Valdosta State University research indicates that artificial intelligence (AI) in biomedical science is projected to reach a significant turning point around 2033, according to a new study co-authored by VSU Professor of Biology Jonghoon Kang. This quantitative analysis suggests a decade of extraordinary expansion for AI applications across various medical and scientific fields, impacting students and professionals in the Valdosta area and beyond.
The research, conducted by Hana A. Shriner and Kang, and published in the Journal of Multiscale Neuroscience, examined the historical growth of AI-related biomedical publications. These publications are indexed in PubMed, the National Library of Medicine’s main database for biomedical scientific literature. Rather than relying on expert opinions or speculative forecasts about AI's future, the study mathematically analyzed the historical record. Researchers focused on the annual number of AI-related biomedical publications and used these past growth patterns to estimate how the field might develop over the next decade, Kang stated in a release.
The analysis revealed that for decades, AI-related biomedical research remained relatively limited before experiencing a dramatic acceleration in recent years. This rapid growth is expected to continue for several more years, with the pace potentially reaching its peak around 2033. Kang clarified that this prediction does not signify a single, sudden technological event in 2033. Instead, the model suggests that the expansion of AI-related biomedical research could continue to accelerate, reaching its greatest rate of growth around that specific time. Kang noted that while "many predictions about the future of artificial intelligence are understandably based on technological expectations and expert judgment," their approach specifically investigated whether "the historical growth of biomedical AI research itself contains mathematical information about its future trajectory." The analysis, he said, "points to 2033 as a potentially important year."
If this prediction holds true, the next ten years could witness an extensive integration of AI across biomedical science. This includes areas such as medical imaging, drug discovery, disease prediction, neuroscience, immunology, molecular biology, and the analysis of increasingly large biological datasets. The researchers consider their prediction to be testable, not merely speculative. Since PubMed continuously records new biomedical research, the trajectory can be re-examined in future years to confirm if publication growth continues to align with the predicted pattern. Kang emphasized this testability, stating, "We do not have to argue indefinitely about whether it is correct. Time will test it."
Kang highlighted that these findings carry significant implications for higher education, particularly for Valdosta State University. He stated that the research "provides quantitative support for VSU’s current efforts to strengthen data science and related programs." Furthermore, he suggested that AI education should increasingly become a core part of academic curricula to ensure that students are adequately prepared for the rapidly evolving scientific and professional environments they will likely enter. Kang views mathematics and AI as complementary foundations for modern science, with mathematics offering a lasting framework for understanding the natural world, and AI providing dynamic tools to apply that knowledge to increasingly complex problems.
Kang serves as a professor in the Department of Biology at Valdosta State University. His professional background includes authoring more than 90 scientific papers in biology and related disciplines, with a focus on quantitative biology, biological thermodynamics, and the application of mathematical and statistical methods to biological challenges.
