Jasmine J
@kce.ac.in
Associate Professor
karpagam college of engineering
@kce.ac.in
Associate Professor
karpagam college of engineering
The healthcare sector has long been an early adopter of technological advances. Nowadays, ML (Machine Learning) a subset of AI plays a key role in many health innovations, including the development of new medical procedures. Machine Learning come up with techniques and tools that can help in solving diagnostic and prognostic problems in medical domains. ML is being applied for the analysis of clinical parameters and their combinations for prognosis. Medical diagnostic reasoning is a very important application area of intelligent systems. Machine learning is applied in a broad range of healthcare applications. On large volumes of data, Machine Learning helps healthcare providers to produce medical solutions. In future Machine Learning algorithms are expected to play a critical role in central nervous system clinical trials. The three main areas machine learning is adapted to include medical imaging, natural language processing of medicaldocuments, and genetic information.
Quality healthcare services backed up with the latest technology is the need for today. Focusing on quality health care services means ensuring patient health management at a superior level at all times. The misuse or lack of available data is preventing healthcare organizations from delivering appropriate patient care and high-quality services for better health.Upto 40% of healthcare provider data records are filled up with errors or misleading information. Many healthcare facilities today are still dependent on outdated systems for keeping patient records. This can make it difficult for the doctor to diagnose which is time-consuming for the doctor and tedious for the patients too. The healthcare system today not only needs an advance system rather it also needs a system that is smooth, transparent, economically efficient and easily operable.
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