Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine
By O. Adir, M. Poley, Gal Chen, Sahar Froim, N. Krinsky, J. Shklover, Janna Shainsky‐Roitman, T. Lammers, Avi Schroeder. Advances in Materials, 2019
381 citations recorded by the source corpus.
Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient‐specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.
Subfields: Biomedical Engineering, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/9e2bb62eec93c81aa57a0b0e7f9c8f59a4af9464
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In Molecular Biology
Citations in the corpus
- Artificial intelligence (AI) and big data in cancer and precision oncology (2020, methodology): “By implementing genomic screening interventions, individuals can benefit from effective therapy based on their genetic factors [4].”
- Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine (2024, background): “Also, AI models can predict the most effective ligand combinations by analyzing data on receptor expression patterns and focusing on those ligands with the greatest binding affinity, thereby improving precision and effectiveness [112].”