Use of Artificial Intelligence in Regulatory Decision-Making

2021 ◽  
Vol 12 (3) ◽  
pp. 11-19
Author(s):  
Robert Jago ◽  
Anna van der Gaag ◽  
Kostas Stathis ◽  
Ivan Petej ◽  
Piyawat Lertvittayakumjorn ◽  
...  
2020 ◽  
Author(s):  
Avishek Choudhury

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.


2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.


Author(s):  
Bronwyn Ashton ◽  
Cassandra Star ◽  
Mark Lawrence ◽  
John Coveney

Summary This research aimed to understand how the policy was represented as a ‘problem’ in food regulatory decision-making in Australia, and the implications for public health nutrition engagement with policy development processes. Bacchi’s ‘what’s the problem represented to be?’ discourse analysis method was applied to a case study of voluntary food fortification policy (VFP) developed by the then Australia and New Zealand Food Regulation Ministerial Council (ANZFRMC) between 2002 and 2012. As a consultative process is a legislated aspect of food regulatory policy development in Australia, written stakeholder submissions contributed most of the key documents ascertained as relevant to the case. Four major categories of stakeholder were identified in the data; citizen, public health, government and industry. Predictably, citizen, government and public health stakeholders primarily represented voluntary food fortification (VF) as a problem of public health, while industry stakeholders represented it as a problem of commercial benefit. This reflected expected differences regarding decision-making control and power over regulatory activity. However, at both the outset and conclusion of the policy process, the ANZFRMC represented the problem of VF as commercial benefit, suggesting that in this case, a period of ‘formal’ stakeholder consultation did not alter the outcome. This research indicates that in VFP, the policy debate was fought and won at the initial framing of the problem in the earliest stages of the policy process. Consequently, if public health nutritionists leave their participation in the process until formal consultation stages, the opportunity to influence policy may already be lost.


Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.


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