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Systems
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Transforming Risk and Reliability Engineering through Artificial Intelligence and Model-Based...
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Transforming Risk and Reliability Engineering through Artificial Intelligence and Model-Based Paradigms

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 1770

Special Issue Editor

Dr. Bryan M. O'Halloran

E-Mail Website
Guest Editor
Department of Systems Engineering, Naval Postgraduate School, Monterey, CA 93943, USA
Interests: reliability; risk; systems safety; model-based systems engineering

Special Issue Information

Dear Colleagues,

Risk and reliability engineering are undergoing significant transformation with the rise of new technologies, such as model-based systems engineering (MBSE), model-based product design (MBPD), digital engineering, and artificial intelligence (AI). These technologies offer unparalleled benefits to how systems are designed and how they operate, especially in relation to the reliability, safety, and other risks associated with the system. As a result, MDPI’s Systems journal has dedicated a Special Issue to the topic of risk and reliability in systems engineering.

This Special Issue provides a platform for researchers, practitioners, and professionals to share their latest research findings, case studies, and developments in the areas of risk and reliability in systems engineering, including the emerging technologies of MBSE, MBPD, and AI. In addition to traditional topics such as risk analysis, reliability engineering, and systems safety, this Special Issue welcomes topics such as condition-based maintenance (CBM), system resilience, availability modeling, risk-based decision making, uncertainty modeling, probabilistic risk assessment (PRA), and more. By addressing these emerging topics, the Special Issue will help to update readers on the latest developments in the field and gain a deeper understanding of the challenges and opportunities that lie ahead.

The Special Issue welcomes original research papers, review articles, and case studies that address these topics of interest. All manuscripts will undergo a rigorous peer-review process, ensuring that the articles selected for publication are of the highest quality.

Dr. Bryan M. O'Halloran
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • model-based systems engineering (MBSE)
  • digital engineering
  • artificial intelligence (AI)
  • condition-based maintenance (CBM)
  • risk and reliability engineering

Published Papers (1 paper)

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Research

35 pages, 2798 KiB  
Article
Risk Analysis of Artificial Intelligence in Medicine with a Multilayer Concept of System Order
by Negin Moghadasi, Rupa S. Valdez, Misagh Piran, Negar Moghaddasi, Igor Linkov, Thomas L. Polmateer, Davis C. Loose and James H. Lambert
Systems 2024, 12(2), 47; https://doi.org/10.3390/systems12020047 - 1 Feb 2024
Viewed by 1418
Abstract
Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories [...] Read more.
Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories of risk for assessment, management, and communication. For this topic, the framing of conventional risk and decision analyses is ongoing. This paper introduces a method to quantify risk as the disruption of the order of AI initiatives in healthcare systems, aiming to find the scenarios that are most and least disruptive to system order. This novel approach addresses scenarios that bring about a re-ordering of initiatives in each of the following three characteristic layers: purpose, structure, and function. In each layer, the following model elements are identified: 1. Typical research and development initiatives in healthcare. 2. The ordering criteria of the initiatives. 3. Emergent conditions and scenarios that could influence the ordering of the AI initiatives. This approach is a manifold accounting of the scenarios that could contribute to the risk associated with AI in healthcare. Recognizing the context-specific nature of risks and highlighting the role of human in the loop, this study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems. This finding suggests that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of “high-stakes” AI systems, particularly in healthcare. Future work should connect this approach with decision analysis and quantifying the value of information. Future work will explore the disruptions of system order in additional layers of the healthcare system, including the environment, boundary, interconnections, workforce, facilities, supply chains, and others. Full article
(This article belongs to the Special Issue Transforming Risk and Reliability Engineering through Artificial Intelligence and Model-Based Paradigms)
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