scholarly journals POS0036 AN ARTIFICIAL INTELLIGENCE MODEL IN RHEUMATOLOGY: INTERPRETATION OF THE SACROILIAC JOINT GRAPHY IN ANKYLOSING SPONDYLITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 223.3-224
Author(s):  
A. C. Genç ◽  
Z. N. Kaya ◽  
F. Turkoglu Genc ◽  
L. Genc Kaya ◽  
Z. Öztürk ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ankylosing Spondylitis ◽  
Test Performance ◽  
Sacroiliac Joint ◽  
Functional Disability ◽  
Axial Spondyloarthritis ◽  
Low Cost ◽  
Training Data ◽  
Data Set ◽  
Grade 3

Background:There is an average of 8 years delay in the diagnosis of ankylosing spondylitis (AS). The most important danger of late diagnosis is that the disease can cause physical and functional disability (2). There is no specific diagnostic biomarker for AS. Sacroiliac joint (SIJ) radiography is frequently used in the diagnosis and follow-up of AS due to its easy accessibility and low cost. It can be classified as grade 0, 1, 2, 3, 4, and these classes may not be sharply separated from each other (3).Objectives:Interpretation of the SIJ radiography may differ from physician to physician. In fact, the same physician may interpret it differently at different times (3). We wanted to find a solution to the intraobserver disagreement problem with the artificial intelligence model.Methods:The SIJ radiography of 590 patients who applied to our center were divided into 3 categories as right and left, separately, grade 0, grade 1-2, grade 3-4, and an educational data set was prepared for the object recognition method. 488 images were augmented through noise from 490 images in the training data. 242 articular objects were trained for grade 0, 278 for grade 1-2, and 1426 for grade 2-3. The model was tested with 100 images for 36 joint objects for grade 0, 29 for grade 1-2, and 135 for grade 3-4 to create a computer vision-artificial intelligence model (image 1).Results:Training performance is 70% for grade 0, %63 for grade 1-2, %90 for grade 3-4 and test performance is %52 for grade 0, %24 for grade 1-2, %86 intersection over union (I/U:Intersection over Union is a form of measurement used to indicate the accuracy of an object detector.) for grade 3-4. The mean average precision (mAP) score of our object detection model is %65.9 for test data set (image 1). The estimation quality of the model can be affected by the distribution and number of each class.Conclusion:The experience of the x-ray technician, dose adjustment, and position differences due to patient compliance complicate the standardization of SIJ radiography and this may cause interobserver disagreement (3). Artificial intelligence models to be created with a larger and homogeneous data set in order to ensure objective standardization in the interpretation of the SIJ graph can help physicians.References:[1]Braun J. ‘Axial spondyloarthritis including ankylosing spondylitis’ Rheumatology (Oxford). 2018 1;57(suppl_6):vi1-vi3[2]Rudwaleit M, van der Heijde D, Khan MA, Braun J, Sieper J. How to diagnose axial spondyloarthritis early. Ann Rheum Dis 2004; 63:535-543.[3]van den Berg, R. et al. Agreement between clinical practice and trained central reading in reading of sacroiliac joints on plain pelvic radiographs. Results from the DESIR cohort. Arthritis Rheumatol 66, 2403–2411 (2014).Disclosure of Interests:None declared.

scholarly journals THU0395 EFFICACY OF IXEKIZUMAB ON DISEASE ACTIVITY AND QUALITY OF LIFE IN PATIENTS WITH ACTIVE NON-RADIOGRAPHIC AXIAL SPONDYLOARTHRITIS AND OBJECTIVE SIGNS OF INFLAMMATION, STRATIFIED BY BASELINE CRP/SACROILIAC JOINT MRI STATUS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 432-433
Author(s):  
W. P. Maksymowych ◽  
H. Marzo-Ortega ◽  
M. Ǿstergaard ◽  
L. S. Gensler ◽  
J. Ermann ◽  
...  
Keyword(s):  
Ankylosing Spondylitis ◽  
Treatment Group ◽  
Disease Activity ◽  
Sacroiliac Joint ◽  
Axial Spondyloarthritis ◽  
Physical Component Score ◽  
Research Support ◽  
Advisory Boards ◽  
Sf 36 ◽  
Active Sacroiliitis

Background:Ixekizumab (IXE), a high-affinity anti-interleukin-17A monoclonal antibody, is effective in patients (pts) with active non-radiographic axial spondyloarthritis (nr-axSpA), who had elevated C-reactive protein (CRP) and/or active sacroiliitis on magnetic resonance imaging (MRI).1Objectives:To determine if disease activity and patient-reported outcomes at Week 16 were similar between groups after stratifying pts by CRP/sacroiliac joint (SIJ) MRI status at baseline.Methods:COAST-X (NCT02757352) included pts with active nr-axSpA and objective signs of inflammation, i.e. presence of sacroiliitis on MRI (Assessment of Spondyloarthritis International Society [ASAS]/ Outcome Measures in Rheumatology criteria) or elevation of serum CRP (>5.0 mg/L). Pts were randomized 1:1:1 to receive subcutaneous 80 mg IXE every 4 weeks (Q4W) or Q2W, or placebo (PBO). Depending on the baseline values of CRP and MRI SIJ (Spondyloarthritis Research Consortium of Canada [SPARCC] score), pts in the intent-to-treat population (N=239) were divided into 3 subgroups (CRP >5 and MRI ≥2; CRP ≤5 and MRI ≥2; CRP >5 and MRI <2). Logistic regression analysis with treatment, subgroup, and treatment-by-subgroup interaction was used to detect treatment group differences in ASAS40, Ankylosing Spondylitis Disease Activity Score (ASDAS) <2.1 (low disease activity), and Bath Ankylosing Spondylitis Disease Activity Index 50 (BASDAI50) responses at Week 16. Analysis of covariance model with baseline value, treatment, subgroup, and treatment-by-subgroup interaction was used to detect the treatment group difference in change from baseline in Short Form-36 physical component score (SF-36 PCS).Results:The proportion of pts achieving ASAS40 (primary endpoint), ASDAS <2.1, and BASDAI50 (secondary endpoints) was higher in IXE treatment groups compared to PBO at Week 16 (Figure 1). The response rates in IXE-treated subjects were higher in all subgroups (CRP >5 and MRI ≥2; CRP ≤5 and MRI ≥2; CRP >5 and MRI <2) without consistent differences in efficacy between the subgroups. Similarly, pts in the IXE groups showed improvement in SF-36 PCS scores (secondary endpoint) versus pts on PBO at Week 16 (Figure 2).Conclusion:Pts with active nr-axSpA and objective signs of inflammation at baseline who were treated with IXE showed an overall improvement in the signs and symptoms of the disease. The efficacy was not different between pts with both elevated CRP and active sacroiliitis on MRI and pts with either elevated CRP or active sacroiliitis on MRI.References:[1]Deodhar A, et al.Lancet.2020.Disclosure of Interests:Walter P Maksymowych Grant/research support from: Received research and/or educational grants from Abbvie, Novartis, Pfizer, UCB, Consultant of: WPM is Chief Medical Officer of CARE Arthritis Limited, has received consultant/participated in advisory boards for Abbvie, Boehringer, Celgene, Eli-Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, UCB, Speakers bureau: Received speaker fees from Abbvie, Janssen, Novartis, Pfizer, UCB., Helena Marzo-Ortega Grant/research support from: Janssen, Novartis, Consultant of: Abbvie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Speakers bureau: Abbvie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Takeda, UCB, Mikkel Ǿstergaard Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Merck, and Novartis, Consultant of: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Speakers bureau: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Lianne S. Gensler Grant/research support from: Pfizer, Novartis, UCB, Consultant of: AbbVie, Eli Lilly, GSK, Novartis, UCB, Joerg Ermann Grant/research support from: Boehringer-Ingelheim, Pfizer, Consultant of: Abbvie, Eli Lilly, Janssen, Novartis,Pfizer, Takeda, UCB, Atul Deodhar Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Denis Poddubnyy Grant/research support from: AbbVie, MSD, Novartis, and Pfizer, Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, MSD, Novartis, Pfizer, Roche, UCB, Speakers bureau: AbbVie, Bristol-Myers Squibb, Eli Lilly, MSD, Novartis, Pfizer, Roche, UCB, David Sandoval Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Rebecca Bolce Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Andris Kronbergs Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Soyi Liu Leage Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Gabriel Doridot Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Vladimir Geneus Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Ann Leung: None declared, David Adams Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Martin Rudwaleit Consultant of: AbbVie, BMS, Celgene, Janssen, Eli Lilly, MSD, Novartis, Pfizer, Roche, UCB Pharma

Prognostic Model for Stratification of Radioresistant Nasopharynx Carcinoma to Curative Salvage Radiotherapy

2018 ◽  
Vol 36 (9) ◽  
pp. 891-899 ◽  
Author(s):  
You Quan Li ◽  
Yun Ming Tian ◽  
Sze Huey Tan ◽  
Ming Zhu Liu ◽  
Grace Kusumawidjaja ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Index ◽  
Intensity Modulated Radiotherapy ◽  
Training Data ◽  
Cancer Center ◽  
Weight Discrimination ◽  
Data Set ◽  
Training Cohort ◽  
Risk Patients ◽  
Grade 3

Purpose To investigate for a prognostic index (PI) to personalize recommendations for salvage intensity-modulated radiotherapy (IMRT) in patients with locally recurrent nasopharyngeal carcinoma (lrNPC). Methods Patients with lrNPC from two academic institutions (Sun Yat-Sen University Cancer Center [SYSUCC-A; n = 251 (training cohort)] and National Cancer Centre Singapore [NCCS; n = 114] and SYSUCC-B [n = 193 (validation cohorts)]) underwent salvage treatment with IMRT from 2001 to 2015. Primary and secondary clinical end points were overall survival (OS) and grade 5 toxicity-free rate (G5-TFR), respectively. Covariate inclusion to the PIs was qualified by a multivariable two-sided P < .05. Discrimination and calibration of the PIs were assessed. Results The primary PI comprised covariates that were adversely associated with OS in the training cohort (gross tumor volumerecurrence hazard ratio [HR], 1.01/mL increase [ P < .001], agerecurrence HR, 1.02/year increase [ P = .008]; repeat IMRT equivalent dose in 2-Gy fractions [EQD2] ≥ 68 Gy HR, 1.42 [ P = .03]; prior radiotherapy-induced grade ≥ 3 toxicities HR, 1.90 [ P = .001]; recurrent tumor [rT]-category 3 to 4 HR, 1.96 [ P = .005]), in ascending order of weight. Discrimination of the PI for OS was comparable between training and both validation cohorts (Harrell’s C = 0.71 [SYSUCC-A], 0.72 [NCCS], and 0.69 [SYSUCC-B]); discretization by using a fixed PI score cutoff of 252 determined from the training data set yielded low- and high-risk subgroups with disparate OS in the validation cohorts (NCCS HR, 3.09 [95% CI, 1.95 to 4.89]; SYSUCC-B HR, 3.80 [95% CI, 2.55 to 5.66]). Our five-factor PI predicted OS and G5-TFR (predicted v observed 36-month OS and G5-TFR, 22% v 15% and 38% v 44% for high-risk NCCS and 26% v 31% and 45% v 46% for high-risk SYSUCC-B). Conclusion We present a validated PI for robust clinical stratification of radioresistant NPC. Low-risk patients represent ideal candidates for curative repeat IMRT, whereas novel clinical trials are needed in the unfavorable high-risk subgroup.

Artificial intelligence (AI) to improve patient outcomes in community oncology practices.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18098-e18098
Author(s):  
John Frownfelter ◽  
Sibel Blau ◽  
Ray D. Page ◽  
John Showalter ◽  
Kelly Miller ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Goodness Of Fit ◽  
Predictive Analytics ◽  
Training Data ◽  
Model Specification ◽  
Care Plan ◽  
Cancer Type ◽  
Pilot Program ◽  
Data Set ◽  
Ecog Ps

e18098 Background: Artificial Intelligence(AI) for predictive analytics has been studied extensively in diagnostic imaging and genetic testing. Cognitive analytics adds by suggesting interventions that optimize health outcomes using real-time data and machine learning. Herein, we report the results of a pilot study of the Jvion, Inc. Cognitive Clinical Success Machine (CCSM), an eigen vector-based deep learning AI technology. Methods: The CCSM uses electronic medical record (EMR) and publicly available socioeconomic/behavioral databases to create a n-dimensional space within which patients are mapped along vectors resulting in thousands of relevant clusters of clinically/behaviorally similar patients. These clusters have a mathematical propensity to respond to a clinical intervention which are updated dynamically with new data from the site. The CCSM generates recommendations for the provider to consider as they develop a care plan based on the patients’ cluster. We tested and trained the CCSM technology at 3 US oncology practices for the risk (low, intermediate, high) of 4 specific outcomes: 30 day severe pain, 30 day mortality, 6 month clinical deterioration (ECOG-PS), and 6 month diagnosis of major depressive disorder (MDD). We report the accuracy of the CCSM based on the testing and training data sets. Area under the curve (AUC) was calculated to show goodness of fit of classification models for each outcome. Results: In the training/testing data set there were 371,787 patients from the 3 sites: female = 61.3%; age ≤ 50 = 21.3%, 51-65 = 26.9%, > 65 = 51.9%; white/Caucasian = 43.4%, black/African American = 5.9%, unknown race = 43.4%. Cancer types were unknown/missing for 66.3% of patients and stage for 90.4% of patients. AUC range per vector: 30 day severe/recurrent pain = 0.85-0.90; 30-day mortality = 0.86-0.97; 6-month ECOG-PS decline of 1 point = 0.88-0.92; and 6-month diagnosis of MDD = 0.77-0.90. Conclusions: The high AUC indicates good separation between true positives/negatives (proper model specification for classifying the risk of each outcome) regardless of the degree of missing data for variables including cancer type and stage. Following testing, a 6 month pilot program was implemented (06/2018-11/2018). Final results of the pilot program are pending.

Liquid Pipeline Rupture Detection Using Multiple Artificial Intelligence Classifiers During Steady-State and Transient Operations

2020 ◽  
Author(s):  
Christopher MacDonald ◽  
Michael Yang ◽  
Shawn Learn ◽  
Ron Hugo ◽  
Simon Park
Keyword(s):  
Artificial Intelligence ◽  
Training Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Transient Model ◽  
Rule Based ◽  
Detection Systems ◽  
Pump Station ◽  
Neuro Fuzzy

Abstract There are several challenges associated with existing rupture detection systems such as their inability to accurately detect during transient (such as pump dynamics) conditions, delayed responses and their inability to transfer models to different pipeline configurations easily. To address these challenges, we employ multiple Artificial Intelligence (AI) classifiers that rely on pattern recognitions instead of traditional operator-set thresholds. AI techniques, consisting of two-dimensional (2D) Convolutional Neural Networks (CNN) and Adaptive Neuro Fuzzy Interface Systems (ANFIS), are used to mimic processes performed by operators during a rupture event. This includes both visualization (using CNN) and rule-based decision making (using ANFIS). The system provides a level of reasoning to an operator through the use of the rule-based AI system. Pump station sensor data is non-dimensionalized prior to AI processing, enabling application to pipeline configurations outside of the training data set. AI algorithms undergo testing and training using two data sets: laboratory-collected data that mimics transient pump-station operations and real operator data that includes Real Time Transient Model (RTTM) simulated ruptures. The use of non-dimensional sensor data enables the system to detect ruptures from pipeline data not used in the training process.

scholarly journals Predictors of remission in patients with non-radiographic axial spondyloarthritis receiving open-label adalimumab in the ABILITY-3 study

RMD Open ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e000917 ◽  
Author(s):  
Joachim Sieper ◽  
Robert Landewé ◽  
Marina Magrey ◽  
Jaclyn K Anderson ◽  
Sheng Zhong ◽  
...  
Keyword(s):  
Ankylosing Spondylitis ◽  
Sacroiliac Joint ◽  
Axial Spondyloarthritis ◽  
Inactive Disease ◽  
Final Visit ◽  
Hla B27 ◽  
Open Label ◽  
Younger Age ◽  
Adalimumab Therapy ◽  
Male Sex

BackgroundThis analysis assessed baseline predictors of remission in patients with non-radiographic axial spondyloarthritis (nr-axSpA) who received open-label adalimumab therapy.MethodsABILITY-3 enrolled 673 adult patients with nr-axSpA who had objective evidence of inflammation by MRI or elevated high-sensitivity C reactive protein at screening, active disease and an inadequate response to two or more non-steroidal anti-inflammatory drugs. Patients received adalimumab 40 mg every other week during a 28-week open-label lead-in period. Clinical remission was defined as Ankylosing Spondylitis Disease Activity Score inactive disease (ASDAS ID; score <1.3) and Assessment of SpondyloArthritis international Society partial remission (ASAS PR; score <2/10 in each of the four ASAS domains). Stepwise logistic regression was used to identify baseline predictors of remission at week 12 and at final visit (last postbaseline visit up to week 28). Only patients without missing data were included.ResultsOverall, 593 patients were included in the ASDAS ID and 596 in the ASAS PR analysis at week 12. Younger age (≤45 years), male sex, positive human leucocyte antigen (HLA)-B27 and higher Spondyloarthritis Research Consortium of Canada (SPARCC) MRI sacroiliac joint score were consistent predictors of remission by both ASAS ID and ASDAS PR at week 12. Results were generally similar in the final visit analysis. Other variables did not consistently predict remission.ConclusionsIn ABILITY-3, consistent and strong baseline predictors of remission included younger age, male sex, HLA-B27 positivity and higher SPARCC MRI sacroiliac joint score among patients with active nr-axSpA receiving adalimumab therapy, similar to previous findings in ankylosing spondylitis.

scholarly journals Autonomous Interpretation of the Microstructure of Steels and Special Alloys

2019 ◽  
Vol 949 ◽  
pp. 24-31 ◽  
Author(s):  
Bartłomiej Mulewicz ◽  
Grzegorz Korpala ◽  
Jan Kusiak ◽  
Ulrich Prahl
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Classification Systems ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Steel Microstructure ◽  
Different Types ◽  
Steel Grades

The main objective of presented research is an attempt of application of techniques taken from a dynamically developing field of image analysis based on Artificial Intelligence, particularly on Deep Learning, in classification of steel microstructures. Our research focused on developing and implementation of Deep Convolutional Neural Networks (DCNN) for classification of different types of steel microstructure photographs received from the light microscopy at the TU Bergakademie, Freiberg. First, brief presentation of the idea of the system based on DCNN is given. Next, the results of tests of developed classification system on 8 different types (classes) of microstructure of the following different steel grades: C15, C45, C60, C80, V33, X70 and carbide free steel. The DCNN based classification systems require numerous training data and the system accuracy strongly depend on the size of these data. Therefore, created data set of numerous micrograph images of different types of microstructure (33283 photographs) gave the opportunity to develop high precision classification systems and segmentation routines, reaching the accuracy of 99.8%. Presented results confirm, that DCNN can be a useful tool in microstructure classification.

scholarly journals Low Cost Optical-electronic Sensor Development Based on Raman Spectroscopy for Liquid

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Luqman Aji Kusumo ◽  
Totok Mujiono ◽  
Hendra Kusuma
Keyword(s):  
Raman Spectroscopy ◽  
Low Cost ◽  
Spectral Response ◽  
Scattered Light ◽  
Test Tube ◽  
Training Data ◽  
Validation Data ◽  
Data Set ◽  
Electronic Sensor ◽  
Cmos Sensor

Spectroscopy is a method that used to identifychemical structure of substances using its spectral patterncharacteristics. Optical spectroscopy term can be applied to anykind of optical photon interactions with matter. Ramanspectroscopy essentially shows spectral response like thewavelength of scattered light is shifted regarding initializingexcitation wavelength. In this paper, we propose a design of lowcost optical-electronic sensor based on Raman spectroscopy.This low cost optical-electronic sensor employs a violet-blue 405nm wavelength laser diode, a biconvex lens with 5 cm diameterand focus point, a test tube, and a Complementary Metal OxideSemiconductor (CMOS) sensor. We tested this low cost opticalelectronic sensor based on Raman spectroscopy in darkcondition. Combination of these hardware and components canprovide measurement result to any liquid sample. From thisexperiment, even all liquid samples that used to test thiscombination of hardware and components are transparent, theystill have different Raman spectra. This combination ofhardware and components can be implemented into someapplication for instance body liquid measurement such as blood.In specific application, we need to employ data analysis and abunch of data set which are organized into three different groupsuch as training data, validation data, and test data group,combined with this developed instrumentation.

Transferable Pipeline Rupture Detection Using Multiple Artificial Intelligence Classifiers During Transient Operations

2021 ◽  
Author(s):  
Christopher Macdonald ◽  
Jaehyun Yang ◽  
Shawn Learn ◽  
Simon S. Park ◽  
Ronald J. Hugo
Keyword(s):  
Artificial Intelligence ◽  
Response Times ◽  
Training Data ◽  
Sensor Data ◽  
Data Sets ◽  
Flow Loop ◽  
Data Set ◽  
Transient Model ◽  
Rule Based ◽  
Pump Station

Abstract There are several challenges associated with existing pipeline rupture detection systems, including an inability to accurately detect during transient conditions (such as changes in pump operating points), an inability to easily transfer from one pipeline configuration to another, and relatively slow response times. To address these challenges, we employ multiple Artificial Intelligence (AI) classifiers that rely on pattern recognition instead of traditional operator-set thresholds. AI techniques, consisting of two-dimensional (2D) Convolutional Neural Networks (CNN) and Adaptive Neuro Fuzzy Interface Systems (ANFIS), are used to mimic processes performed by operators during a rupture event. This includes both visualization (using CNN) and rule-based decision making (using ANFIS). The system provides a level of reasoning to an operator through the use of rule-based AI. Pump station sensor data is non-dimensionalized prior to AI processing, enabling pipeline configurations outside of the training data set, independent of geometry, length, and medium. AI algorithms undergo testing and training using two data sets: laboratory-collected flow loop data that mimics transient pump-station operations and real operator data that include simulated ruptures using the Real Time Transient Model (RTTM). The multiple AI classifier results are fused together to provide higher reliability especially detecting ruptures from pipeline data not used in the training process.

scholarly journals Implementation of artificial intelligence technologies in human resources management in Russian companies

2021 ◽  
pp. 97-121
Author(s):  
Yuri Petrunin ◽  
◽  
Anna Pugacheva ◽  
Keyword(s):  
Artificial Intelligence ◽  
Personnel Management ◽  
Personnel Selection ◽  
Human Resources Management ◽  
Training Data ◽  
Data Set ◽  
Management Processes ◽  
Efficiency And Effectiveness ◽  
Selection Of

The article examines the problems and prospects of the introduction of artificial intelligence technologies in the selection of personnel in commercial companies in Russia. In recent years, both the number of applications and the number of scientific articles on the use of artificial intelligence technologies in personnel management processes both in our country and abroad have been growing. However, at present, there is a certain gap in the issues of evaluating the effectiveness of the use of these technologies, identifying the most promising areas for the use of artificial intelligence in the selection of personnel, and determining the factors that affect the results of such implementations in relation to Russian conditions. The survey of experts and practitioners in the field of working with artificial intelligence technologies in the field of personnel management of leading Russian companies allowed us to partially answer the relevant questions. The analysis of the respondents ' responses showed that these technologies favorably affect the selection of employees, improve the quality of selection, increase its speed, unload employees, save money resources and help eliminate bias towards candidates. The factors that increase the efficiency and effectiveness of the implementation of artificial intelligence technologies in the selection of personnel were identified: the category of selected employees, the scale of selection, and the possibility of integration with existing software. The difficulties of using artificial intelligence technologies in the selection of personnel include the presence of atypical positions for selection, the dependence of the results on the quality and volume of the training data set, and the possible reluctance of candidates to communicate with the robot. According to the results of the study, we can make a reasonable conclusion that artificial intelligence in the field of personnel selection, despite the presence of certain problems, has many advantages, as well as great prospects for development.

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