scholarly journals Ethical considerations about artificial intelligence for prognostication in intensive care

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael Beil ◽  
Ingo Proft ◽  
Daniel van Heerden ◽  
Sigal Sviri ◽  
Peter Vernon van Heerden
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intensive Care ◽  
Prediction Models ◽  
Ethical Challenges ◽  
Ethical Considerations ◽  
Or Groups ◽  
Data Stewardship ◽  
Technical Refinement ◽  
Selection Of

Abstract Background Prognosticating the course of diseases to inform decision-making is a key component of intensive care medicine. For several applications in medicine, new methods from the field of artificial intelligence (AI) and machine learning have already outperformed conventional prediction models. Due to their technical characteristics, these methods will present new ethical challenges to the intensivist. Results In addition to the standards of data stewardship in medicine, the selection of datasets and algorithms to create AI prognostication models must involve extensive scrutiny to avoid biases and, consequently, injustice against individuals or groups of patients. Assessment of these models for compliance with the ethical principles of beneficence and non-maleficence should also include quantification of predictive uncertainty. Respect for patients’ autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models. Moreover, a system of continuous oversight can help to maintain public trust in this technology. Based on these considerations as well as recent guidelines, we propose a pathway to an ethical implementation of AI-based prognostication. It includes a checklist for new AI models that deals with medical and technical topics as well as patient- and system-centered issues. Conclusion AI models for prognostication will become valuable tools in intensive care. However, they require technical refinement and a careful implementation according to the standards of medical ethics.

scholarly journals Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems

2020 ◽  
Vol 2674 (10) ◽  
pp. 448-460
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Decision ◽  
Performance Models ◽  
Remaining Service Life ◽  
Management Decision Making ◽  
Decision Making Processes

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

scholarly journals ASSESSING ETHICAL AI-BASED DECISION-MAKING: TOWARDS AN APPLIED ANALYTICAL FRAMEWORK

2019 ◽  
Vol 2019 ◽  
Author(s):  
Paul Henman
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Analytical Framework ◽  
Government Accountability ◽  
Ethical Challenges ◽  
Government Decision ◽  
Policy Work ◽  
Key Questions

Globally there is strong enthusiasm for using Artificial Intelligence (AI) in government decision making, yet this technocratic approach is not without significant downsides including bias, exacerbating discrimination and inequalities, and reducing government accountability and transparency. A flurry of analytical and policy work has recently sought to identify principles, policies, regulations and institutions for enacting ethical AI. Yet, what is lacking is a practical framework and means by which AI can be assessed as un/ethical. This paper provides an overview of an applied analytical framework for assessing the ethics of AI. It notes that AI (or algorithmic) decision-making is an outcome of data, code, context and use. Using these four categories, the paper articulates key questions necessary to determine the potential ethical challenges of using an AI/algorithm in decision making, and provides the basis for their articulation within a practical toolkit that can be demonstrated against known AI decision-making tools.

scholarly journals How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
Ken Redekop
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Intensive Care ◽  
Best Practice ◽  
Investment Decisions ◽  
Cost Savings ◽  
Economic Evaluations ◽  
Successful Implementation ◽  
Practice Methods

Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. Methods The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. Results When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (− 0.5%, − €886) and to improve patient-ventilator interaction (− 3%, − €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. Conclusions We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

scholarly journals Ethical Considerations in the Advent of Artificial Intelligence in Otolaryngology

2019 ◽  
Vol 162 (1) ◽  
pp. 38-39
Author(s):  
Alexandra M. Arambula ◽  
Andrés M. Bur
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Medical Ethics ◽  
Patient Care ◽  
Care Delivery ◽  
Shared Decision ◽  
Ethical Considerations ◽  
Widespread Implementation

Artificial intelligence (AI) is quickly expanding within the sphere of health care, offering the potential to enhance the efficiency of care delivery, diminish costs, and reduce diagnostic and therapeutic errors. As the field of otolaryngology also explores use of AI technology in patient care, a number of ethical questions warrant attention prior to widespread implementation of AI. This commentary poses many of these ethical questions for consideration by the otolaryngologist specifically, using the 4 pillars of medical ethics—autonomy, beneficence, nonmaleficence, and justice—as a framework and advocating both for the assistive role of AI in health care and for the shared decision-making, empathic approach to patient care.

Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making

2020 ◽  
pp. 084653712094143
Author(s):  
Jaryd R. Christie ◽  
Pencilla Lang ◽  
Lauren M. Zelko ◽  
David A. Palma ◽  
Mohamed Abdelrazek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Biomarker Discovery ◽  
Surgical Techniques ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Cancer Management

Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and assessment of recurrence. Artificial intelligence (AI)–based prediction models are being developed to address these issues and may have a future role in screening, diagnosis, treatment selection, and decision-making around salvage therapy. Imaging plays an essential role in all components of lung cancer management and has the potential to play a key role in AI applications. Artificial intelligence has demonstrated value in prognostic biomarker discovery in lung cancer diagnosis, treatment, and response assessment, putting it at the forefront of the next phase of personalized medicine. However, although exploratory studies demonstrate potential utility, there is a need for rigorous validation and standardization before AI can be utilized in clinical decision-making. In this review, we will provide a summary of the current literature implementing AI for outcome prediction in lung cancer. We will describe the anticipated impact of AI on the management of patients with lung cancer and discuss the challenges of clinical implementation of these techniques.

scholarly journals Ethical Management of Artificial Intelligence

2021 ◽  
Vol 13 (4) ◽  
pp. 1974
Author(s):  
Alfred Benedikt Brendel ◽  
Milad Mirbabaie ◽  
Tim-Benjamin Lembcke ◽  
Lennart Hofeditz
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Future Research ◽  
Ethical Considerations ◽  
Managerial Decision ◽  
Ethical Management ◽  
Ethical Concerns ◽  
Starting Point ◽  
Limited Support ◽  
Ai Ethics

With artificial intelligence (AI) becoming increasingly capable of handling highly complex tasks, many AI-enabled products and services are granted a higher autonomy of decision-making, potentially exercising diverse influences on individuals and societies. While organizations and researchers have repeatedly shown the blessings of AI for humanity, serious AI-related abuses and incidents have raised pressing ethical concerns. Consequently, researchers from different disciplines widely acknowledge an ethical discourse on AI. However, managers—eager to spark ethical considerations throughout their organizations—receive limited support on how they may establish and manage AI ethics. Although research is concerned with technological-related ethics in organizations, research on the ethical management of AI is limited. Against this background, the goals of this article are to provide a starting point for research on AI-related ethical concerns and to highlight future research opportunities. We propose an ethical management of AI (EMMA) framework, focusing on three perspectives: managerial decision making, ethical considerations, and macro- as well as micro-environmental dimensions. With the EMMA framework, we provide researchers with a starting point to address the managing the ethical aspects of AI.

scholarly journals Artificial Intelligence implemented to recognize patterns of sustainable areas by evaluating the database of socioenvironmental safety restrictions

2021 ◽  
Vol 10 (10) ◽  
pp. e212101018841
Author(s):  
Julio Leite Azancort Neto ◽  
Arleson Lui Silva Gonçalves ◽  
Brennus Caio Carvalho da Cruz ◽  
Larissa Luz Gomes ◽  
Denis Carlos Lima Costa
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Economic Effects ◽  
Individual Values ◽  
Modeling Process ◽  
New Methodologies ◽  
The Individual ◽  
Optimal Area ◽  
Decision Making Rule ◽  
Selection Of

The several papers recently published, applied to sustainable development, has been considering new methodologies and techniques in identifying the main criteria, in numeric format, that are useful in formulating possible solutions to the solid waste problem. This paper presents the Mathematical and Computational Modeling Process (PM2C), applied in the determination of control variables related to selection of areas destined to the construction of landfills, in order to benefit from new analyzes and values obtained by methods such as AHP (Analytical Hierarchy Process) and GIS (Geographic Information Systems). The main objective of this paper is the use of Artificial Intelligence (AI), through a Decision Tree strategy, as a selective method and optimal solutions in choosing the best area dedicated to the construction of landfills, with the creation and analysis of new values applied to scenarios defined in the paper of Andrade e Barbosa (2015). The results, expressed in analytical and graphical forms, show the individual values for each criterion and new scenarios involved in the phenomena. This paper highlights the importance of incorporating new conditions and criteria to propose a new decision-making rule, simultaneously, associating qualitative and quantitative characteristics, related to social and economic effects, applied to the environment management system. Based on these principles, it was possible to simulate new scenarios that demonstrate, with very high precision, the best values of useful criteria for decision-making in the selection of the optimal area for implementation of a landfill.

scholarly journals Supporting Decision Making in Intensive Care: Ethical Principles for Managing Access to Care During the COVID-19 Pandemic

2021 ◽  
Vol 8 ◽  
Author(s):  
Stefano D'Errico ◽  
Martina Padovano ◽  
Matteo Scopetti ◽  
Federico Manetti ◽  
Martina Zanon ◽  
...  
Keyword(s):  
Decision Making ◽  
Intensive Care ◽  
Access To Care ◽  
Healthcare Professionals ◽  
Ethical Issues ◽  
Health Resources ◽  
Ethical Considerations ◽  
Healthcare Resources ◽  
Health Threat ◽  
Critical Issues

The pandemic from COVID-19 causes a health threat for many countries and requires an internationally coordinated response due to the high spread of the infection. The current local and international situation gives rise to logistical and ethical considerations regarding the imbalance between needs for assistance and availability of health resources in the continuation of the emergency. A shortage condition will require healthcare professionals to choose between patients who will have access to respiratory support and those who will have to continue without. The sharing of criteria for the introduction of patients to the different therapeutic paths is fundamental to prevent the onset of ethical issues. The present paper analyzes the critical issues related to the scarcity of healthcare resources and the limitation of access to intensive care with the aim of proposing ethically sustainable principles for the management of the current pandemic situation.

scholarly journals Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations

2021 ◽  
Vol 27 (4) ◽  
pp. 146045822110523
Author(s):  
Nicholas RJ Möllmann ◽  
Milad Mirbabaie ◽  
Stefan Stieglitz
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Digital Health ◽  
Status Quo ◽  
Ethical Principles ◽  
Ethical Considerations ◽  
Autonomous Decision ◽  
The Status

The application of artificial intelligence (AI) not only yields in advantages for healthcare but raises several ethical questions. Extant research on ethical considerations of AI in digital health is quite sparse and a holistic overview is lacking. A systematic literature review searching across 853 peer-reviewed journals and conferences yielded in 50 relevant articles categorized in five major ethical principles: beneficence, non-maleficence, autonomy, justice, and explicability. The ethical landscape of AI in digital health is portrayed including a snapshot guiding future development. The status quo highlights potential areas with little empirical but required research. Less explored areas with remaining ethical questions are validated and guide scholars’ efforts by outlining an overview of addressed ethical principles and intensity of studies including correlations. Practitioners understand novel questions AI raises eventually leading to properly regulated implementations and further comprehend that society is on its way from supporting technologies to autonomous decision-making systems.

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