ARTIFICIAL INTELLIGENCE AND PERSONALIZED MEDICINE
By Nicholas J. Schork. Cancer treatment and research, 2019
299 citations recorded by the source corpus.
The development of high-throughput, data-intensive biomedical research assays and technologies, such as DNA sequencing, imaging protocols, and wireless health monitoring devices, has created a need for researchers to develop strategies for analyzing, integrating and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate the ‘big data’ produced by these assays, experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition, the application of data-intensive biomedical technologies in research studies has revealed that humans vary widely at the genetic, biochemical, physiological, exposure and behavioral levels, especially with respect to disease processes and treatment responsiveness. This suggests that there is often a need to tailor, or ‘personalize,’ medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for personalizing medicines, AI can play an important role in the development of personalized medicines at all relevant phases of the clinical development and implementation of new personalized health products, from finding appropriate intervention targets to testing them for their utility. We describe many areas where AI can play a role in the development of personalized medicines, and argue that AI’s ability to advance personalized medicine will depend critically on the refinement of relevant assays and ways of storing, aggregating, accessing and ultimately integrating the data they produce. We also point out the limitations of many AI techniques, as well as consider areas for further research.
Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/1cb489f9d228557270c94917f102025832b83701
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Citations in the corpus
- Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, background): “efforts towards solving the complex issues in a number of fields, including engineering [7]–[9], medicine [10]–[13], economy [14], and psychology [15].”
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications (2023, background): “This personalized approach to healthcare enhances treatment efficacy and minimizes the risk of adverse effects, leading to improved patient outcomes and quality of life [1,11,12].”