and natural language processing (NLP), to analyze vast and complex healthcare datasets and uncover insights that were previously out of reach. The value of AI in the life science industry is expected to reach $2.25 billion in 2024 and increase another fourfold by 2032.
Understanding AI’s transformative influence on the industry has become essential for those in life sciences.
AI in drug discovery and development
Drug discovery and development, aimed at identifying new medicines to treat human disease, represents one of the most challenging endeavors in life sciences. This process has traditionally been both time-intensive and costly. The journey from initial molecule exploration to market launch typically spans 10 to 12 years, with research and development costs reaching substantial levels—for instance, average research and development costs for the top 12 biopharmaceutical companies reached $2.168 billion per drug in 2018, marking a significant increase from $1.188 billion in 2010. This reality has created a pressing need for more efficient and cost-effective approaches to bringing new drugs to market.
AI is emerging as a promising tool to help address these challenges. Although its impact varies across different stages of the drug discovery and development process, at its core, AI’s contribution stems from its ability to analyze large datasets more rapidly than traditional methods. Modern AI systems can process information from multiple sources simultaneously—including scientific literature, clinical trial data, conference proceedings, and public databases—to help researchers identify potential therapeutic candidates. This capability also enables researchers to screen existing compounds more efficiently to identify promising molecules for further investigation.
AI also supports the optimization of drug candidates through its ability to analyze molecular structures and predict potential interactions. By processing data about drugs’ chemical structures and biological effects, AI models can help researchers understand how modifications might affect a drug’s properties. This includes predicting possible interactions between drug candidates and their intended biological targets, as well as potential unintended interactions that might cause side effects. However, these predictions serve as guides rather than guarantees; extensive laboratory testing remains essential.
In clinical trials, AI is proving valuable for several aspects of the process, including trial design and patient recruitment. The traditional challenge of finding suitable trial participants often leads to significant delays—historically, difficulties in patient recruitment have contributed to many trial cancellations. AI can help address this by analyzing patient records to identify potential trial candidates more efficiently. During trials, AI-powered tools can assist in data collection and management, particularly when integrated with NLP to extract information from clinical notes. This technology also supports monitoring adverse events, helping research teams identify potential safety concerns more quickly and effectively.
Integrating remote monitoring tools, such as wearable devices, has created new opportunities for AI applications in clinical trials. These systems can collect continuous health data and monitor protocol adherence, providing researchers with richer datasets for analysis.
Through these various applications, AI is helping to make the drug discovery and development process more efficient. However, it’s important to note that it augments rather than replaces traditional research methods. The technology serves as a powerful tool for generating hypotheses, identifying promising directions, and processing large amounts of data, but the fundamental work of laboratory validation, clinical testing, and careful safety evaluation remains essential to bringing new drugs to market.
AI in pharmacovigilance
After a drug reaches the market, ensuring its ongoing safety becomes a critical priority through pharmacovigilance—the systematic monitoring and assessment of drug safety signals. This process has traditionally relied on manual review of adverse event reports, medical literature, and clinical data. AI is increasingly supporting these efforts by helping process and analyze the diverse streams of safety information that companies receive.
Modern pharmacovigilance systems, enhanced by AI and particularly NLP, can assist in processing information from various sources, including clinical reports, medical literature, and healthcare records. These tools help safety teams identify potential patterns that might indicate emerging safety concerns, which human experts can then investigate more thoroughly. This combination of AI-assisted monitoring with human medical expertise helps companies maintain comprehensive safety surveillance while managing the growing volume of safety data.
The economic impact of adverse drug reactions and their management is substantial, with estimates at over $30 billion annually in the U.S. alone. While AI tools cannot prevent all adverse events, they can help safety teams process information more efficiently and potentially identify some safety signals earlier in the drug development process. This improved efficiency in safety monitoring contributes to both patient protection and resource optimization.
AI in personalized medicine
Healthcare has traditionally relied on treatment approaches that work well for the average patient but may not be optimal for everyone. Personalized medicine aims to refine this approach by considering individual patient characteristics, including genetic factors, environment, lifestyle, and other health conditions. AI is helping advance this field by supporting the analysis of complex patient data to identify patterns that might inform treatment decisions.
By analyzing large datasets of patient information, AI systems can help identify potential relationships between patient characteristics and treatment outcomes. This analysis can provide insights that help healthcare providers make more informed decisions about treatment options. For instance, patterns in the data might suggest which patients are more or less likely to respond well to particular therapies.
AI systems can also support preventive care by analyzing patient records to identify risk factors for various conditions. This analysis considers multiple data points, including medical history, genetic information where available, and lifestyle factors.
While AI cannot definitively predict who will develop specific conditions, it provides a valuable tool to help healthcare providers identify patients who might benefit from closer monitoring or preventive interventions.
AI’s role in life sciences
The development of AI capabilities in recent years has created new opportunities to support life science research and development. However, it’s important to understand that AI serves as a tool to augment human expertise rather than replace it. AI systems can help analyze complex datasets more quickly than traditional methods, supporting activities such as compound screening, clinical trial planning, and safety monitoring. This improved analytical capability can help reduce some aspects of research time and cost, but the fundamental requirements for careful scientific validation, clinical testing, and safety evaluation remain unchanged.
The integration of AI tools across various aspects of life sciences continues to evolve, with new applications emerging as technology develops. Success in this field requires careful attention to data quality, validation of AI-generated insights, and close collaboration between technology experts and life science professionals. As these tools mature, they will likely continue to enhance our ability to develop and monitor new therapies while maintaining the high standards required for patient safety and scientific excellence.
Veradigm’s NLP-enriched EHR data solutions
Veradigm is deeply committed to supporting AI’s life science benefits. For instance, Veradigm has completely integrated advanced NLP into our electronic EHR dataset to create a collection of real-world, research-ready data derived directly from community-based physician EHRs. Veradigm’s proprietary NLP models extract meaningful elements from unstructured and semi-structured data, providing deeper insights about the patient cohort of interest.
Veradigm is also using NLP to deliver social determinants of health (SDoH) data, providing life science researchers with insights into socioeconomic, environmental, and behavioral factors, including income levels, education, housing stability, and social support, areas that can be overlooked in traditional clinical data. Contact us today to learn more about NLP-enriched Veradigm Network EHR Data.