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Applications of Machine Learning in Medicine
IJIRIS:: AM Publications, 2023
As medical data and information technologies advance, an increasing number of practitioners are recognizing or planning to use artificial intelligence. Radically alter medical practice through the use of cutting-edge machine learning techniques. Research is now being done to determine how machine learning and predictive analysis might be used to tailor individual therapies. In order to create a medical model that can rapidly and reliably forecast new data, machine learning must first learn a large quantity of medical data and investigate the dependencies in data concentration. This allows for the early detection of diseases and the support of therapeutic decisions. Clinical medicine must continue to identify and treat severely ill emergency patients quickly while dealing with a relative paucity of medical resources. The age of big data has made clinical demands and thoughtful medical treatment generate demand. The solution to the fore mentioned challenges lies in the assistance supplied by machines.
Burgeoning of Machine Learning in the field of Medical & Health Sciences
2021
In recent years, there has been a significant improvement in medical science and its related equipment, especially to diagnose a particular disease in the early stage. Use of technology helps in early diagnoses which leads to early treatment and recovery from disease. If the person does not receive proper treatment in accordance to the diagnosis the disease might get worse which results in increase morbidity and mortality rate. In short, early diagnoses and right treatment is the best remedy against any particular disease. Due to this fact, there is a need to analyze complex medical data, medical reports and medical images that could provide mechanisms to help the health care professional with more precision. In the field of medical science there is a need to devise standardized mechanism to analyze complex medical data. Introduction of machine learning and artificial intelligence provide ease to examine medical reports and images that aids healthcare professionals with greater accu...
Showcasing the Impact of Machine Learning in Healthcare
Deleted Journal, 2020
Machine learning makes the machines learn from provided data and with the help of its algorithms it predicts and analyzes the data. This makes the machines artificially intelligent. These techniques spread its wings in all the areas of healthcare whether it is the diagnosis, treatment etc. Here a brief overview of all the areas where machine learning / artificial intelligence techniques can be applied.
Machine Learning in Medical Applications
Lecture Notes in Computer Science, 2001
Research in Machine Learning methods to-date remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the workshop were to foster fundamental and applied research in the application of machine learning methods to medical problem solving and to medical research, to provide a forum for reporting significant results, to determine whether Machine Learning methods are able to underpin the research and development on intelligent systems for medical applications, and to identify those areas where increased research is likely to yield advances. A number of recommendations for a research agenda were produced, including both technical and human-centered issues.
Diagnostics
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a ...
Machine Learning in Clinical, Academic, and Surgical Medicine
Academia Letters, 2021
Fundamentals of Machine Learning Machine Learning (ML) is the use of computer systems that can perform intelligent predictions on large datasets often consisting of millions of unique data points. Using algorithms and statistical models to analyze and draw inferences from patterns in data. Recent progress in ML has attained what appears to be human level of semantic understanding and information extraction and sometimes the ability to detect abstract patterns with greater accuracy than human experts (Nichols et al., 2018). One of the advantages of ML, is its ability to enable computers to learn without being explicitly programmed allowing it to have many useful applications. Currently there are several different ML algorithms in use today, which are referred to as models. In medicine the desired model outcomes are generally either classification or regression. Classification refers to making a prediction based on qualitative data (e.g., labelling whether an image shows a dog or a cat) and regression is a prediction of a continuous variable such as the height of an individual given a set of known variables (Nichols et al., 2018). Machine learning uses deep learning and neural networks to make their predictions. Neural
The Future of Health care: Machine Learning
International Journal of Engineering & Technology
Machine learning (ML) is a rising field. Machine learning is to find patterns automatically and reason about data.ML enables personalized care called precision medicine. Machine learning methods have made advances in healthcare domain. This paper discuss about application of machine learning in health care. Machine learning will change health care within a few years. In future ML and AI will transform health care, but quality ML and AI decision support systems (DSS) Should Require to address the problems faced by patients and physicians in effective diagnosis.
Review of Inclusion of Machine Learning Techniques in Medicine Field
International Journal of Engineering Applied Sciences and Technology, 2021
The field of medicine is seeing indomitable changes and development with the inclusion of machine learning techniques and artificial intelligence. These inclusive systems are improving the overall efficiency and accuracy in every facet of medicine: Prognosis, diagnosis, and treatment are all positively impacted. The understanding of machine learning, its nuances, and techniques will be transforming the field of medicine in the coming years. This paper aims to learn and review the effect of machine learning and its inclusion in the medical field spanning pathology, radiology, mental health, and dentistry. There is an indepth understanding of how these inclusive systems could address the problems faced by patients and physicians leading to effective diagnosis and treatment. The research methodology is the systematic literature review of articles with the cluster of keywords identified as the central objective.