Artificial Intelligence vs. Natural Stupidity: Evaluating AI Readiness for the Vietnamese Medical Information System

Abstract

This review paper presents a framework to evaluate the artificial intelligence (AI) readiness for the healthcare sector in developing countries: a combination of adequate technical or technological expertise, financial sustainability, and socio-political commitment embedded in a healthy psycho-cultural context could bring about the smooth transitioning toward an AI-powered healthcare sector. Taking the Vietnamese healthcare sector as a case study, this paper attempts to clarify the negative and positive influencers. With only about 1500 publications about AI from 1998 to 2017 according to the latest Elsevier AI report, Vietnamese physicians are still capable of applying the state-of-the-art AI techniques in their research. However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field. The AI readiness in Vietnam’s healthcare also suffers from the unprepared information infrastructure—using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health, the paper found that the frequency of the word “database” actually decreases from 2012 to 2016, and the word has a high probability to accompany words such as “lacking”, “standardizing”, “inefficient”, and “inaccurate.” Finally, manifestations of psycho-cultural elements such as the public’s mistaken views on AI or the non-transparent, inflexible and redundant of Vietnamese organizational structures can impede the transition to an AI-powered healthcare sector.

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

“…As a result, the diagnostic performance varies and is limited. With the development of digital technology, image-based diagnosis techniques have been widely used to help doctors investigate problems with organs that are underneath the skin and/or deep inside the human body [1][2][3][4][5][6][7][8][9][10][11]. For example, doctors have used X-ray imaging to capture lung and/or bone images that can help to indicate whether a disease/injury exists in these organs [9,10].…”

“…However, the use of captured images is still dependent on personal knowledge and experiences of doctors. To overcome this problem, Computer-Aided Diagnosis systems (CAD) have been developed to assist doctors in the diagnosis and treatment processes [1][2][3][4][5][6][7][8][9][10]. As indicated by its name, the CAD systems can serve as an additional expert in the double screening process that aims to enhance the human diagnostic performance based on a computer program [11].…”

“…With the development of technology, such as the back propagation algorithm, neural network, and Graphics Processing Units (GPUs), the deep learning-based technique has recently been applied to solve many problems in medical image processing systems [1,2,10,12,21,36]. For the thyroid nodule detection/classification problem, the deep learning-based method has gained a lot of success.…”

AI-readiness for circular economy_Prospects and challenges

“…One can compare this problem to the unexpectedly slow rate of AI adoption in the health-care sector. As pointed out in many review articles about AI-powered medicine, the application of AI suffers not only from the lack of a coherence and consistent data infrastructure, but also, political, social, and cultural barriers (Vuong, et al, 2019). Vuong et al, (2019), in studying AI-readiness for the medical sector in Vietnam, have proposed adequate technical expertise, financial sustainability, and socio-political commitment are necessary for a full-blown AI-powered healthcare sector.…”

“…As pointed out in many review articles about AI-powered medicine, the application of AI suffers not only from the lack of a coherence and consistent data infrastructure, but also, political, social, and cultural barriers (Vuong, et al, 2019). Vuong et al, (2019), in studying AI-readiness for the medical sector in Vietnam, have proposed adequate technical expertise, financial sustainability, and socio-political commitment are necessary for a full-blown AI-powered healthcare sector. As one would expect that peculiarities in a psycho-cultural setting can greatly influence major decisions of individuals and organizations (Vuong, et al, 2018), the authors show the psycho-cultural factors such as the public's incorrect views on AI or the inflexible structure of organizations and institutions can hinder the prospects of a fully AI-powered medical sector.…”