Abstract
In health care, machine learning (ML) algorithms harness the things of health data offered by the Internet of Things (IoT) to enhance patient outcomes. Such approaches offer both potential applications and some significant challenges. There are many domains in ML, but three domains that are commonly used are Natural Language Processing (NLP) for medical papers, medical imaging, and genetic information. Maximum of these disciplines deal with diagnosis, detection, and prediction. The most common types of applications are treatment recommendations after diagnoses, administrative tasks, patients’ adherence, and involvement. While there are numerous circumstances inside which AI can do healthcare activities equally as good as possible, if not faster than, and quicker than humans, obstacles will prevent the occupation of healthcare providers that become fully automated for a long time. A substantial infrastructure of medical devices now provides data, but the supporting infrastructure is lacking.
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Upadhyay, N., Gulati, A. (2023). Applications of Artificial Intelligence and Machine Learning for Diagnosis, Prediction, and Smart Health Care. In: Yadav, D.K., Gulati, A. (eds) Artificial Intelligence and Machine Learning in Healthcare. Springer, Singapore. https://doi.org/10.1007/978-981-99-6472-7_6
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