scholarly journals The perils of artificial intelligence in healthcare: Disease diagnosis and treatment

2019 ◽  
Vol 9 (1) ◽  
pp. 1-6
Author(s):  
C. Lee Jung
Keyword(s):  
Artificial Intelligence ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment

scholarly journals Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jia Zhou ◽  
Meng Du ◽  
Shuai Chang ◽  
Zhiyi Chen
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Diseases ◽  
Clinical Diagnosis ◽  
Future Development ◽  
Disease Diagnosis ◽  
Beating Heart ◽  
Accurate Diagnosis ◽  
Diagnosis And Treatment ◽  
Functional Evaluation ◽  
Current Application

AbstractUltrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiography poses a challenge for sonographers to diagnose accurately. However, artificial intelligence for detection, functional evaluation, and disease diagnosis has gradually become an alternative for accurate diagnosis and treatment using echocardiography. This work discusses the current application of artificial intelligence in echocardiography technology, its limitations, and future development directions.

scholarly journals Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

2021 ◽  
Vol 12 ◽  
Author(s):  
Shaohui Wang ◽  
Ya Hou ◽  
Xuanhao Li ◽  
Xianli Meng ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Artificial Intelligence ◽  
Cloud Computing ◽  
Deep Learning ◽  
Traditional Medicine ◽  
Western Medicine ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Unknown Etiology ◽  
Drug Mechanism

Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

Síndrome da Anca de Ressalto

2018 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Bernardo Almeida
Keyword(s):  
Surgical Treatment ◽  
Hip Joint ◽  
Scientific Literature ◽  
Therapeutic Targets ◽  
Clinical History ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Snapping Hip ◽  
Predominant Symptom ◽  
Snapping Hip Syndrome

Snapping hip syndrome is a condition in which the predominant symptom is the snapping feelingaround the hip joint caused by a dynamic impingement between muscles or tendons and boneprominences. The etiology of the snapping hip types and consequently the therapeutic targets havebeen subjects of discussion and controversy along the years. A careful clinical history and physicalexamination is frequently enough for this disease diagnosis. Treatment is typically conservative,however when it is not successful surgical treatment is indicated, consisting on the snapping muscleor tendons lengthening. The authors review in this paper the current scientific literature about functionalanatomy, physiopathology, symptoms, diagnosis and treatment of snapping hip.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

scholarly journals Availability and Use of Mobile Health Technology for Disease Diagnosis and Treatment Support by Health Workers in the Ashanti Region of Ghana: A Cross-Sectional Survey

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1233
Author(s):  
Ernest Osei ◽  
Kwasi Agyei ◽  
Boikhutso Tlou ◽  
Tivani P. Mashamba-Thompson
Keyword(s):  
Healthcare Professionals ◽  
Healthcare Delivery ◽  
Disease Diagnosis ◽  
Text Messages ◽  
Diagnosis And Treatment ◽  
Cross Sectional Survey ◽  
Mobile Wireless ◽  
Ashanti Region ◽  
Phone Calls ◽  
Treatment Support

Mobile health (mHealth) technologies have been identified as promising strategies for improving access to healthcare delivery and patient outcomes. However, the extent of availability and use of mHealth among healthcare professionals in Ghana is not known. The study’s main objective was to examine the availability and use of mHealth for disease diagnosis and treatment support by healthcare professionals in the Ashanti Region of Ghana. A cross-sectional survey was carried out among 285 healthcare professionals across 100 primary healthcare clinics in the Ashanti Region with an adopted survey tool. We obtained data on the participants’ background, available health infrastructure, healthcare workforce competency, ownership of a mobile wireless device, usefulness of mHealth, ease of use of mHealth, user satisfaction, and behavioural intention to use mHealth. Descriptive statistics were conducted to characterise healthcare professionals’ demographics and clinical features. Multivariate logistic regression analysis was performed to explore the influence of the demographic factors on the availability and use of mHealth for disease diagnosis and treatment support. STATA version 15 was used to complete all the statistical analyses. Out of the 285 healthcare professionals, 64.91% indicated that mHealth is available to them, while 35.08% have no access to mHealth. Of the 185 healthcare professionals who have access to mHealth, 98.4% are currently using mHealth to support healthcare delivery. Logistic regression model analysis significantly (p < 0.05) identified that factors such as the availability of mobile wireless devices, phone calls, text messages, and mobile apps are associated with HIV, TB, medication adherence, clinic appointments, and others. There is a significant association between the availability of mobile wireless devices, text messages, phone calls, mobile apps, and their use for disease diagnosis and treatment compliance from the chi-square test analysis. The findings demonstrate a low level of mHealth use for disease diagnosis and treatment support by healthcare professionals at rural clinics. We encourage policymakers to promote the implementation of mHealth in rural clinics.

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