Current status and applications of Artificial Intelligence (AI) in medical field: An overview

Calandra

BMC Med Inform Decis Mak

et al. 2021

308

101

Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Section: Methodsmentioning

confidence: 99%

Section: Methodsologymentioning

confidence: 99%

The role of artificial intelligence in healthcare: a structured literature review

Calandra

BMC Med Inform Decis Mak

et al. 2021

308

101

“…Despite the striking usefulness and value of AI technology in the healthcare sector (e.g., Haleem et al, 2019;Hamet & Tremblay, 2017), many researchers still examine its usage from mainly patients' perspectives (e.g., Fadhil & Gabrielli, 2017;Nadarzynski et al, 2019Nadarzynski et al, , 2020Tran et al, 2019;Yang et al, 2015) and disregard the perspective of healthcare practitioners. To illustrate, Nadarzynski et al (2020) examine patients' acceptance of medical chatbots and Fadhil & Gabrielli (2017) investigate how such chatbots induce behaviour change interventions in patients.…”

Section: Current Research On Ai In Healthcarementioning

confidence: 99%

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

et al. 2021

Artificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology.

Arch Computat Methods Eng

“…An intelligent machine is a whippy noetic agent that perceives its environment and takes action that maximizes its opportunity of success at an arbitrary goal [ 31 ]. In the past decade, much work carried out using the ANN [ 32 , 33 ]. It can relate complex and non-linear problems, which may be complete or incomplete.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.