Driving Change in Healthcare with AI: The Role of Data Analytics and Informatics in Genomic Medicine - Neha Dhaliwal (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This study looks into how data analytics, informatics, and AI have changed genomic medicine, with a focus on personalized treatments, predicting disease risk, and targeted therapies. Some of the most important results are patient stratification, disease risk prediction, and speeding up the development of drugs. It is emphasized that there are problems with data protection, ethics, and rules, and that strong leadership and teamwork are needed. For game-changing innovations in healthcare in the future, the focus will be on AI models that can be understood, data standards, and ethical behaviour. On comparing ML algorithms for the purpose, it was seen that Convolutional Neural Networks (CNNs) proved to be the most precise for analyzing image-based genomic data, despite their substantial computational demands. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks achieved high accuracy with sequential data but required considerable computational resources.