Personalized Treatment Plans Powered by AI and Genomics - Gopalakrishnan Mahadevan (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
This paper aims at investigating the implementation of AI in genomics specifically in the cancer treatment. Through using the Random Forest and Neural Network models, it would like to find out the prediction of patient outcomes from the genomic and clinical features dataset. One of the acknowledged conclusions of the research is the correlation between the genomic mutations and treatment efficacy, with the affecting genes highlighted to be TP53 and BRCA1. While the result of the models under different configurations show high accuracy, some issues such as data bias and ethical issues including patient privacy concern are still valid in the use of the models. The outcome identifies the need to improve AI models, incorporate different sets of data, and progress the understanding of genomic interpretations for optimizing the clinical applicability of personalized medicine.