Artificial intelligence in health care: A game changer

2019 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Manigreeva Krishnatreya
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Game Changer

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Telemedicine (or Teleconsultation?) Practice Guidelines of India: A Game changer or a half-hearted attempt amid COVID-19? (Preprint)

2020 ◽  
Author(s):  
Venkatesh U ◽  
Aravind Gandhi P
Keyword(s):  
Health Care ◽  
Practice Guidelines ◽  
Health Care Workers ◽  
Data Security ◽  
Patient Relationship ◽  
Medical Practitioners ◽  
Care Workers ◽  
The Government ◽  
First Time ◽  
Game Changer

UNSTRUCTURED Telemedicine is where health care intersects with Information Technology. In India, there has been no statutory regulations or official guidelines, specific for Telemedicine practice and allied matters, so far. For the first time, Government of India has released Telemedicine Practice Guidelines for Registered Medical Practitioners on March 25, 2020, amid the COVID-19 outbreak. Through this paper, we would like to initiate the discussion on the features of the guidelines, limitations, and its significance in times of COVID-19 pandemic. The guidelines are with a restricted scope for providing medical consultation to patients, excluding other aspects of Telemedicine such as research and evaluation, and the continuing education of health-care workers. The guidelines have elaborated on the eligibility for practicing Telemedicine in India, the modes and types of Teleconsultation, delved into doctor-patient relationship, consent, & management protocols, touched upon the data security & privacy aspects of the Teleconsultation. After releasing the guidelines, Telescreening of public for COVID-19 symptoms is being advocated by the Government of India. COVID-19 National Teleconsultation Centre (CoNTeC) has been initiated, which connects the doctors across the India to AIIMS in real-time for accessing expert guidance on treatment of the COVID-19 patients.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

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