Technology Advancements for Whistleblowing Reporting Platforms and Employees’ Decision to Blow the Whistle

2020 ◽  
Author(s):  
Lei Gao
Keyword(s):  
Artificial Intelligence ◽  
Low Cost ◽  
Virtual Agents ◽  
Experimental Paradigm ◽  
Virtual Agent ◽  
Public Companies ◽  
Online Platform ◽  
Sarbanes Oxley ◽  
Significant Difference ◽  
Section 302

Section 302 of the Sarbanes-Oxley Act requires public companies to maintain platforms for employees to report questionable practices anonymously. Technological advancements have now enabled many firms to incorporate technology into their whistleblowing platforms. An online platform is often promoted as a medium that offers more anonymity than the traditional phone platform. Furthermore, developments in artificial intelligence have enhanced the creation of virtual agents, which can run 24/7/365 at a low cost. Using an experimental paradigm, this study found no significant difference in perceived anonymity between online reporting and phone reporting. The phone platform attracted more reporting intention when a live agent handled reports because witnesses feel more support when talking to a live agent over the phone. However, the witnesses were more likely to report to an online platform when a virtual agent handled the reports because witnesses believed that it is more efficient and provides greater control while reporting.

scholarly journals Reflecting on Public Behavior With Artificial Intelligence-assisted Detection of Face Mask Wearing During the COVID-19 Pandemic

2021 ◽  
Author(s):  
Kasem Seresirikachorn ◽  
Paisan Ruamviboonsuk ◽  
Ngamphol Soonthornworasiri ◽  
Panisa Singhanetr ◽  
Natsuda Kaothanthong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Low Cost ◽  
Virus Transmission ◽  
Face Mask ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Internal Validation ◽  
Factors Affecting ◽  
Protected Group ◽  
Significant Difference

Abstract Background: COVID-19 has created health and socioeconomic damage worldwide, and face masks are a low-cost but effective method of preventing transmission of this disease. Artificial intelligence (AI)-assisted systems can come into play to help visualize the public’s awareness of mask wearing and gain a better picture of whether there is adequate practice of protection during the outbreak. We reported the rate of face mask wearing by the general public using the artificial intelligence-assisted face mask detector, AiMASK.Methods: This cross-sectional study was conducted between January 23 and April 22, 2021 in over 32 districts in Bangkok, Thailand. After the introduction of AiMASK, development and internal validation were performed, and average accuracy of 97.8% was found. Data were classified into a protected group (correct face mask wearing) and an unprotected group (incorrect or non-mask wearing). We analyzed the association between factors affecting the unprotected group using univariate logistic regression analysis.Results: No significant difference was found between results from human graders and those of AiMASK using two proportion Z test (p=0.74). AiMASK detected a total of 1,124,524 people, the majority of whom were in the protected group (95.98%). The unprotected group consisted of 2.06% who practised incorrect mask-wearing, and the other 1.96% were those who did not wear masks. A moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r= -0.507, p<0.001). People were 1.15 times more likely to be in the unprotected group during the holidays and in the evening than on working days and in the morning (OR=1.15, 95% CI 1.13-1.17, p<0.001). Districts in the city center were 1.31 times more likely to have higher proportions of unprotected individuals than suburban districts (OR=1.31, 95% CI 1.28-1.34, p<0.001). Conclusions: AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people’s mask-wearing behavior, and half of the unprotected group were those who wore masks incorrectly. Public policies should communicate the importance of wearing masks consistently throughout the day and during holidays as well as providing instructions for effective mask wearing to prevent virus transmission.

scholarly journals Artificial intelligence-based education assists medical students’ interpretation of hip fracture

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi-Tung Cheng ◽  
Chih-Chi Chen ◽  
Chih-Yuan Fu ◽  
Chung-Hsien Chaou ◽  
Yu-Tung Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Education ◽  
Hip Fracture ◽  
Medical Students ◽  
Medical Image ◽  
Learning System ◽  
Significant Difference ◽  
Dynamic Collaboration ◽  
Increase In Accuracy ◽  
Image Learning

Abstract Background With recent transformations in medical education, the integration of technology to improve medical students’ abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees’ medical image learning. Materials We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. Results The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. Conclusion The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

Evaluation and Comparison of Artificial Intelligence Vision Aids: Orcam MyEye 1 and Seeing AI

2021 ◽  
pp. 0145482X2110274
Author(s):  
Christina Granquist ◽  
Susan Y. Sun ◽  
Sandra R. Montezuma ◽  
Tu M. Tran ◽  
Rachel Gage ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Visual Impairments ◽  
Light Level ◽  
Viewing Distance ◽  
Light Perception ◽  
Time To Completion ◽  
Significant Difference ◽  
Text Reading ◽  
Size Contrast

Introduction: We compared the print-to-speech properties and human performance characteristics of two artificial intelligence vision aids, Orcam MyEye 1 (a portable device) and Seeing AI (an iPhone and iPad application). Methods: There were seven participants with visual impairments who had no experience with the two reading aids. Four participants had no light perception. Two individuals with measurable acuity and one with light perception were tested while blindfolded. We also tested performance with text of varying appearance in varying viewing conditions. To evaluate human performance, we asked the participants to use the devices to attempt 12 reading tasks similar to activities of daily living. We assessed the ranges of text attributes for which reading was possible, such as print size, contrast, and light level. We also assessed if individuals could complete tasks with the devices and measured accuracy and completion time. Participants also completed a survey concerning the two aids. Results: Both aids achieved greater than 95% accuracy in text recognition for flat, plain word documents and ranged from 13 to 57% accuracy for formatted text on curved surfaces. Both aids could read print sizes as small as 0.8M (20/40 Snellen equivalent, 40 cm viewing distance). Individuals successfully completed 71% and 55% ( p = .114) of tasks while using Orcam MyEye 1 and Seeing AI, respectively. There was no significant difference in time to completion of tasks ( p = .775). Individuals believed both aids would be helpful for daily activities. Discussion: Orcam MyEye 1 and Seeing AI had similar text-reading capability and usability. Both aids were useful to users with severe visual impairments in performing reading tasks. Implications for Practitioners: Selection of a reading device or aid should be based on individual preferences and prior familiarity with the platform, since we found no clear superiority of one solution over the other.

scholarly journals Quantification and Classification of Diclofenac Sodium Content in Dispersed Commercially Available Tablets by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

2021 ◽  
Vol 14 (5) ◽  
pp. 440
Author(s):  
Eirini Siozou ◽  
Vasilios Sakkas ◽  
Nikolaos Kourkoumelis
Keyword(s):  
Infrared Spectroscopy ◽  
Low Cost ◽  
Diclofenac Sodium ◽  
Reference Method ◽  
Sodium Content ◽  
Attenuated Total Reflection ◽  
Accuracy And Precision ◽  
Significant Difference ◽  
Ft Ir

A new methodology, based on Fourier transform infrared spectroscopy equipped with an attenuated total reflectance accessory (ATR FT-IR), was developed for the determination of diclofenac sodium (DS) in dispersed commercially available tablets using chemometric tools such as partial least squares (PLS) coupled with discriminant analysis (PLS-DA). The results of PLS-DA depicted a perfect classification of the tablets into three different groups based on their DS concentrations, while the developed model with PLS had a sufficiently low root mean square error (RMSE) for the prediction of the samples’ concentration (~5%) and therefore can be practically used for any tablet with an unknown concentration of DS. Comparison with ultraviolet/visible (UV/Vis) spectrophotometry as the reference method revealed no significant difference between the two methods. The proposed methodology exhibited satisfactory results in terms of both accuracy and precision while being rapid, simple and of low cost.

scholarly journals Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046265
Author(s):  
Shotaro Doki ◽  
Shinichiro Sasahara ◽  
Daisuke Hori ◽  
Yuichi Oi ◽  
Tsukasa Takahashi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Psychological Distress ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Significant Difference ◽  
Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

scholarly journals Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries

2021 ◽  
Vol 14 (8) ◽  
pp. 339
Author(s):  
Tatjana Vasiljeva ◽  
Ilmars Kreituss ◽  
Ilze Lulle
Keyword(s):  
Artificial Intelligence ◽  
Primary Data ◽  
Structured Interviews ◽  
The Public ◽  
Qualitative And Quantitative ◽  
Public Survey ◽  
Significant Difference ◽  
Main Factors ◽  
Negative Factors ◽  
Near Future

This paper looks at public and business attitudes towards artificial intelligence, examining the main factors that influence them. The conceptual model is based on the technology–organization–environment (TOE) framework and was tested through analysis of qualitative and quantitative data. Primary data were collected by a public survey with a questionnaire specially developed for the study and by semi-structured interviews with experts in the artificial intelligence field and management representatives from various companies. This study aims to evaluate the current attitudes of the public and employees of various industries towards AI and investigate the factors that affect them. It was discovered that attitude towards AI differs significantly among industries. There is a significant difference in attitude towards AI between employees at organizations with already implemented AI solutions and employees at organizations with no intention to implement them in the near future. The three main factors which have an impact on AI adoption in an organization are top management’s attitude, competition and regulations. After determining the main factors that influence the attitudes of society and companies towards artificial intelligence, recommendations are provided for reducing various negative factors. The authors develop a proposition that justifies the activities needed for successful adoption of innovative technologies.

scholarly journals Can Hematological Parameters Play a Role in the Differential Diagnosis of Adrenal Tumors?

Uro ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 39-44
Author(s):  
Mehmet Gürkan Arıkan ◽  
Göktan Altuğ Öz ◽  
Nur Gülce İşkan ◽  
Necdet Süt ◽  
İlkan Yüksel ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Inflammatory Markers ◽  
Blood Count ◽  
Complete Blood Count ◽  
Low Cost ◽  
Easy Access ◽  
Adrenal Tumors ◽  
Tumor Diameter ◽  
Lymphocyte Ratio ◽  
Significant Difference

There have been few studies reported with conflicting results in the use of neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), redcell-distribution-width (RDW), etc. for predicting prognosis and differential diagnosis of adrenal tumors. The aim of this study is to investigate the role of inflammatory markers through a complete blood count, which is an easy access low-cost method, for the differential diagnosis of adrenocortical adenoma (ACA), adrenocortical carcinoma (ACC), and pheochromocytoma. The data of patients who underwent adrenalectomy between the years of 2010–2020 were retrospectively analyzed. Systemic hematologic inflammatory markers based on a complete blood count such as neutrophil ratio (NR), lymphocyte ratio (LR), NLR, PLR, RDW, mean platelet volume (MPV), and maximum tumor diameter (MTD) were compared between the groups. A statistically significant difference was found between the three groups in terms of PLR, RDW, and MTD. With post-hoc tests, a statistically significant difference was found in PLR and MTD between the ACA and ACC groups. A statistically significant difference was found between the ACA and pheochromocytoma groups in PLR and RDW values. In conclusion, it could be possible to plan a more accurate medical and surgical approach using PLR and RDW, which are easily calculated through an easy access low-cost method such as a complete blood count, together with MTD in the differential diagnosis of ACC, ACA, and pheochromocytoma.

A Transparent and Decentralized Model of Perception and Action for Intelligent Virtual Agents

2014 ◽  
Vol 23 (04) ◽  
pp. 1460020 ◽  
Author(s):  
George Anastassakis ◽  
Themis Panayiotopoulos
Keyword(s):  
Knowledge Representation ◽  
Virtual Environment ◽  
Virtual World ◽  
Perception And Action ◽  
Virtual Agents ◽  
Virtual Agent ◽  
Complex Interactions ◽  
Intelligent Virtual Agents ◽  
Decentralized Model ◽  
Over Time

Intelligent virtual agent behaviour is a crucial element of any virtual environment application as it essentially brings the environment to life, introduces believability and realism and enables complex interactions and evolution over time. However, the development of mechanisms for virtual agent perception and action is neither a trivial nor a straight-forward task. In this paper we present a model of perception and action for intelligent virtual agents that meets specific requirements and can as such be systematically implemented, can seamlessly and transparently integrate with knowledge representation and intelligent reasoning mechanisms, is highly independent of virtual world implementation specifics, and enables virtual agent portability and reuse.

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