The integration of generative artificial intelligence and machine learning in healthcare: Current applications and future directions

Abstract

Machine learning (ML) and generative artificial intelligence (GAI) in recent years are rapidly revolutionizing the healthcare industry offering improved precision and efficiency of healthcare delivery. The use of these advanced technologies in healthcare such as medical imaging, drug discoveries, predictive analytics, and personalized medicine can diagnose diseases at the earliest, set smarter treatment plans, and improve patient outcomes. ML and generative AI aid in diagnostic accuracy and progress prediction particularly in fields like radiology and oncology, reducing error by 25% compared to traditional methods. Additionally, generative AI-based chatbots like ChatGPT have their role in healthcare through the immediate availability of medical information and assessing urgency and severity of when to seek medical guidance. Despite these advancements, ethical challenges persist such as data privacy, bias in AI algorithms, and lack of transparency. Therefore, techniques like data anonymization and the addition of controlled noise can be applied to remove identification from datasets. This narrative review highlights the implications of integrating ML and generative AI in healthcare affecting clinical practice, patient outcomes, and the broader healthcare system. ML and GAI enhance diagnostic precision and assist healthcare virtually, thereby allowing earlier diagnosis of disease, facilitating personalized treatment strategies, improving patient outcomes, and lowering costs.

Keywords

diagnostic imaging / generative AI / healthcare / machine learning / virtual health assistants

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