CONSUMER HEALTH APPLICATIONS, MACHINE LEARNING, AND SYSTEMS NEUROSCIENCE: THE USE OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN CLINICAL MEDICINE AND HEALTHCARE DELIVERY

2018 ◽  
Vol 5 (2) ◽  
pp. 46 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Medicine ◽  
Healthcare Delivery ◽  
Consumer Health ◽  
Systems Neuroscience ◽  
Health Applications

scholarly journals Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic

2021 ◽  
Vol 4 ◽  
Author(s):  
Jay Carriere ◽  
Hareem Shafi ◽  
Katelyn Brehon ◽  
Kiran Pohar Manhas ◽  
Katie Churchill ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Chronic Conditions ◽  
Healthcare Delivery ◽  
Occupational Therapists ◽  
Healthcare Systems ◽  
Research Development ◽  
Policy Makers ◽  
Neurological Conditions

The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.

A brief overview on the contribution of machine learning in systems neuroscience

2021 ◽  
Vol 17 ◽  
Author(s):  
Graziella Orrù ◽  
Ciro Conversano ◽  
Rebecca Ciacchini ◽  
Angelo Gemignani
Keyword(s):  
Machine Learning ◽  
Cognitive Processes ◽  
Cross Validation ◽  
Neural Circuits ◽  
Clinical Medicine ◽  
Relevant Literature ◽  
Causal Analysis ◽  
Clinical Neuroscience ◽  
Systems Neuroscience ◽  
The Impact

Background: The use of Machine Learning (ML) is witnessing an exponential growth in the field of artificial intelligence (AI) and neuroscience, in particular in subdisciplines such as Systems Neuroscience (SN), as a viable alternative to the use of classical statistical techniques. The combination of this interconnection allows a more detailed study of algorithms and neural circuits that emulate core cognitive processes. ML toolbox includes algorithms that are suited to solving problems of classification, regression, clustering and anomaly detection. Objective: The aim of the present opinion was to exemplify the contribution of ML in the field of SN in three different fields: 1) cognitive modelling; 2) neuroimaging; 3) analysis of clinical datasets. Method: We gathered evidence from the relevant literature related to the interaction between neuroscience and AI and the impact of ML in SN. Results : ML is specifically suited to the analysis of large clinical neuroscience datasets. Experimental results in neuroscience are hard to replicate for a number of reasons and ML may contribute to attenuating these replicability issues via the ubiquitous use of cross-validation procedures. While ML modelling is primarily focused on prediction accuracy, one of the drawbacks in ML is the opacity of various algorithms that resist to intuitive understanding. Conclusions: Future avenues of research have already been traced and include increased interpretability of currently opaque ML models functioning and causal analysis. Causal analysis is intended to distinguish between spurious associations and cause-effect relationship and is a primary interest in both clinical medicine and basic neuroscience.

scholarly journals Prediction of Atrial Fibrillation Using Machine Learning: A Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew S. Tseng ◽  
Peter A. Noseworthy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Clinical Medicine ◽  
Cardiac Computed Tomography ◽  
Laboratory Data ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
Cerebrovascular Events ◽  
Future Direction

There has been recent immense interest in the use of machine learning techniques in the prediction and screening of atrial fibrillation, a common rhythm disorder present with significant clinical implications primarily related to the risk of ischemic cerebrovascular events and heart failure. Prior to the advent of the application of artificial intelligence in clinical medicine, previous studies have enumerated multiple clinical risk factors that can predict the development of atrial fibrillation. These clinical parameters include previous diagnoses, laboratory data (e.g., cardiac and inflammatory biomarkers, etc.), imaging data (e.g., cardiac computed tomography, cardiac magnetic resonance imaging, echocardiography, etc.), and electrophysiological data. These data are readily available in the electronic health record and can be automatically queried by artificial intelligence algorithms. With the modern computational capabilities afforded by technological advancements in computing and artificial intelligence, we present the current state of machine learning methodologies in the prediction and screening of atrial fibrillation as well as the implications and future direction of this rapidly evolving field.

scholarly journals Addressing health disparities in the Food and Drug Administration’s artificial intelligence and machine learning regulatory framework

2020 ◽  
Vol 27 (12) ◽  
pp. 2016-2019
Author(s):  
Kadija Ferryman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Disparities ◽  
Medical Devices ◽  
Health Data ◽  
Learning Tools ◽  
Regulatory Oversight ◽  
Health Records ◽  
Marginalized Groups ◽  
Health Applications

Abstract The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparities. However, these tools have also been shown to exacerbate inequities faced by marginalized groups. Focusing on health disparities should be part of good machine learning practice and regulatory oversight of software as medical devices. Using the Food and Drug Administration (FDA)'s proposed framework for regulating machine learning tools in medicine, I show that addressing health disparities during the premarket and postmarket stages of review can help anticipate and mitigate group harms.

scholarly journals Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

2020 ◽  
Vol 29 (157) ◽  
pp. 200181
Author(s):  
Danai Khemasuwan ◽  
Jeffrey S. Sorensen ◽  
Henri G. Colt
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Medical Imaging ◽  
Healthcare Delivery ◽  
Predictive Modelling ◽  
Pulmonary Medicine ◽  
The Novel ◽  
Digital Revolution ◽  
Massive Growth

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

scholarly journals Artificial Intelligence, Machine Learning, and Cardiovascular Disease

2020 ◽  
Vol 14 ◽  
pp. 117954682092740
Author(s):  
Pankaj Mathur ◽  
Shweta Srivastava ◽  
Xiaowei Xu ◽  
Jawahar L Mehta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Prescription Drugs ◽  
Drug Targets ◽  
Data Privacy ◽  
Clinical Medicine ◽  
Treatment Strategies ◽  
Learning Networks ◽  
Cardiovascular Medicine

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

scholarly journals On Computable Expressions of Policies for Digital Health: Use for Privacy Consent

2021 ◽  
Author(s):  
Zoran Milosevic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Informed Consent ◽  
Deontic Logic ◽  
Formal Model ◽  
Digital Health ◽  
Healthcare Delivery ◽  
Multiple Stakeholders ◽  
Organizational Policies ◽  
Artificial Intelligence Systems

This paper proposes a formal model for expressing policies in digital health. The aim is to support computable expressions of legislative, regulative and organizational policies. The model is grounded in the semantics of deontic logic [1] and in modelling concepts for expressing accountability, specified in the new RM-ODP Enterprise Language standard [2]. An example of privacy consent based on the FHIR consent resource [3] is used to explain the use of these modelling concepts. The example involves multiple stakeholders and illustrates the complexity associated with the use of machine learning and artificial intelligence systems as part of healthcare delivery governed by informed consent policies.

scholarly journals Big Data and Artificial Intelligence (AI) are Poised to Transform Infertility Healthcare

2020 ◽  
Author(s):  
Ines de Santiago ◽  
Lukasz Polanski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Assisted Reproduction ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Poor Quality ◽  
Research Progress ◽  
Successful Outcome

Advances in machine learning (ML) and artificial intelligence (AI) are transforming the way we treat patients in ways not even imagined a few years ago. Cancer research is at the forefront of this movement. Infertility, though not a life-threatening condition, affects around 15% of couples trying for a pregnancy. Increasing availability of large datasets from various sources creates an opportunity to introduce ML and AI into infertility prevention and treatment. At present in the field of assisted reproduction, very little is done in order to prevent infertility from arising, with the main focus put on treatment when often advanced maternal age and low ovarian reserve make it very difficult to conceive. A shift from this disease-centric model to a health centric model in infertility is already taking place with more emphasis on the patient as an active participator in the process. Poor quality and incomplete data as well as biological variability remain the main limitations in the widespread and reliable implementation of AI in the field of reproductive medicine. That said, one of the areas where this technology managed to find a foothold is identification of developmentally competent embryos. More work is required however to learn about ways to improve natural conception, the detection and diagnosis of infertility, and improve assisted reproduction treatments (ART) and ultimately, develop clinically useful algorithms able to adjust treatment regimens in order to assure a successful outcome of either fertility preservation or infertility treatment. Progress in genomics, digital technologies and advances in integrative biology has had a tremendousimpact on research and clinical medicine. With the rise of ‘big data’, artificial intelligence, and the advances in molecular profiling, there is an enormous potential to transform not only scientific research progress, but also clinical decision making towards predictive, preventive, and personalized medicine. In the field of reproductive health, there is now an exciting opportunity to leverage these technologies and develop more sophisticated approaches to diagnose and treat infertility disorders. In this review, we present a comprehensive analysis and interpretation of different innovation forces that are driving the emergence of a system approach to the infertility sector. Here we discuss recent influential work and explore the limitations of the use of Machine Learning models in this rapidly developing area.

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.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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