Application of Machine Learning in Biomedical Field

Abstract

In the biomedical industry, machine learning has grown in importance as a technology because it enables academics and healthcare professionals to analyse and interpret massive volumes of complex data in ways that were previously impossible. Medical imaging, drug development, personalised medicine, electronic health records, genomics, and wearable technology are a few examples of machine learning uses in the healthcare industry. The application of machine learning in healthcare is not without its difficulties and restrictions, though, including the requirement for high-quality training data, the complexity of the regulatory environment, and ethical issues with bias and privacy. Advancements in deep learning and neural networks, the emergence of federated learning, integration with other cutting-edge technologies like blockchain and the Internet of Things, and increased cooperation between machine learning and biomedical experts are some of the future directions for machine learning in the biomedical field. To fully utilise machine learning in healthcare, more funding must be allocated to this field of study.