scholarly journals Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine (Preprint)

2020 ◽  
Author(s):  
Matthew Michelson ◽  
Tiffany Chow ◽  
Neil A Martin ◽  
Mike Ross ◽  
Amelia Tee Qiao Ying ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Side Effect ◽  
Processing Time ◽  
Meta Analysis ◽  
Ocular Toxicity ◽  
Individual Study ◽  
Novel Approach ◽  
Time Period ◽  
Clinical Queries

BACKGROUND Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. OBJECTIVE We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. METHODS The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. RESULTS By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4% (95% CI 1.11%-9.96%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. CONCLUSIONS We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis.

scholarly journals Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine

10.2196/20007 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e20007
Author(s):  
Matthew Michelson ◽  
Tiffany Chow ◽  
Neil A Martin ◽  
Mike Ross ◽  
Amelia Tee Qiao Ying ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Side Effect ◽  
Processing Time ◽  
Meta Analysis ◽  
Ocular Toxicity ◽  
Individual Study ◽  
Novel Approach ◽  
Time Period ◽  
Clinical Queries

Background Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. Objective We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. Methods The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. Results By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4% (95% CI 1.11%-9.96%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. Conclusions We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis.

scholarly journals Ocular toxicity and Hydroxychloroquine: A Rapid Meta-Analysis

2020 ◽  
Author(s):  
Matthew Michelson ◽  
Tiffany Chow ◽  
Neil Martin ◽  
Mike Ross ◽  
Amelia Tee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Quality ◽  
Side Effect ◽  
Meta Analysis ◽  
Clinical Question ◽  
Ocular Toxicity ◽  
Individual Study ◽  
Rapid Access ◽  
The Individual ◽  
Clinical Queries

AbstractRapid access to evidence is crucial in times of evolving clinical crisis. To that end, we propose a novel mechanism to answer clinical queries: Rapid Meta-Analysis (RMA). Unlike traditional meta-analysis, RMA balances quick time-to-production with reasonable data quality assurances, leveraging Artificial Intelligence to strike this balance. This article presents an example RMA to a currently relevant clinical question: Is ocular toxicity and vision compromise a side effect with hydroxychloroquine therapy?As of this writing, hydroxychloroquine is a leading candidate in the treatment of COVID-19. By combining AI with human analysis, our RMA identified 11 studies looking at ocular toxicity as a side effect and estimated the incidence to be 3.4% (95% CI: 1.11-9.96%). The heterogeneity across the individual study findings was high, and interpretation of the result should take this into account. Importantly, this RMA, from search to screen to analysis, took less than 30 minutes to produce.

A Novel Approach to the Automatic Designation of Predefined Census Enumeration Areas and Population Sampling Frames: A Case Study in Somalia

2019 ◽  
Author(s):  
Sarchil Qader ◽  
Veronique Lefebvre ◽  
Amy Ninneman ◽  
Kristen Himelein ◽  
Utz Pape ◽  
...  
Keyword(s):  
Population Sampling ◽  
Census Enumeration ◽  
Novel Approach

Perancangan dan Pembuatan Alat Bantu Meja Putar untuk Mesin Cnc 4 Axis

2016 ◽  
Vol 10 (2) ◽  
pp. 162-171
Author(s):  
Hafid Hafid ◽  
Tatang Sutisna
Keyword(s):  
Processing Time ◽  
Rotary Table ◽  
Engineering Process ◽  
Cnc Machine ◽  
Economical Analysis ◽  
Machining Center ◽  
Design And Manufacturing ◽  
The Cost ◽  
Productivity And Competitiveness

The design and manufacturing of the rotary table with the specification Ø 170 mm (6 inches) for CNC machine 4 axis has been done. The objective of manufacturing a rotary table is to increase the efficiency of CNC machine Hardford 4 axis to be above 80% in line machining center CV. IM’s workshop. The engineering methods was taken, consist of: working preparation, manufacturing of working drawing, engineering process, the manufacturing and testing. The prototype has been tested and operated, the resulting of increasing productivity of which were as follows: the process of assembling was increased to be 3 time ( before 1 time) and processing time for a specific case reduced from 5 hours to 3 hours, number of operators for the case of assembling the rotary reduced to 1 person (before 4 persons), safety and security become to be better. The results show increased efficiency of CNC machine Hardford, from under 50% to be above 80%. Based on the economical analysis obtained by the cost of good sold (C.G.S) of the rotary table is IDR 34.060.000. The results presented in this paper is expected to be case study for developing a business of the metal and engineering SMEs domestic to the effort of improving efficiency, quality, productivity and competitiveness in global market.ABSTRAKPerancangan dan pembuatan alat bantu meja putar (rotary table) dengan spesifikasi teknis Ø 170 mm (6 inci) untuk mesin CNC 4 axis telah dilakukan. Tujuan pembuatan rotary table adalah untuk meningkatkan efisiensi mesin CNC Hardford 4 axis di atas 80% pada line machining center Bengkel CV. IM. Metode rancang bangun yang dilakukan, meliputi: persiapan kerja, pembuatan gambar kerja, proses engineering, pembuatan dan uji coba. Prototip tersebut telah diuji coba dan dioperasikan dengan hasil peningkatan produktivitas sebagai berikut: proses pengerjaan bongkar pasang meningkat menjadi 3 kali (sebelumnya 1 kali) dan waktu pengerjaan untuk kasus tertentu berkurang dari 5 jam menjadi 3 jam, jumlah operator untuk kasus bongkar pasang rotary berkurang menjadi 1 orang (sebelumnya 4 orang), keselamatan kerja dan keamanan menjadi lebih baik. Hasil peningkatan berupa efisiensi mesin CNC Hardford 4 axis dari sebelumnya di bawah 50% menjadi di atas 80%. Berdasarkan hasil perhitungan analisis ekonomi diperoleh harga pokok produksi (HPP) alat bantu meja putar adalah sebesar Rp. 34.060.000. Bahasan ini diharapkan menjadi contoh kasus bagi pengembangan usaha IKM logam dan mesin dalam negeri untuk meningkatkan efisiensi, mutu, produktivitas dan keunggulan daya saing di pasar global.Kata kunci: alat bantu meja putar, mesin CNC, harga pokok produksi

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Sign in / Sign up

Export Citation Format

Share Document