Medical applications of machine learning and AI: A need or an opportunity?

Abstract

Estimates put the amount of new healthcare data at 2,314,000 gigabytes in 2022, according to the global trend towards digitising health care systems. Better reasoning and more effective utilisation of obtained data are continuous goals of intelligent system development. This application is not limited to diagnostic purposes, which are retrospection-based. Prospective interpretation, which provides early prognosis, is another possible extension of this. However, these technologies leave doctors in a difficult position, caught between thorough scientific assessments and clinical case presentations. What they don't have is a solid foundation to go into when it comes to medical machine learning. With any luck, this paper will serve as a helpful resource for doctors who are curious in the applications of AI and ML in healthcare, particularly in the recent past. First, we will go over the broad strokes of history when it comes to healthcare systems' use of AI and ML concepts. Next, we provide a rundown of some of the medical specialties that are making use of or testing these technologies, including haematology, neurology, cardiology, oncology, radiology, ophthalmology, gene therapy, and cell biology.