Personalized medicine refers to using individual genetic or other biomarker information to guide decisions made in regard to the prevention, diagnosis and treatment of disease. The application of biomedical technologies in research studies has shown humans vary widely at the genetic, biochemical, physiological, exposure and behavioral levels, especially with respect to disease processes and treatment responsiveness. Thus, personalized medicine seeks to guide decisions about whom should receive certain therapies, what the ideal specific doses of a given therapy are and whom should be monitored more carefully because they’re predisposed to a particular safety issue. In this way, the medicine is tailored and personalized to an individual’s unique features.

Personalized medicine is generally comprised of two elements. One is the drug, biologic or other therapeutic intervention and second is the diagnostic test. The Center for Drug Evaluation and Research (CDER) has been very involved in developing infrastructure programs and doing a review on their capacity to be on the cutting edge of personalized medicine initiatives. This includes streamlining regulatory oversight around databases, bioinformatics tools and standards. Most recently, CDER has been working with multiple United States Food and Drug Administration (FDA) centers to develop informative guidance and policies on personalized medicine in a timely way. In the past, developing multi-center guidance and policies on personalized medicine involving multiple FDA centers, including the Center for Devices and Radiological Health (CDRH) and the Center for Biologics Evaluation and Research, was challenging to coordinate due to the physical separation between these entities.

The development of data-intensive biomedical research assays and technologies, such as DNA sequencing, imaging protocols and wireless health monitoring devices, has generated a massive amount of data. This massive amount of data will need to be analyzed using statistical methods and visualized using design interfaces. The use of artificial intelligence (AI) techniques on this data is bound to make the most impact.

AI can play a significant role in the development of personalized medicine in all clinical phases of development and help with implementation of new personalized health products by finding appropriate intervention targets and testing their effectiveness. For example, precision medicine methods identify phenotypes of patients with less-common responses to treatment or unique healthcare needs. Thus, AI can be extremely useful for vetting new drugs for treating a disease like cancer. AI’s ability to impact personalized medicine will continue to advance as assays and technologies to gather data, and data storing, aggregating, accessing and integration methods are improved.

AI can be leveraged for sophisticated computation and inference to generate insights, enable the system to reason and learn, and empower clinician decision making through augmented intelligence. Recent studies suggest that research exploring AI’s role in personalized medicine will help solve difficult challenges facing precision medicine. So far, the dominant use of AI in personalized medicine is on treating patients with disease by identifying the disease, determining the best treatment intervention and testing if the intervention works as desired. Thus, most AI based tools have advanced personalized medicine by focusing on the diagnosis and treatment of disease.

AI’s role in personalized medicine of disease prevention is expected to increase. AI can be leveraged to identify individuals with a genetic predisposition to disease and help halt diseases early and before elaborate treatments are needed. AI’s most impactful role in personalized medicine will be combining nongenomic and genomic determinants and information from patient symptoms, clinical history and lifestyles to facilitate personalized diagnosis and help predict future health events.

If you’re ready to explore the potential of AI to solve your organization’s needs, Ellumen’s AI experts are here to help. Stayed tuned for forthcoming articles in Ellumen’s blog series exploring topics related to AI innovation, and listen to the AI Innovation Space podcast on Spotify.

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Iyanuoluwa Odebode, Ph.D. is Ellumen’s artificial intelligence/machine learning expert, supporting the Innovation Lab project by identifying and researching new & emerging technology. He also serves as an adjunct professor in cybersecurity at University of Maryland Baltimore County. Prior to his work at Ellumen, Iyanuoluwa built an algorithm for repurposing old drugs for new use using the DReiM methodology. He has been published in ResearchGate and IEEE for his research on machine learning. Iyanuoluwa completed his master’s in bioinformatics at Morgan State University and his Ph.D. in information systems (machine learning/AI) at University of Maryland Baltimore County.

Todd R. McCollough is a Software Engineer for Ellumen. He works primarily with the CVIX/VIX Services which support image viewers and applications to provide the ability for users to query, retrieve and manipulate VA and DoD medical images and image artifacts. Todd is a co-inventor on several issued U.S. patents and is passionate about discovering novel ways to image patients and improve patient care. Todd received his bachelor’s and master’s degrees in biomedical engineering from Northwestern University.