Decision Support Technology Research Articles

The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes

The paper is focused on solving the modern scientific and applied problem related to development and practical use in Decision Support Systems (DSS) of information technologies directed towards forecasting of non-linear non-stationary processes (NNP) that take place in economy and finances as well as in many other areas of activities. Thus, object of study are non-linear non-stationary processes taking place in economy and financial sphere. The basic problem of the study is development of new mathematical models and methods of analysis and forecasting non-linear non-stationary processes in economy and finances, improvement of information decision support technologies that would help to enhance quality of forecast estimates and respective decisions in conditions of uncertainties and risk. The methods given in the paper are used for automating the process of intellectual data analysis that describe the processes under study and automatizing model constructing procedures. As a result of the study performed the information technology was developed to be used in DSS based upon system analysis principles, taking into consideration possible data uncertainties, regression and intellectual data analysis. The technology provides for constructing adequate models of the process under study and computing high quality forecast estimates. The particular feature of the approach proposed is that it provides for high quality of experimental results due to taking into consideration special features of non-linear non-stationary processes that take place in various spheres of activities and their evolution is influenced by many specific factors. The use of the technology proposed in decision support systems of enterprises, state governmental organs, and local self-government will create basis for effective solving the tasks of governing development of non-linear non-stationary processes that take place in many spheres of activities. The approaches proposed in the paper can be used in practice as separately as well as parts of existing information systems at enterprises and organizations.

Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems.

Expert assessments with pre-defined numerical or language terms can limit the scope of decision-making models. We propose that decision-making models can incorporate expert judgments expressed in natural language through sentiment analysis. To help make more informed choices, we present the Sentiment Analysis in Recommender Systems with Multi-person, Multi-criteria Decision Making (SAR-MCMD) method. This method compiles the opinions of several experts by analyzing their written reviews and, if applicable, their star ratings. The growth of online applications and the sheer amount of available information have made it difficult for users to decide which information or products to select from the Internet. Intelligent decision-support technologies, known as recommender systems, leverage users' preferences to suggest what they might find interesting. Recommender systems are one of the many approaches to dealing with information overload issues. These systems have traditionally relied on single-grading algorithms to predict and communicate users' opinions for observed items. To boost their predictive and recommendation abilities, multi-criteria recommender systems assign numerous ratings to various qualities of products. We created, manually annotated, and released the technique in a case study of restaurant selection using 'TripAdvisor reviews', 'TMDB 5000 movies', and an 'Amazon dataset'. In various areas, cutting-edge deep learning approaches have led to breakthrough progress. Recently, researchers have begun to focus on applying these methods to recommendation systems, and different deep learning-based recommendation models have been suggested. Due to its proficiency with sparse data in large data systems and its ability to construct complex models that characterize user performance for the recommended procedure, deep learning is a formidable tool. In this article, we introduce a model for a multi-criteria recommender system that combines the best of both deep learning and multi-criteria decision-making. According to our findings, the suggested system may give customers very accurate suggestions with a sentiment analysis accuracy of 98%. Additionally, the metrics, accuracy, precision, recall, and F1 score are where the system truly shines, much above what has been achieved in the past.

Enhancing Efficiency and Cost-Effectiveness: A Groundbreaking Bi-Algorithm MCDM Approach

Numerous scholars have thoroughly studied the topic of choosing machines considering the progress and technological growth seen in machinery options. This scholarly investigation explores decision-making methods specifically designed to aid the selection of machines in manufacturing businesses. Additionally, this research emphasizes the need for decision-making frameworks in manufacturing facilities, highlighting the importance of smart machine selection strategies in those contexts. In this research, we show a dual-MCDM approach that includes DEX—decision experts—and the EDAS method that are popularly employed to solve decision-making problems in both academic and practical industries. Throughout the previous decade, business leaders and managers increasingly use MCDM solutions to overcome machine selection challenges. At this time, while various decision-support technologies and procedures have been developed and used, it is essential that we discuss the sequence of our study objectives and drive the proposed method for widening use in practical firms. In short, this research may be helpful as a literature review for MDCM studies and related topics. It will also help executives, engineers, and specialists determine which equipment or machines to create and increase product quality in manufacturing and industry.

Decision support technology for a seller on a marketplace in a competitive environment

This article deals with the problem of improving the effectiveness of a marketplace. The stakeholders of a marketplace are buyers and sellers. The objects are the aggregate of homogeneous products. The effectiveness of the trading platform, which can be characterized by the number of transactions made, will depend on how sufficiently the sellers put up offers. The paper looks at mathematical models to support the decision-making of the seller in making such offers. Focusing not only on the buyer demand but also on the presence of competitors on the site is a distinguishing feature of the models. To describe the competition, the apparatus of game theory is offered, namely the normal form of the game with a bimatrix model with two players: the seller – customer of service and the coalition of other sellers. To match offer and demand, as well as to find the probability of a transaction, fuzzy set theory and aggregation using the Choquet integral are used.

Effect of an Innovative Immunization Practice Model to Improve Population Health: Results of the Project IMPACT Immunizations Scaled Demonstration.

Background: U.S. adult vaccination rates remain low. Community pharmacists have skills and opportunity to improve this shortcoming. This study sought to evaluate an innovative practice model on identification of unmet vaccination needs and their resolution. Methods: This prospective, multi-site, multi-state, observational study was conducted in 22 community pharmacy practices in Iowa and Washington. Adults receiving influenza vaccination, medication therapy review, prescriptions for diabetes or cardiovascular disease, or another clinical encounter with a participating pharmacist from December 2017 through November 2019 were included. Pharmacists reviewed vaccination forecasts generated by clinical decision support technology based on their state immunization information system (IIS) to identify unmet vaccination needs, educate patients, and improve vaccination rates. The primary outcomes were numbers of vaccination forecast reviews, patients educated, unmet vaccination needs identified and resolved, and vaccinations administered. Secondary outcomes included numbers of vaccination declinations; times a forecasted vaccine was not recommended because a contraindication was identified by the pharmacist; and times the patients declined a forecasted vaccine due to self-reported vaccination despite lack of documentation in the state IIS. Descriptive statistics were calculated. Results: Pharmacists reviewed vaccination forecasts for 6,234 patients. The vaccination forecasts predicted there were 11,789 vaccinations needed (1.9 per person). 6,405 of the 11,789 unmet vaccination needs (54.3%) were fulfilled during the study period, including 60% on the same day. Of the forecasted needs, 1,085 (9.2%) were found to be previously administered and 59 (0.5%) contraindicated. The remaining patients received information about their personal vaccination needs and recommendations to be vaccinated. Conclusion: Availability of vaccination histories during patient encounters allowed pharmacists to identify and resolve adult vaccination needs in independent and chain community practice settings.

Testamentary capacity assessment in dementia using artificial intelligence: prospects and challenges.

Testamentary capacity (TC), a set of capacities involved in making a valid Will, has become prominent in capacity evaluations due to the demographic increase in older persons and associated increase in cognitive impairment. The assessment of contemporaneous TC follows the criteria derived from the Banks v Goodfellow case, which do not bind capacity solely on the basis of presence of a cognitive disorder. Although effort is being made for establishing more objective criteria for TC judgment, variations in situational complexity call for incorporating the different circumstances of the testator in capacity assessment. Artificial intelligence (AI) technologies such as statistical machine learning have been used in forensic psychiatry mainly for the prediction of aggressive behavior and recidivism but little has been done in the area of capacity assessment. However, the statistical machine learning model responses are difficult to interpret and explain, which presents problems with regard to the new General Data Protection Regulation (GDPR) of the European Union. In this Perspective we present a framework for an AI decision support tool for TC assessment. The framework is based on AI decision support and explainable AI (XAI) technology.

Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence

Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence

Studies on Multi-Criteria Decision-Making Based Healthcare Systems Using The Machine Learning

There is a lot of information in healthcare and medical records. However, it is challenging for humans to turn data into information and spot hidden patterns in today's digitally-based culture. Effective decision-support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments as well as in different healthcare management activities. Research paper presented an extensive literature survey for Healthcare systems using the machine learning that is based on Multiple criteria decision making (MCDM). Various existing studies are considered for review and a critical analysis is being done through the reviews study which can help to the researchers to explore further research area to cater the need of the field

Evidence of the Contribution of the Technological Progress on Aquaculture Production for Economic Development in China—Research Based on the Transcendental Logarithmic Production Function Method

Technology includes hard technology and soft technology. Material technology embodied in production conditions and working conditions such as machinery, equipment and infrastructure is called hard technology, referring to the technology directly used in the development and production of means of production and means of subsistence, such as product design technology, equipment manufacturing technology, etc. Non-material technology, which embodies the experience, skills and management ability of process management, decision support and information technology, is called soft technology. Hard technologies make things easier and faster, while soft technologies promote flexibility and creativity. However, hard technologies take time to produce and have negative environmental impacts, while soft technologies are simple to produce but hard to use. Hence, finding the right balance between hard and soft technology investment is important for the sustainable increase of productivity. In recent years, China has continuously increased its investment in science and technology in aquaculture industry. However, the majority of the investment has gone to hard technology, which has hampered the long-term development of the industry. This paper aims to look at the status quo of the scientific and technological progress of the aquaculture industry in China and explore how the advancement of hard and soft technologies play a role in the economic growth of the aquaculture industry. Transcendental logarithmic production function is employed to calculate the contribution rate of technological progress on China’s aquaculture industry, and the contribution rates of hard technologies and soft technologies are examined separately. The results indicate that, from 2012 to 2020, the contribution rate of overall technical progress on aquaculture in China was 80.159%, of which 71.720% came from the progress of hard technologies, while only 8.439% came from the progress of soft technologies. Based on this conclusion, the paper calls for a balance of hard and soft technologies in the aquaculture industry in China to ensure a healthy and sustainable aquaculture industry. Policy suggestions are also put forward.

External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images.

State of the art artificial intelligence (AI) models have the potential to become a "one-stop shop" to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images. Eighty-five biopsy proven prostate cancer patients who underwent 68Ga PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at https://gitlab.com/dejankostyszyn/prostate-gtv-segmentation and data processing has been done in agreement with the reference work. When compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model's performance when compared to reader 1 or reader 2 manual contouring). In conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice.

Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies.

This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field's best evidence. We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.

Signal and Visual Approaches for Parkinson's Disease Detection from Spiral Drawings

The development of medical decision-support technologies that provide accurate biomarkers to physicians is an important research area. For example, in the case of Parkinson's Disease (PD), the current supervisions of patients become intrusive, occasional, and subjective. However, new technologies such as wearable devices, signal processing, computer vision, and deep learning could offer a non-intrusive, continuous, and objective solution to help physicians with patient monitoring. The Parkinson's Disease Spiral Drawings public dataset was selected to face PD detection in this work by comparing four representation methods of the X, Y, and the pressure time series: signal, visual, hand-crafted, and fusion. The signal approach uses the Fast Fourier Transform of recording windows and a Convolutional Neural Network for modeling; the visual strategy employs visual transformer features from gray-scale images; the hand-crafted technique utilizes statistics calculated from temporal signals, and the fusion combines the information from the previous approaches. In these procedures, a Random Forest classifier was used for PD detection using the attributes extracted from each type of representation. The best results showed an F1 score of 93.33% and 93.06% at the user level using a signal approach with the three signals for the Static Spiral Task and an image-based proposal with X and Y coordinates for the Dynamic Spiral Task, respectively.

Artificial Intelligence Functionalities During the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has led us to use virtual solutions and emerging technologies such as artificial intelligence (AI). Recent studies have clearly demonstrated the role of AI in health care and medical practice; however, a comprehensive review can identify potential yet not fulfilled functionalities of such technologies in pandemics. Therefore, this scoping review study aims at assessing AI functionalities in the COVID-19 pandemic in 2022. A systematic search was carried out in PubMed, Cochran Library, Scopus, Science Direct, ProQuest, and Web of Science from 2019 to May 9, 2022. Researchers selected the articles according to the search keywords. Finally, the articles mentioning the functionalities of AI in the COVID-19 pandemic were evaluated. Two investigators performed this process. Initial search resulted in 9123 articles. After reviewing the title, abstract, and full text of these articles, and applying the inclusion and exclusion criteria, 4 articles were selectd for the final analysis. All 4 were cross-sectional studies. Two studies (50%) were performed in the United States, 1 (25%) in Israel, and 1 (25%) in Saudi Arabia. They covered the functionalities of AI in the prediction, detection, and diagnosis of COVID-19. To the extent of the researchers' knowledge, this study is the first scoping review that assesses the AI functionalities in the COVID-19 pandemic. Health-care organizations need decision support technologies and evidence-based apparatuses that can perceive, think, and reason not dissimilar to human beings. Potential functionalities of such technologies can be used to predict mortality, detect, screen, and trace current and former patients, analyze health data, prioritize high-risk patients, and better allocate hospital resources in pandemics, and generally in health-care settings.

About Modern Intelligent Tools of Economy and Management

Revolutionary changes are taking place in the field of mathematical, statistical and instrumental methods of economics, a brief review of which is the subject of this article. A new paradigm of mathematical research methods is the basis of the methodology of systemic fuzzy interval mathematics. The role of such tools as terminological systems is great. Among the intellectual tools of economics and management, one of the main places is occupied by methods of making managerial decisions. The basic concepts in mathematical methods and models of decision theory are fuzzy and interval numbers and sets, and therefore the intellectual tools of this theory are based on systemic fuzzy interval mathematics. A variety of expert procedures designed to collect and analyze the subjective opinions of experts use intuition as a basic component of human thinking. Methods of awakening and stimulating intuition deserve careful study and development with a view to applying it to solving practical problems. Methods for the development and application of technologies for information and analytical decision support, i.e. controlling are important for economics and management. Particularly promising are the controlling methods and its component – ​the controlling of science, including scientometric and expert methods of managing scientific activities. And therefore we must bring the paradigm of economic science in line with modern requirements, adequately transform the teaching system. The explosive development of the digital economy, by which we mean modern information and communication technologies in the field of economics and management, makes it possible to form a new paradigm of economic science. Its basis, in our opinion, should be a solidary information economy.

Need for numbers: assessing cancer survivors’ needs for personalized and generic statistical information

BackgroundStatistical information (e.g., on long-term survival or side effects) may be valuable for healthcare providers to share with their patients to facilitate shared decision making on treatment options. In this pre-registered study, we assessed cancer survivors’ need for generic (population-based) versus personalized (tailored towards patient/tumor characteristics) statistical information after their diagnosis. We examined how information coping style, subjective numeracy, and anxiety levels of survivors relate to these needs and identified statistical need profiles. Additionally, we qualitatively explored survivors’ considerations for (not) wanting statistical information.MethodsCancer survivors’ need for statistics regarding incidence, survival, recurrence, side effects and quality of life were assessed with an online questionnaire. For each of these topics, survivors were asked to think back to their first cancer diagnosis and to indicate their need for generic and personalized statistics on a 4-point scale (‘not at all’- ‘very much’). Associations between information coping style, subjective numeracy, and anxiety with need for generic and personalized statistics were examined with Pearson’s correlations. Statistical need profiles were identified using latent class analysis. Considerations for (not) wanting statistics were analyzed qualitatively.ResultsOverall, cancer survivors (n = 174) had a higher need for personalized than for generic statistics (p < .001, d = 0.74). Need for personalized statistics was associated with higher subjective numeracy (r = .29) and an information-seeking coping style (r = .41). Three statistical need profiles were identified (1) a strong need for both generic and personalized statistics (34%), (2) a stronger need for personalized than for generic statistics (55%), and (3) a little need for both generic and personalized statistics (11%). Considerations for wanting personalized cancer statistics ranged from feelings of being in control to making better informed decisions about treatment. Considerations for not wanting statistics related to negative experience with statistics and to the unpredictability of future events for individual patients.ConclusionsIn light of the increased possibilities for using personalized statistics in clinical practice and decision aids, it appears that most cancer survivors want personalized statistical information during treatment decision-making. Subjective numeracy and information coping style seem important factors influencing this need. We encourage further development and implementation of data-driven personalized decision support technologies in oncological care to support patients in treatment decision making.

RULES AND METHOD OF SUPPORTING THE DECISION-MAKING REGARDING THE POSSIBILITY OF EXTRACORPOREAL FERTILIZATION

Considering the impossibility of applying for the paid services of a lawyer in most medical clinics, the legal correctness of decisions regarding the provision of various medical services can be significantly increased by a decision support system or technology regarding the possibility of providing medical services – at the expense of providing a conclusion on the possibility or impossibility of providing this or that medical service, taking into account civil legal grounds.&#x0D; The conducted analysis proved that none of the known systems/technologies are intended to support decision-making regarding the possibility of providing the extracorporeal fertilization, taking into account civil legal grounds. Although the use of such cross-disciplinary systems/technologies can significantly increase the correctness of the decision from a legal point of view, protect the doctor and the patient from legal collisions, provide an opportunity for a quick and free check of the presence of all significant conditions from a legal point of view, as well as recommendations for further implementation or non-implementation of extracorporeal fertilization procedures.&#x0D; The article develops rules and method of supporting the decision-making regarding the possibility of extracorporeal fertilization based on the civil legal grounds, which reflect the peculiarities of decision-making on whether or not to carry out extracorporeal fertilization based on the assessment of the sufficiency of information (compliance with requirements) for such a procedure, ensure adaptation to the specifics of the subject area and is a theoretical basis for the development of a decision-making support system/technology regarding the possibility of providing the extracorporeal fertilization, taking into account civil legal grounds.&#x0D; The developed rules and method ensure: verification of the agreement for the provision of extracorporeal fertilization services for the fulfillment of all essential conditions for the provision of in vitro fertilization from the point of view of civil legal grounds without the participation of legal experts, which ensures the possibility for medical clinics to comply with the requirements of current civil legislation without paying for the services of hired lawyers; a conclusion on the possibility or impossibility of providing the extracorporeal fertilization from the point of view of the civil legislation of Ukraine.&#x0D; The direction of the authors' future research is the realization of the subsystem of information technology for medical decision-making support taking into account civil legal grounds in the form of web-oriented software based on the developed method of supporting the decision-making regarding the possibility of extracorporeal fertilization taking into account civil legal grounds.

Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation.

Pathologies such as cancer metastasis and osteoporosis strongly affect the mechanical properties of the vertebral bone and increase the risk of fragility fractures. The prediction of the fracture risk with a patient-specific model, directly generated from the diagnostic images of the patient, could help the clinician in the choice of the correct therapy to follow. But before such models can be used to support any clinical decision, their credibility must be demonstrated through verification, validation, and uncertainty quantification. In this study we describe a procedure for the generation of such patient-specific finite element models and present a first validation of the kinematics of the spine segment. Quantitative computed tomography images of a cadaveric lumbar spine segment presenting vertebral metastatic lesions were used to generate the model. The applied boundary conditions replicated a specific experimental test where the spine segment was loaded in compression-flexion. Model predictions in terms of vertebral surface displacements were compared against the full-field experimental displacements measured with Digital Image Correlation. A good agreement was obtained from the local comparison between experimental data and simulation results (R2 > 0.9 and RMSE% <8%). In conclusion, this work demonstrates the possibility to apply the developed modelling pipeline to predict the displacement field of human spine segment under physiological loading conditions, which is a first fundamental step in the credibility assessment of these clinical decision-support technology.

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians.

The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for “pragmatic AI-augmentation” that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice—automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.

Formation of management and technological maturity levels of enterprises for their dynamic development

Abstract Currently, the market environment contains many factors influencing the enterprise’s competitiveness. Instability, the unpredictability of events, and insufficiently effective functioning of market mechanisms alter the management focus and processes engaged in an enterprise’s functioning. Some of them are intensive in terms of required resources and finance. Such a dynamic situation requires the enterprise management to build innovative solutions to flexibly respond and timely adapt to change. Therefore, this study aims to develop theoretical and applied approaches to determining the level of managerial and technological maturity of the basic set of technologies implemented in enterprises. In the context of this issue’s development and aiming to achieve the purpose, the study proposed a model approach where the introduction of individual technologies allows combining the rules for determining the enterprise’s management and the technological maturity level, i.e., its readiness for such changes. The construction of the model was based on the analysis and calculation of statistical data from four groups of technologies (corporate, industrial, decision support, and information technologies, which are divided into subclasses) and based on the theory of dynamic innovation development. The results were tested at seven food industry enterprises in Ukraine. Based on the study, the actual level of managerial and technological maturity of enterprises was determined, creating one complex set of technologies that depend on the level and structural changes in management and the level of technological maturity of enterprises. It can be used as a typical model for differently sized enterprises representing various industries.

Применение технологий искусственного интеллекта в научно-технологическом прогнозировании

Currently, artificial intelligence is widely used in the formation of social, economic and environmental forecasts. When creating artificial intelligence, machine learning technologies, deep learning technology and searching for patterns in information arrays (Big Data), artificial language processing and generation technologies, etc. are widely used. At the same time, the issue of using artificial intelligence in scientific and technological forecasting has not been worked out enough. The purpose of the study was to find effective approaches to the use of artificial intelligence technologies in the formation of scientific and technological forecasts. The objective of the study was to identify artificial intelligence technologies that can be used at various stages of the life cycle of scientific and technological forecasting and to specify individual ways of using them to solve problems of predicting the level of development of science, engineering and technology compared to the world. This confirms the relevance of the study. The main research method is the analysis of domestic and foreign publications and best practices for using artificial intelligence technologies in scientific and technological forecasting, as well as the results of research work performed by the authors in the field of scientific and technological forecasting and adapting them to improve the formation of forecasts in the context of digital transformation of the economy and enterprises The authors considered the structure of artificial functions performed by technologies and identified priority areas for the use of artificial intelligence at various stages of scientific and technological forecasting. The expediency and features of the use of semantic analysis and cognitive technologies in predicting the level of readiness of equipment and technologies in comparison with the world under various scenario conditions are shown, which provides the greatest efficiency of the adopted solution. The issues of information and analytical support for the use of artificial intelligence in scientific and technological forecasting based on information technologies for decision support are considered. The novelty of the presented results lies in the fact that, for the first time, the authors describe the possibilities of using the most effective artificial intelligence technologies at various stages of the life cycle for the formation of scientific and technological forecasts from the standpoint of a systematic and integrated approach.