Machine Learning in Healthcare: Opportunities and Challenges

Advancements in Machine Learning are revolutionizing healthcare by innovating in diagnostic and healthcare support technologies. The study explores the core ML strategies in healthcare including supervised and unsupervised models as well as their role in predicting diseases and in monitoring real-time. Despite ML's great promise challenges including privacy of data and ethical issues restrict its widespread acceptance. To address these problems, emergent approaches like federated learning and explainable AI are unfolding. To realize the full potential of this technology, ML for health care requires a collaborative effort from the health care professional, AI analytics specialist, and the policy makers. In addressing those challenges this work also assesses the current state of application of ML in healthcare as it looks at the challenges and the way forward to enhance the uptake of healthcare technology such as IoT and Blockchain towards developing a personalized healthcare system.