Integrating artificial intelligence with precision medicine: a synergistic approach

The treatment of brain cancer using personalized medicine relies on gathering large amounts of patient-specific data from various sources. These include imaging data, various omics data, patient records, and data concerning the patient’s lifestyle and living conditions. This large amount of diverse data needs to be integrated and analyzed in order for it to be used to personalize patient’s treatment. This analysis consists of detecting patterns within the data. All these tasks are time-consuming, prone to bias, and subtle changes may be missed by a human observer. Artificial intelligence (AI) may offer an alternative solution to these problems as machine and deep learning algorithms can learn to recognize patterns, interpret what these patterns mean and make predictions about the brain cancer progression, the patient’s survival, and design personalized treatment plans. AI integration into personalized medicine also can be used to improve drug design by identifying lead compounds and running docking and binding models between the drug and its target. This can lower the cost of the process of drug design and result in more rapid drug development. AI has other applications in personalized medicine, including remote medicine and mobile health. This chapter reviews the ways in which AI is integrated into the process of providing brain cancer patients with personalized treatment options.