Use of Artificial Intelligence in the Screening and Treatment of Chronic Diseases

2020 ◽  
pp. 15-54
Author(s):  
Chaitanya Mamillapalli ◽  
Daniel J. Fox ◽  
Ramanath Bhandari ◽  
Ricardo Correa ◽  
Vishnu Vardhan Garla ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chronic Diseases

scholarly journals Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

10.2196/20701 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20701 ◽  
Author(s):  
Theresa Schachner ◽  
Roman Keller ◽  
Florian v Wangenheim
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Chronic Diseases ◽  
Language Processing ◽  
Systematic Literature Review ◽  
Chronic Conditions ◽  
Experimental Studies ◽  
Future Research ◽  
Conversational Agents ◽  
Quasi Experimental

Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

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.

A survey on artificial intelligence techniques for chronic diseases: open issues and challenges

2021 ◽  
Author(s):  
Keyur Patel ◽  
Chinmay Mistry ◽  
Dev Mehta ◽  
Urvish Thakker ◽  
Sudeep Tanwar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chronic Diseases ◽  
Artificial Intelligence Techniques ◽  
Open Issues

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 Black boxes, not green: Mythologizing artificial intelligence and omitting the environment

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172093514 ◽  
Author(s):  
Benedetta Brevini
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Chronic Diseases ◽  
Public Discourse ◽  
Traffic Accidents ◽  
Data Centers ◽  
Environmental Problems ◽  
Environmental Costs ◽  
Scarce Resources ◽  
Black Boxes

We are repeatedly told that AI will help us to solve some of the world's biggest challenges, from treating chronic diseases and reducing fatality rates in traffic accidents to fighting climate change and anticipating cybersecurity threats. However, the article contends that public discourse on AI systematically avoids considering AI’s environmental costs. Artificial Intelligence- Brevini argues- runs on technology, machines, and infrastructures that deplete scarce resources in their production, consumption, and disposal, thus increasing the amounts of energy in their use, and exacerbate problems of waste and pollution. It also relies on data centers, that demands impressive amounts of energy to compute, analyse, categorize. If we want to stand a chance at tackling the Climate Emergency, then we have to stop avoiding addressing the environmental problems generated by AI.

scholarly journals On How Chronic Conditions Affect the Patient-AI Interaction: A Literature Review

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 313
Author(s):  
Mohammed Tahri Sqalli ◽  
Dena Al-Thani
Keyword(s):  
Artificial Intelligence ◽  
Grounded Theory ◽  
Literature Review ◽  
Chronic Diseases ◽  
Chronic Conditions ◽  
Selection Process ◽  
System Development ◽  
Healthcare Providers ◽  
Health Coaching ◽  
Different Types

Background: Across the globe, managing chronic diseases has been recognized as a challenge for patients and healthcare providers. The state of the art in managing chronic conditions requires not only responding to the clinical needs of the patient, but also guaranteeing a comfortable state of wellbeing for them, despite living with the disease. This demands mutual effort between the patient and the physician in constantly collecting data, monitoring, and understanding the disease. The advent of artificial intelligence has made this process easier. However, studies have rarely attempted to analyze how the different artificial intelligence based health coaching systems are used to manage different types of chronic conditions. Objective: Throughout this grounded theory literature review, we aim to provide an overview for the features that characterize artificial intelligence based health coaching systems used by patients with chronic diseases. Methods: During our search and paper selection process process, we use three bibliographic libraries (PubMed, IEEE Xplore, and ACM Digital Library). Using the grounded theory, we extract overarching themes for the artificial intelligence based health coaching systems. These systems are then classified according to their role, platform, type of interaction with the patient, as well as targeted chronic conditions. Of 869 citations retrieved, 31 unique studies are included in this review. Results: The included studies assess 14 different chronic conditions. Common roles for AI-based health coaching systems are: developing adherence, informing, motivating, reminding, preventing, building a care network, and entertaining. Health coaching systems combine the aforementioned roles to cater to the needs of the patients. The combinations of these roles differ between multilateral, unilateral, opposing bilateral, complementing bilateral, one-role-missing, and the blurred role combinations. Conclusion: Clinical solutions and research related to artificial intelligence based health coaching systems are very limited. Clear guidelines to help develop artificial intelligence-based health coaching systems are still blurred. This grounded theory literature review attempted to shed the light on the research and development requirements for an effective health coaching system intended for patients with chronic conditions. Researchers are recommended to use this review to identify the most suitable role combination for an effective health coaching system development.

scholarly journals Artificial Intelligence for Diabetes Management and Decision Support: Literature Review (Preprint)

2018 ◽  
Author(s):  
Ivan Contreras ◽  
Josep Vehi
Keyword(s):  
Artificial Intelligence ◽  
Chronic Diseases ◽  
Diabetes Management ◽  
Daily Practice ◽  
Self Management ◽  
Review Of The Literature ◽  
Research Activity ◽  
Artificial Intelligence Methods ◽  
Prediction And Prevention

BACKGROUND Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. OBJECTIVE The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. METHODS A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. RESULTS We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. CONCLUSIONS We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

scholarly journals Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Theresa Schachner ◽  
Roman Keller ◽  
Florian von Wangenheim
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Chronic Diseases ◽  
Language Processing ◽  
Systematic Literature Review ◽  
Chronic Conditions ◽  
Experimental Studies ◽  
Future Research ◽  
Conversational Agents ◽  
Quasi Experimental

BACKGROUND A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. OBJECTIVE The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. METHODS We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. RESULTS The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. CONCLUSIONS The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

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