Artificial intelligence for nature— Why knowledge representation and problem solving should play a key role in environmental decision support

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

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Evaluating alternative environmental decision support matrices for future Higg MSI scenarios

The International Journal of Life Cycle Assessment ◽

10.1007/s11367-021-01928-8 ◽

2021 ◽

Author(s):

Valentina Prado ◽

Jesse Daystar ◽

Michele Wallace ◽

Steven Pires ◽

Lise Laurin

Keyword(s):

Decision Support ◽

Environmental Decision

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Sustainable Production in Cement via Artificial Intelligence based Decision Support System: Case Study

Case Studies in Construction Materials ◽

10.1016/j.cscm.2021.e00628 ◽

2021 ◽

pp. e00628

Author(s):

Kübra Tümay Ateş ◽

Cenk Şahin ◽

Yusuf Kuvvetli ◽

Bülent A. Küren ◽

Aykut Uysal

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Sustainable Production

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Assessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networks

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211021040 ◽

2021 ◽

pp. 1748006X2110210

Author(s):

Andreas Brandsæter ◽

Ottar L Osen

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Decision Support ◽

Object Detection ◽

Autonomous Navigation ◽

Adversarial Networks ◽

Maritime Navigation ◽

Ship Navigation ◽

Autonomous Ship ◽

Ship Operation

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.

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