scholarly journals Will Artificial Intelligence Translate Big Data Into Improved Medical Care or Be a Source of Confusing Intrusion? A Discussion Between a (Cautious) Physician Informatician and an (Optimistic) Medical Informatics Researcher

10.2196/16272 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16272 ◽  
Author(s):  
Qing Zeng-Treitler ◽  
Stuart J Nelson
Keyword(s):  
Artificial Intelligence ◽  
New England ◽  
Medical Care ◽  
Data Driven ◽  
Single Individual ◽  
Physician Burnout ◽  
Problem Space ◽  
The Past ◽  
Challenges And Opportunities ◽  
Development And Utilization

Artificial intelligence (AI), the computerized capability of doing tasks, which until recently was thought to be the exclusive domain of human intelligence, has demonstrated great strides in the past decade. The abilities to play games, provide piloting for an automobile, and respond to spoken language are remarkable successes. How are the challenges and opportunities of medicine different from these challenges and how can we best apply these data-driven techniques to patient care and outcomes? A New England Journal of Medicine paper published in 1980 suggested that more well-defined “specialized” tasks of medical care were more amenable to computer assistance, while the breadth of approach required for defining a problem and narrowing down the problem space was less so, and perhaps, unachievable. On the other hand, one can argue that the modern version of AI, which uses data-driven approaches, will be the most useful in tackling tasks such as outcome prediction that are often difficult for clinicians and patients. The ability today to collect large volumes of data about a single individual (eg, through a wearable device) and the accumulation of large datasets about multiple persons receiving medical care has the potential to apply to the care of individuals. As these techniques of analysis, enumeration, aggregation, and presentation are brought to bear in medicine, the question arises as to their utility and applicability in that domain. Early efforts in decision support were found to be helpful; as the systems proliferated, later experiences have shown difficulties such as alert fatigue and physician burnout becoming more prevalent. Will something similar arise from data-driven predictions? Will empowering patients by equipping them with information gained from data analysis help? Patients, providers, technology, and policymakers each have a role to play in the development and utilization of AI in medicine. Some of the challenges, opportunities, and tradeoffs implicit here are presented as a dialog between a clinician (SJN) and an informatician (QZT).

scholarly journals Will Artificial Intelligence Translate Big Data Into Improved Medical Care or Be a Source of Confusing Intrusion? A Discussion Between a (Cautious) Physician Informatician and an (Optimistic) Medical Informatics Researcher (Preprint)

2019 ◽  
Author(s):  
Qing Zeng-Treitler ◽  
Stuart J Nelson
Keyword(s):  
Artificial Intelligence ◽  
New England ◽  
Medical Care ◽  
Data Driven ◽  
Single Individual ◽  
Physician Burnout ◽  
Problem Space ◽  
The Past ◽  
Challenges And Opportunities ◽  
Development And Utilization

UNSTRUCTURED Artificial intelligence (AI), the computerized capability of doing tasks, which until recently was thought to be the exclusive domain of human intelligence, has demonstrated great strides in the past decade. The abilities to play games, provide piloting for an automobile, and respond to spoken language are remarkable successes. How are the challenges and opportunities of medicine different from these challenges and how can we best apply these data-driven techniques to patient care and outcomes? A New England Journal of Medicine paper published in 1980 suggested that more well-defined “specialized” tasks of medical care were more amenable to computer assistance, while the breadth of approach required for defining a problem and narrowing down the problem space was less so, and perhaps, unachievable. On the other hand, one can argue that the modern version of AI, which uses data-driven approaches, will be the most useful in tackling tasks such as outcome prediction that are often difficult for clinicians and patients. The ability today to collect large volumes of data about a single individual (eg, through a wearable device) and the accumulation of large datasets about multiple persons receiving medical care has the potential to apply to the care of individuals. As these techniques of analysis, enumeration, aggregation, and presentation are brought to bear in medicine, the question arises as to their utility and applicability in that domain. Early efforts in decision support were found to be helpful; as the systems proliferated, later experiences have shown difficulties such as alert fatigue and physician burnout becoming more prevalent. Will something similar arise from data-driven predictions? Will empowering patients by equipping them with information gained from data analysis help? Patients, providers, technology, and policymakers each have a role to play in the development and utilization of AI in medicine. Some of the challenges, opportunities, and tradeoffs implicit here are presented as a dialog between a clinician (SJN) and an informatician (QZT).

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities

2021 ◽  
Vol 61 (1) ◽  
pp. 159-179 ◽  
Author(s):  
Saad Khan ◽  
Ruth Hauptman ◽  
Libusha Kelly
Keyword(s):  
Radiation Therapy ◽  
Adverse Events ◽  
Data Driven ◽  
Medical Interventions ◽  
The Past ◽  
Challenges And Opportunities ◽  
Microbiome Research ◽  
Data Driven Approach ◽  
Difficult Challenge ◽  
The Many

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

scholarly journals Modern Business Intelligence: Big Data Analytics and Artificial Intelligence for Creating the Data-Driven Value

2021 ◽  
Author(s):  
Ahmed A.A. Gad-Elrab
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Data Driven ◽  
A Value ◽  
Challenges And Opportunities ◽  
Modern Business

Currently, business intelligence (BI) systems are used extensively in many business areas that are based on making decisions to create a value. BI is the process on available data to extract, analyze and predict business-critical insights. Traditional BI focuses on collecting, extracting, and organizing data for enabling efficient and professional query processing to get insights from historical data. Due to the existing of big data, Internet of Things (IoT), artificial intelligence (AI), and cloud computing (CC), BI became more critical and important process and received more great interest in both industry and academia fields. The main problem is how to use these new technologies for creating data-driven value for modern BI. In this chapter, to meet this problem, the importance of big data analytics, data mining, AI for building and enhancing modern BI will be introduced and discussed. In addition, challenges and opportunities for creating value of data by establishing modern BI processes.

scholarly journals Path to independence: Overview of challenges and opportunities of computational data-driven research in biology

2020 ◽  
Author(s):  
Karishma Chhugani ◽  
Vanessa Jönsson ◽  
SERGHEI MANGUL
Keyword(s):  
Machine Learning ◽  
Secondary Analysis ◽  
High Impact ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Omics Data ◽  
Primary Analysis ◽  
The Past ◽  
Computational Data ◽  
Challenges And Opportunities

During the past decade, the rapid advancement of high-throughput technologies has reshaped modern biomedical research by vastly extending the diversity, richness, and availability of data and methods across various domains. Currently, computational researchers are empowered with data, methods, and tools that allow for the possibility of making important contributions in biomedicine –– through primary analysis of pre-clinical and clinical datasets, the application and development of novel machine learning algorithms towards task automation and diagnostic or treatment predictions, and secondary analysis of existing public omics data. Here we discuss the challenges and pitfalls researchers from dry labs are facing and how they are gaining independence and leading high impact projects.

scholarly journals The History and Practice of AI in the Environmental Sciences

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Environmental Science ◽  
Weather Forecasting ◽  
Optimization Problems ◽  
Data Driven ◽  
Probabilistic Prediction ◽  
Environmental Sciences ◽  
Forecasting Models ◽  
The Past ◽  
Scientists And Engineers

Abstract Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. The use of AI methods began in the 1950s and environmental scientists were adopting them by the 1980s. Although an “AI Winter” temporarily slowed the growth, a more recent resurgence has brought it back with gusto. This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. The environmental sciences possess a host of problems amenable to advancement by intelligent techniques. We review a few of the early applications along with the ML methods of the time and how their progression has impacted these sciences. While AI methods have changed from expert systems in the eighties to neural networks and other data-driven methods, and more recently deep learning, the environmental problems tackled have remained similar. We discuss the types of applications that have shown some of the biggest advances due to AI usage and how they have evolved over the past decades, including topics in weather forecasting, probabilistic prediction, climate estimation, optimization problems, image processing, and improving forecasting models. We finish with a look at where AI as employed in environmental science appears to be headed and some thoughts on how it might be best blended with physical / dynamical modeling approaches to further advance our science.

Google Hangout with Annie Baker, Playwright 1/25/16, 2:33 PM

2016 ◽  
Vol 69 (4) ◽  
pp. 57-64
Author(s):  
Genevieve Yue
Keyword(s):  
New York ◽  
New England ◽  
Selection Process ◽  
Pulitzer Prize ◽  
Film Festival ◽  
The Past

Genevieve Yue interviews playwright Annie Baker, whose Pulitzer Prize–winning play The Flick focuses on the young employees of a single-screen New England movie house. Baker is one of the most critically lauded playwrights to emerge on the New York theater scene in the past ten years, in part due to her uncompromising commitment to experimentation and disruption. Baker intrinsically understands that arriving at something meaningful means taking a new way. Accordingly, Baker did not want to conduct a traditional interview for Film Quarterly. After running into each other at a New York Film Festival screening of Chantal Akerman's No Home Movie (2015)—both overwhelmed by the film—Yue and Baker agreed to begin their conversation by choosing a film neither of them had seen before and watching it together. The selection process itself led to a long discussion, which led to another, and then finally, to the Gmail hangout that forms the basis of the interview.

Brain-eating Amoebae Infection: Challenges and Opportunities in Chemotherapy

2019 ◽  
Vol 19 (12) ◽  
pp. 980-987 ◽  
Author(s):  
Mohammad Ridwane Mungroo ◽  
Ayaz Anwar ◽  
Naveed Ahmed Khan ◽  
Ruqaiyyah Siddiqui
Keyword(s):  
Mortality Rate ◽  
Free Living ◽  
Outdoor Activities ◽  
The Past ◽  
Free Living Amoeba ◽  
Challenges And Opportunities ◽  
Life Threatening ◽  
The Central Nervous System ◽  
Living Nature ◽  
Treatment And Diagnosis

Pathogenic free-living amoeba are known to cause a devastating infection of the central nervous system and are often referred to as “brain-eating amoebae”. The mortality rate of more than 90% and free-living nature of these amoebae is a cause for concern. It is distressing that the mortality rate has remained the same over the past few decades, highlighting the lack of interest by the pharmaceutical industry. With the threat of global warming and increased outdoor activities of public, there is a need for renewed interest in identifying potential anti-amoebic compounds for successful prognosis. Here, we discuss the available chemotherapeutic options and opportunities for potential strategies in the treatment and diagnosis of these life-threatening infections.

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.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

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