Healthcare uses of artificial intelligence: Challenges and opportunities for growth

2019 ◽  
Vol 32 (5) ◽  
pp. 272-275 ◽  
Author(s):  
Eric Racine ◽  
Wren Boehlen ◽  
Matthew Sample
Keyword(s):  
Artificial Intelligence ◽  
Information Management ◽  
Ethical Challenges ◽  
Healthcare Applications ◽  
Dynamic Information ◽  
Lifestyle Management ◽  
Challenges And Opportunities ◽  
Clinical Tools ◽  
Expected Benefits ◽  
Insight Into

Forms of Artificial Intelligence (AI), like deep learning algorithms and neural networks, are being intensely explored for novel healthcare applications in areas such as imaging and diagnoses, risk analysis, lifestyle management and monitoring, health information management, and virtual health assistance. Expected benefits in these areas are wide-ranging and include increased speed in imaging, greater insight into predictive screening, and decreased healthcare costs and inefficiency. However, AI-based clinical tools also create a host of situations wherein commonly-held values and ethical principles may be challenged. In this short column, we highlight three potentially problematic aspects of AI use in healthcare: (1) dynamic information and consent, (2) transparency and ownership, and (3) privacy and discrimination. We discuss their impact on patient/client, clinician, and health institution values and suggest ways to tackle this impact. We propose that AI-related ethical challenges may represent an opportunity for growth in organizations.

Edge Computing for Secured IoT Analytics on the Cloud

2022 ◽  
pp. 162-175
Author(s):  
S. Meenakshi Sundaram ◽  
Tejaswini R. Murgod
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Healthcare Providers ◽  
Edge Computing ◽  
Healthcare Applications ◽  
Online Mode ◽  
Multiple Data ◽  
Multiple Data Streams ◽  
Software Services ◽  
Insight Into

This chapter provides an insight into building healthcare applications that are deployed in the cloud storage using edge computing and IoT data analytics approaches. Data is collected from environments both within or external to the hospital. The devices that are connected enable the healthcare providers to monitor patients at large distances, manage chronic disease, and manage medication dosages. The data from these devices can be added to clinical research to gain an insight into the participant's experiences. Artificial intelligence techniques like machine learning or deep learning can be employed at the edge of the networks for IoT analytics of multiple data streams in online mode. The industrial edge computing is growing rapidly from 7% in 2019 to being expected to reach approximately 16% by 2025. The total market for intelligent industrial edge computing that includes hardware, software, services has reached $11.6B in 2019 and is expected to increase to $30.8B by 2025.

The Power of Matrix Factorization: Methods for Deconvoluting Genetic Heterogeneous Data at Expression Level

2021 ◽  
Vol 15 (8) ◽  
pp. 841-853
Author(s):  
Yuan Liu ◽  
Zhining Wen ◽  
Menglong Li
Keyword(s):  
Artificial Intelligence ◽  
Matrix Factorization ◽  
In Silico ◽  
Genetic Data ◽  
Heterogeneous Data ◽  
Expression Level ◽  
Mathematical Methods ◽  
Challenges And Opportunities ◽  
Factorization Methods ◽  
Genetic Profiles

Background:: The utilization of genetic data to investigate biological problems has recently become a vital approach. However, it is undeniable that the heterogeneity of original samples at the biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample could differentiate the expression profile, and set considerable biases for downstream research. Matrix factorization (MF) which originated as a set of mathematical methods, has contributed massively to deconvoluting genetic profiles in silico, especially at the expression level. Objective: With the development of artificial intelligence algorithms and machine learning, the number of computational methods for solving heterogeneous problems is also rapidly abundant. However, a structural view from the angle of using MF to deconvolute genetic data is quite limited. This study was conducted to review the usages of MF methods on heterogeneous problems of genetic data on expression level. Methods: MF methods involved in deconvolution were reviewed according to their individual strengths. The demonstration is presented separately into three sections: application scenarios, method categories and summarization for tools. Specifically, application scenarios defined deconvoluting problem with applying scenarios. Method categories summarized MF algorithms contributed to different scenarios. Summarization for tools listed functions and developed web-servers over the latest decade. Additionally, challenges and opportunities of relative fields are discussed. Results and Conclusion: Based on the investigation, this study aims to present a relatively global picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further to help researchers in selecting suitable MF methods based on the different scenarios.

AI Narratives

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ancient Greece ◽  
Historical Overview ◽  
Intelligent Machines ◽  
Narrative History ◽  
History Of ◽  
Imaginative Thinking ◽  
The Relationship ◽  
Insight Into

This book is the first to examine the history of imaginative thinking about intelligent machines. As real artificial intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed, and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing prehistory of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first centuries in which a greater density of narratives emerged alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI’s social, ethical, and philosophical consequences, including questions of dehumanization, automation, anthropomorphization, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.

scholarly journals Insight into the HIV-1 Vif SOCS-box–ElonginBC interaction

Open Biology ◽  
2013 ◽  
Vol 3 (11) ◽  
pp. 130100 ◽  
Author(s):  
Zhisheng Lu ◽  
Julien R. C. Bergeron ◽  
R. Andrew Atkinson ◽  
Torsten Schaller ◽  
Dennis A. Veselkov ◽  
...  
Keyword(s):  
Solution Structure ◽  
Hydrophobic Interactions ◽  
Dynamic Information ◽  
Ubiquitin Ligase Complex ◽  
Biophysical Studies ◽  
Viral Infectivity Factor ◽  
Β Sheet ◽  
Socs Box ◽  
Insight Into ◽  
Hiv 1

The HIV-1 viral infectivity factor (Vif) neutralizes cell-encoded antiviral APOBEC3 proteins by recruiting a cellular ElonginB (EloB)/ElonginC (EloC)/Cullin5-containing ubiquitin ligase complex, resulting in APOBEC3 ubiquitination and proteolysis. The suppressors-of-cytokine-signalling-like domain (SOCS-box) of HIV-1 Vif is essential for E3 ligase engagement, and contains a BC box as well as an unusual proline-rich motif. Here, we report the NMR solution structure of the Vif SOCS–ElonginBC (EloBC) complex. In contrast to SOCS-boxes described in other proteins, the HIV-1 Vif SOCS-box contains only one α-helical domain followed by a β-sheet fold. The SOCS-box of Vif binds primarily to EloC by hydrophobic interactions. The functionally essential proline-rich motif mediates a direct but weak interaction with residues 101–104 of EloB, inducing a conformational change from an unstructured state to a structured state. The structure of the complex and biophysical studies provide detailed insight into the function of Vif's proline-rich motif and reveal novel dynamic information on the Vif–EloBC interaction.

scholarly journals Dutch Public health Foresight study 2018/20

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
H Hilderink
Keyword(s):  
Public Health ◽  
Health Care ◽  
Health Care Expenditures ◽  
Living Environment ◽  
Societal Challenges ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
National Prevention ◽  
Business As Usual ◽  
Insight Into

Abstract The four-year Public Health Foresight Study (VTV) provides insight into the most important societal challenges for public health and health care in the Netherlands. The seventh edition of the Dutch Public Health Foresight study was published in 2018, with an update in 2020. In this update a business-as-usual or Trend Scenario was developed using 2018 as a base year. In the trend scenario demographic and epidemiological projections have been used to depict the future trends regarding ageing, health, disease, health behaviors, health expenditures and health inequalities. Next, these trends are used to identify the most important future challenges and opportunities for public health. In the 2020 update, special attentions is given to climate change and the local living environment and their impacts and interaction with public health outcomes. Trends in lifestyle-related lifestyle show both positive (smoking prevalence) and negative (overweight prevalence) future developments. Dementia will be the leading cause of mortality and disease burden in 2040 by far. Health care expenditures will double by 2040, with cancers showing the most rapid growth of all disease groups. The insights of this study are directly used as input for the National Health Policy Memorandum and for the National Prevention Accord.

Artificial Intelligence and Ethics in Dentistry: A Scoping Review

2021 ◽  
pp. 002203452110138
Author(s):  
C.M. Mörch ◽  
S. Atsu ◽  
W. Cai ◽  
X. Li ◽  
S.A. Madathil ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Ethical Issues ◽  
Data Extraction ◽  
Cochrane Library ◽  
Data Sets ◽  
Ethical Challenges ◽  
Internal Validation ◽  
Neural Learning ◽  
Learning Techniques

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.

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