scholarly journals Adoption of machine learning for medical diagnosis

2021 ◽  
Author(s):  
Mohammed Yousef Shaheen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Chronic Diseases ◽  
Medical Diagnosis ◽  
Healthcare Industry ◽  
Early Adopter ◽  
Medical Treatments ◽  
Health Related ◽  
Detection And Diagnosis ◽  
Diagnosis Of Diseases

The healthcare industry has historically been an early adopter of technology advancements and has reaped significant benefits. Machine learning (an artificial intelligence subset) is being used in a variety of health-related fields, including the invention of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. One of the most important uses of machine learning in healthcare is the detection and diagnosis of diseases and conditions that are otherwise difficult to identify. This can range from tumors that are difficult to detect in their early stages to other hereditary illnesses. This research identifies and discusses the various usages of machine learning in medical diagnosis.

scholarly journals Adoption of machine learning for medical diagnosis

2021 ◽  
Author(s):  
Mohammed Yousef Shaheen
Keyword(s):  
Machine Learning ◽  
Chronic Diseases ◽  
Medical Diagnosis ◽  
Patient Data ◽  
Healthcare Industry ◽  
Early Adopter ◽  
Medical Treatments ◽  
Health Related ◽  
Detection And Diagnosis ◽  
Diagnosis Of Diseases

The healthcare industry has historically been an early adopter of technologyadvancements and has reaped significant benefits. Machine learning (an artificialintelligence subset) is being used in a variety of health-related fields, including theinvention of new medical treatments, the management of patient data and records, andthe treatment of chronic diseases. One of the most important uses of machine learningin healthcare is the detection and diagnosis of diseases and conditions that areotherwise difficult to identify. This can range from tumors that are difficult to detect intheir early stages to other hereditary illnesses. This research identifies and discussesthe various usages of machine learning in medical diagnosis.

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

Application of Machine Learning to Analyse Biomedical Signals for Medical Diagnosis

2021 ◽  
pp. 205-236
Author(s):  
Upendra Kumar ◽  
Shashank Yadav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Analysis ◽  
Medical Diagnosis ◽  
Medical Information ◽  
Heart Diseases ◽  
Information Analysis ◽  
Appropriate Treatment ◽  
New Knowledge ◽  
Interest In Research

Interest in research involving health-medical information analysis based on artificial intelligence has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis techniques to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). This study presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient.

scholarly journals Machine learning in dental, oral and craniofacial imaging: a review of recent progress

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11451
Author(s):  
Ruiyang Ren ◽  
Haozhe Luo ◽  
Chongying Su ◽  
Yang Yao ◽  
Wen Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Medical Imaging ◽  
Medical Diagnosis ◽  
Recent Progress ◽  
Medical Science ◽  
Research Progress ◽  
Daily Lives ◽  
Major Application ◽  
Machine Learning Models

Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

scholarly journals Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Albert K. Feeny ◽  
Mina K. Chung ◽  
Anant Madabhushi ◽  
Zachi I. Attia ◽  
Maja Cikes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Patient Outcomes ◽  
Cardiac Electrophysiology ◽  
Recent Literature ◽  
Human Capabilities ◽  
Key Points ◽  
Detection And Diagnosis

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

Detection of Chronic Disease in Primary Care Using Artificial Intelligence Techniques

2020 ◽  
pp. 195-219
Author(s):  
Hakan Gulmez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Primary Care ◽  
Chronic Disease ◽  
Early Detection ◽  
Early Diagnosis ◽  
Chronic Diseases ◽  
Global Burden ◽  
Access Data ◽  
Intelligence Technique

Chronic diseases are the leading causes of death and disability worldwide. By 2020, it is expected to increase to 73% of all deaths and 60% of global burden of disease associated with chronic diseases. For all these reasons, early diagnosis and treatment of chronic diseases is very important. Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning is the development of computer programs that can access data and use it to learn for themselves. The learning process starts by searching for patterns in the examples, experiences, or observations. It will make faster and better decisions in the future based on all these. The primary purpose in machine learning is to allow computers to learn automatically without human help and affect. Considering all the reasons above, this chapter finds the most appropriate artificial intelligence technique for the early detection of chronic diseases.

scholarly journals Reimagining the Future of Healthcare Industry through Internet of Medical Things (IoMT), Artificial Intelligence (AI), Machine Learning (ML), Big Data, Mobile Apps and Advanced Sensors

2019 ◽  
Vol 9 (1) ◽  
pp. 3014-3019
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Science Fiction ◽  
Medical Devices ◽  
Mobile Apps ◽  
The Internet ◽  
Healthcare Industry ◽  
Remote Locations ◽  
Internet Of Medical Things

From Bluetooth enabled hearing aids to robotic caretakers, wearable and smart devices industries are immensely contributing to the development of the healthcare industry with the help of Internet of Things (IoT). Latest technologies like Artificial Intelligence, 3D Printing, Big data, Machine Learning, Advanced Sensors, Mobile Applications and other technologies will continue to generate lot of opportunities for Medtech organizations. Some of the latest healthcare innovations practiced at present might have been seen or read by some of us only in science fiction movies or science fiction stories a long ago. Presently, IoT and Artificial Intelligence is creating a revolution in healthcare industry when it comes to diagnosis and treatment of varied diseases. From smartphones to robots, artificial intelligence is already making its presence felt in healthcare industry and as such it is progressively recognizing the transformative nature of IoT technologies which drives innovation in the development of connected medical devices. Gradual increase in the number of connected medical devices with the advent of technology advancements helps to capture and transmit medical related data wherever and whenever required to the concerned people and thus, it gave birth to the Internet of Medical Things (IoMT), where the Internet of Things (IoT) and healthcare meet. The IoMT helps to constantly monitor and alter (if required) the behvaiour of the patient and his/her health status in real time and also supports healthcare organizations to effectively streamline clinical processes, patient information and related work flows to enhance its operational productivity. The IoMT has made and continues to make the delivery of P4 Medicine (Predictive, Preventive, Personalized and Participatory) even for remote locations with the help of connected sensors and devices helping in real-time patient care. IoMT helps doctors and caregivers to provide patient care and support by constantly monitoring data related to patients through mobile apps and connected medical devices even when patient(s) or doctor(s) are located at remote locations. This research paper discusses about six use cases explaining how IoMT is applied in healthcare industry.

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