Applications of artificial intelligence and machine learning in respiratory medicine

Thorax ◽  
2020 ◽  
Vol 75 (8) ◽  
pp. 695-701 ◽  
Author(s):  
Sherif Gonem ◽  
Wim Janssens ◽  
Nilakash Das ◽  
Marko Topalovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Input Data ◽  
Pulmonary Function Tests ◽  
Machine Learning Techniques ◽  
Respiratory Medicine ◽  
Physiological Data ◽  
Lung Pathology ◽  
Thoracic Imaging ◽  
The Past

The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development of deep neural networks (DNNs)—complex networks residing in silico but loosely modelled on the human brain—that can process complex input data such as a chest radiograph image and output a classification such as ‘normal’ or ‘abnormal’. DNNs are ‘trained’ using large banks of images or other input data that have been assigned the correct labels. DNNs have shown the potential to equal or even surpass the accuracy of human experts in pattern recognition tasks such as interpreting medical images or biosignals. Within respiratory medicine, the main applications of AI and machine learning thus far have been the interpretation of thoracic imaging, lung pathology slides and physiological data such as pulmonary function tests. This article surveys progress in this area over the past 5 years, as well as highlighting the current limitations of AI and machine learning and the potential for future developments.

scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

scholarly journals Use of Artificial Intelligence/Machine Learning in Cancer Research During the COVID-19 Pandemic

2020 ◽  
Vol 5 (S1) ◽  
pp. 251-253
Author(s):  
Vineet Kumar Kamal ◽  
Dolly Kumari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Patients ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
The Past ◽  
Learning Techniques ◽  
Increased Risk ◽  
The Right

The cancer patients are more vulnerable and are at increased risk of COVID-19 and related outcomes due to their weakened immune systems, specially patients with lung cancer. Amid pandemic, the diagnosis, treatment, and care of cancer patients are very difficult and challenging due to several factors. In such situations, the latest technology in artificial intelligence (AI) or machine learning algorithms (ML) have potential to provide better diagnosis, treatments and cares of cancer patients. For example, the researches may use clinical and imaging data with machine learning techniques to make differences between coronavirus-related lung changes and those caused by immunotherapy and radiotherapy. During this pandemic, AI can be used to ensure we are getting the right patients enrolled speedily and more efficiently than the traditional, and complex ways in the past in cancer clinical trials. This is the appropriate time to go beyond the “research as usual” approach and update our research via AI and ML tools to care the cancer patients and discover new and more effective treatments.

scholarly journals Artificial intelligence in reproductive medicine

Reproduction ◽  
2019 ◽  
Vol 158 (4) ◽  
pp. R139-R154 ◽  
Author(s):  
Renjie Wang ◽  
Wei Pan ◽  
Lei Jin ◽  
Yuehan Li ◽  
Yudi Geng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Medicine ◽  
Large Datasets ◽  
Machine Learning Techniques ◽  
Future Directions ◽  
Computing Power ◽  
Implementation Phase ◽  
The Past ◽  
Learning Techniques

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

scholarly journals In defence of machine learning: Debunking the myths of artificial intelligence

2018 ◽  
Vol 14 (4) ◽  
pp. 734-747 ◽  
Author(s):  
Constance de Saint Laurent
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Progress ◽  
Life Time ◽  
Clear Understanding ◽  
Social Scientists ◽  
Scientific Publications ◽  
The Past ◽  
Potential Benefits ◽  
The Media

There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

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