[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126777-en":3,"doc-seo-126777-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126777,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Applications of Deep Learning and Machine Learning in Healthcare Domain - A Literature Review","Artificial intelligence has advanced quickly in algorithms, hardware implementations, and deployment across many fields. This literature review summarizes recent AI applications in biomedicine, including disease diagnostics, assistive living support, biomedical information processing, and biomedical science. It highlights methods such as brain-computer interfaces, arterial spin labeling imaging, biomarkers, and natural language processing that help reduce errors and track disease progression. The review also covers machine learning, deep learning, and AI tools for computer-assisted diagnosis, decision support, expert systems, and healthcare implementations.","Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature  \nReview  \nBeschi I S1, Dr. S. Prakash Kumar2  \n1Research Scholar, PG & Department of Computer Science, Maruthupandiyar College (Affiliated to Bharathidasan University,  \nTiruchirappalli), Vallam, Thanjavur, Tamilnadu, India.  \n2Assistant Professor, PG & Department of Computer Science, Maruthupandiyar College (Affiliated to Bharathidasan University,  \nTiruchirappalli), Vallam, Thanjavur, Tamilnadu, India.  \nAbstract  \nIn recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra-and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry.  \nKeywords: Healthcare, Machine Learning, Deep Learning, Artificial Intelligence, Disease Severity, Survival prediction , Big data.  \n1. INTRODUCTION  \nArtificial intelligence (AI) [1] is classified as computer intelligence as opposed to human or other living species intelligence. AI is also the study of \"intelligent robots,\" or any entity or system that can perceive and recognise its environment and take appropriate action to improve its chances of achieving its goals. AI also applies to cases in which computers can learn and analyse in the same way as humans do, and thereby assist in problem solving. Machine Learning (ML) is another name for this form of intelligence [2] . Manufacturing, transportation, and governance have all been transformed by Machine Learning (ML)/Deep Learning (DL) systems. DL has delivered stateof-the-art output in a variety of domains over the last few years, including computer vision, text analytics, and speech processing, among others. ML/DL algorithms have become inseparable from our daily lives as a result of their widespread use in different domains (e.g., social media) . Healthcare is now being influenced by ML/DL algorithms, an area that has previously been immune to large-scale technological disruptions [3] . Recently, ML/DL techniques have demonstrated excellent results in a variety of tasks, including body organ identification from medical images, classification of interstitial lung diseases, lung nodule detection, medical image reconstruction, and brain tumour segmentation, to name a few [4] . Intelligent software is expected to assist  \nradiologists and doctors in testing patients in the near future, and machine learning can revolutionise medical study and practise. Clinical medicine has emerged as a promising application field for ML/DL models, with human-level success in clinical pathology, radiology, ophthalmology, and dermatology already achieved [5] .  \nThe advancement of concomitantly advancing technologies like cloud/edge computing, mobile networking, and big data technology is also benefiting the potential of ML models for healthcare applications [6] . ML/DL can generate highly accurate predictive outcomes and promote human-centered intelligent solutions when used in conjunction with these technologies [7] . These innovations have the potential torevitalise the healthcare sector, as well as provide other benefits such as enabling remote healthcare systems for rural and low-income areas.  \n2. MACHINE LEARNING IN HEALTHCARE  \nThe main stages of designing an ML-ba","cbCaimfon8LcU0Yd","https://ap.wps.com/l/cbCaimfon8LcU0Yd","pdf",297470,1,10,"English","en",105,"# Introduction\n# Machine Learning in Healthcare\n## Un-Supervised Machine Learning\n## Supervised Machine Learning\n## Semi-Supervised Machine Learning\n## Reinforcement Learning","[{\"question\":\"What does the paper focus on regarding AI in biomedicine?\",\"answer\":\"It summarizes recent AI applications in biomedicine, including disease diagnostics, biomedical information processing, and biomedical science, while emphasizing methods that reduce errors and monitor disease progression.\"},{\"question\":\"How does the review describe machine learning usage in healthcare systems?\",\"answer\":\"It discusses how machine learning and deep learning support clinical medicine through tasks such as diagnosis assistance, decision support systems, expert systems, and software implementations for doctors.\"},{\"question\":\"Which learning paradigms are covered for healthcare machine learning?\",\"answer\":\"The paper covers unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning, explaining how each approach uses labeled and unlabeled data.\"}]","Applications of Deep Learning and Machine Learning in Healthcare Domain - 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