Two Singapore public healthcare AI applications for national screening programs and other examples

This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs.The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases,targeting the rapidly increasing number of adults in the country with diabetes.In the second example,the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the months following discharge.Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted,illustrating the multiplicity of factors that shape the requirements for successful large‐scale deployments of AI systems that are deeply embedded within clinical workflows.In the first example,the choice was made to use the system in a semi‐automated(vs.fully automated)mode as this was assessed to be more cost‐effective,though still offering substantial productivity improvement.In the second example,machine learning algorithm design and model execution trade-offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy.The article concludes with several lessons learned related to deploying AI systems within healthcare settings,and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.

A 3D indicator for guiding AI applications in the energy sector

The utilisation of Artificial Intelligence (AI) applications in the energy sector is gaining momentum, withincreasingly intensive search for suitable, high-quality and trustworthy solutions that displayed promisingresults in research. The growing interest comes from decision makers of both the industry and policydomains, searching for applications to increase companies’ profitability, raise efficiency and facilitate theenergy transition. This paper aims to provide a novel three-dimensional (3D) indicator for AI applicationsin the energy sector, based on their respective maturity level, regulatory risks and potential benefits. Casestudies are used to exemplify the application of the 3D indicator, showcasing how the developed frameworkcan be used to filter promising AI applications eligible for governmental funding or business development.In addition, the 3D indicator is used to rank AI applications considering different stakeholder preferences(risk-avoidance, profit-seeking, balanced). These results allow AI applications to be better categorised in theface of rapidly emerging national and intergovernmental AI strategies and regulations that constrain the useof AI applications in critical infrastructures.

Does the Accounting Information Systems(AIS)Influence the Economy?

The purpose of this study is to look at the impact of accounting information systems on the economy.The study has been directed based on the analytical and theoretical.It observed a total of 500 respondents.To run the research and to get informative results,this paper used primary data.It uses the Chi squire test,ANOVA tests,and Multinomial Logistic tests for analyzing the results.It calculates the data with the help of IBM statistical packages for social science(SPSS).This paper assumes that AIS is beneficial for Bangladeshi organizations,which contributes to the economic development of Bangladesh.However,it finally shows that this system has a gap between what accounting information systems are&what should be.This paper suggests that an organization may get potential benefits through the implementation of AIS in Bangladesh.It also will be benefited stakeholders from implying it.The paper conducts based on the listed financial organizations of Bangladesh.This is the main limitation of this study.It is the first work in Bangladesh based on my knowledge.It provides accurate information to all stakeholders that help them to the right decision.It will also help to improve the economic development of Bangladesh.

Toward Human-centered XAIin Practice:A survey

Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI development.Explainable AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human understanding.However,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI practice.We first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of XAI.In addition,a taxonomy of XAI methods is developed for visual computing with analysis of method properties.Holding clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in practice.And we check the availability of open-source XAI tools for stakeholders'use.This survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.

2018 special issue on artificial intelligence 2.0：theories and applications

In July 2017,the Chinese government issued a guideline on developing artificial intelligence（AI）,namely,the‘New-Generation Artificial Intelligence Development Plan’,through 2030 to the public,setting a goal of becoming a global innovation center in this field by 2030.According to the development plan,breakthroughs should be made in basic theories of AI in terms of big data intelligence.

On the potential of using ensemble learning algorithm to approach the partitioning coefficient(k)value in Scheil-Gulliver equation

The Scheil-Gulliver equation is essential for assessing solid fractions during alloy solidification in materials science.Despite the prevalent use of the Calculation of Phase Diagrams(CALPHAD)method,its computational intensity and time are limiting the simulation efficiency.Recently,Artificial Intelligence has emerged as a potent tool in materials science,offering robust and reliable predictive modeling capabilities.This study introduces an ensemble-based method that has the potential to enhance the prediction of the partitioning coefficient(k)in the Scheil equation by inputting various alloy compositions.The findings demonstrate that this approach can predict the temperature and solid fraction at the eutectic temperature with an accuracy exceeding 90%,while the accuracy for k prediction surpasses 70%.Additionally,a case study on a commercial alloy revealed that the model's predictions are within a 5℃deviation from experimental results,and the predicted solid fraction at the eutectic temperature is within a 15%difference of the values obtained from the CALPHAD model.