scholarly journals Artificial intelligence and dichotomy on benefits and challenges: what do healthcare providers say?

2019 ◽  
Vol 7 (9) ◽  
pp. 3251
Author(s):  
Bharat Singh ◽  
Surekha Kashyap ◽  
Ankita Grover
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Data Privacy ◽  
Healthcare Providers ◽  
Technical Support ◽  
Staff Satisfaction ◽  
Job Experience ◽  
Medical College ◽  
Initial Resistance ◽  
Job Profile

Background: In a developing country like India, with a vibrant information technology (IT) sector, employing Artificial Intelligence (AI) should be carefully weighed before its introduction in healthcare with relation to perception of healthcare providers (HCP's/Doctors).  Methods: This qualitative study was conducted in medical college and affiliated hospital in India. Initially a pilot study was conducted for reliability and internal consistency of questionnaire. Thereafter, pre-tested questionnaire was distributed to 153 healthcare providers and their responses were analyzed on SPSS version 20.0 (IBM) to identify the demographic and job-related differences in their perception regarding the benefits and challenges of using AI in healthcare.Results: Most of respondent were agreed upon the benefits of using AI in healthcare and most cited benefits were speedy decision making, better resource utilization and improvement in staff satisfaction. Similarly most cited challenges were lack of training on AI enabled machines, lack of skilled technical support, high cost of AI and data privacy issue. Further, Age and Job experience were significantly associated with benefits like timely and speedy decision making, improvement in the patient and staff satisfaction respectively. Furthermore, Age, Department, Job experience, Job profile were significantly associated with challenges like high cost of AI, lack of skilled technical support, lack of training in AI enabled machines and lack of trust in AI among patients.Conclusions: Significant challenges of using AI in healthcare with demographic and job related variable based on the results of this research paper need to be resolved first in order to overcome the initial resistance in employing AI in healthcare. 

scholarly journals Intellectualization of engineering and technical support of railway construction

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Aleksey Polyanskiy
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Technological Process ◽  
Technical Support ◽  
Automated System ◽  
Railway Track ◽  
New Paradigm ◽  
Technological Processes ◽  
Railway Construction ◽  
Over Time

The article is devoted to the theoretical foundations of improving the engineering support of railway construction. One of the main purposes of the existing engineering support system is to evaluate the results of development and monitor the implementation of technological processes for the construction of railway facilities. In the course of the study, it was determined that a number of stages in the development and implementation of technological processes contain tasks for which the use of automated design and control systems is sufficient. However, there are tasks whose solution depends on the experience and intuitive abilities of the engineer (developer of organizational and technological documentation). To solve such problems, in addition to settlement procedures, logical ones are also necessary. In addition, the intensification of railway construction, many restrictions on the production of works and resources, as well as deviations from planned indicators, require prompt decision-making aimed at compliance with design requirements. Obviously, the total amount of information and data on the design and actual technological processes does not guarantee the efficiency and rationality in decision-making by an engineer. In this case, to solve technological, organizational and managerial tasks, it becomes possible to use some methods and means of artificial intelligence. In this regard, it was proposed to supplement the existing system of engineering and technical support for railway construction with a subsystem of engineering and intellectual support for the technological process of constructing a railway track. For the purpose of intellectualization and the formation of a new paradigm of engineering support for railway construction, an analysis was made of modern and promising practices in the development and implementation of technological processes from the perspective of the life cycle of a railway track object. It was found that modern technological processes for the construction of railway facilities should be flexible to changing working conditions. The study showed that this can be achieved through the formation of adaptive digital technological regulations. The basis of the digital regulation is the information model of the technological process. The model formation procedure is divided into stages containing tasks, the solution of which is possible using artificial intelligence tools such as expert systems, artificial neural networks, and genetic algorithms. A fundamental feature of the engineering and intellectual support of the technological process is the possibility of its operational regulation based on the results of monitoring its development over time. A feature of this approach is the need for the operational processing of a large amount of data that determine the development of technological processes over time, the conditions of work, the production capabilities of construction (contracting) organizations. For this, the mathematical and conceptual models of an intelligent automated system have been developed. Its main purpose is the operational solution of the problems of development and implementation of the technological process of construction of railway facilities. The results obtained during the study, as well as the developed tools, made it possible to determine the possibilities of integrating the developed methodology into the existing system for designing and managing the construction of railway facilities. The results given in the article were obtained during the dissertation research performed by the author.

scholarly journals Regulatory responses to medical machine learning

2020 ◽  
Author(s):  
Timo Minssen ◽  
Sara Gerke ◽  
Mateo Aboy ◽  
Nicholson Price ◽  
Glenn Cohen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Privacy ◽  
Healthcare Providers ◽  
Medical Data ◽  
Training Set ◽  
International Context ◽  
International Perspectives ◽  
The Usa ◽  
New Applications

Abstract Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML). MML is based on the application of machine learning (ML) algorithms to automatically identify patterns and act on medical data to guide clinical decisions. MML poses challenges and raises important questions, including (1) How will regulators evaluate MML-based medical devices to ensure their safety and effectiveness? and (2) What additional MML considerations should be taken into account in the international context? To address these questions, we analyze the current regulatory approaches to MML in the USA and Europe. We then examine international perspectives and broader implications, discussing considerations such as data privacy, exportation, explanation, training set bias, contextual bias, and trade secrecy.

scholarly journals Understanding, Explaining, and Utilizing Medical Artificial Intelligence

2021 ◽  
Author(s):  
Romain Cadario ◽  
Chiara Longoni ◽  
Carey K Morewedge
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Decision Making ◽  
Healthcare Providers ◽  
Cost Effective ◽  
Decision Processes ◽  
Medical Decision ◽  
Medical Decisions ◽  
Important Barrier ◽  
Nationally Representative

Medical artificial intelligence is cost-effective, scalable, and often outperforms human providers. One important barrier to its adoption is the perception that algorithms are a “black box”—people do not subjectively understand how algorithms make medical decisions, and we find this impairs their utilization. We argue a second barrier is that people also overestimate their objective understanding of medical decisions made by human healthcare providers. In five pre- registered experiments with convenience and nationally representative samples (N = 2,699), we find that people exhibit such an illusory understanding of human medical decision making (Study 1). This leads people to claim greater understanding of decisions made by human than algorithmic healthcare providers (Studies 2A-B), which makes people more reluctant to utilize algorithmic providers (Studies 3A-B). Fortunately, we find that asking people to explain the mechanisms underlying medical decision making reduces this illusory gap in subjective understanding (Study 1). Moreover, we test brief interventions that, by increasing subjective understanding of algorithmic decision processes, increase willingness to utilize algorithmic healthcare providers without undermining utilization of human providers (Studies 3A-B). Corroborating these results, a study on Google testing ads for an algorithmic skin cancer detection app shows that interventions that increase subjective understanding of algorithmic decision processes lead to a higher ad click-through rate (Study 4). Our findings show how reluctance to utilize medical algorithms is driven both by the difficulty of understanding algorithms, and an illusory understanding of human decision making.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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