This document discusses the Japanese context of Society 5.0. Based on a society-centered approach, Society 5.0 seeks to take advantage of technological advances to finally solve the problems that currently threaten Japan, such as aging, birth rates and lack of competitiveness, among others. Additionally, another objective is to contribute to the progress of the country and develop the foundations for a better world, in which no individual can be excluded from the technological advances of our current society, to achieve this goal, the Sustainable Development Goals (SDG) have been developed. SDGs seek to assess the methods of use of modern technology and thus find the best strategies and tools to use it in a way that guarantees sustainability within the framework of a new society that demands constant renovations.

https://www.mdpi.com/2071-1050/13/12/6567/pdf

Society 5.0: A Japanese Concept for a Superintelligent Society

Because of its multifactorial nature, predicting the presence of cancer using a single biomarker is difficult. We aimed to establish a novel machine-learning model for predicting hepatocellular carcinoma (HCC) using real-world data obtained during clinical practice. To establish a predictive model, we developed a machine-learning framework which developed optimized classifiers and their respective hyperparameter, depending on the nature of the data, using a grid-search method. We applied the current framework to 539 and 1043 patients with and without HCC to develop a predictive model for the diagnosis of HCC. Using the optimal hyperparameter, gradient boosting provided the highest predictive accuracy for the presence of HCC (87.34%) and produced an area under the curve (AUC) of 0.940. Using cut-offs of 200 ng/mL for AFP, 40 mAu/mL for DCP, and 15% for AFP-L3, the accuracies of AFP, DCP, and AFP-L3 for predicting HCC were 70.67% (AUC, 0.766), 74.91% (AUC, 0.644), and 71.05% (AUC, 0.683), respectively. A novel predictive model using a machine-learning approach reduced the misclassification rate by about half compared with a single tumor marker. The framework used in the current study can be applied to various kinds of data, thus potentially become a translational mechanism between academic research and clinical practice.

/pdf/machine-learning-approach-for-the-development-of-a-novel-1efssp7b4h.pdf

Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma

• Big data analytics and artificial intelligence technology based collaborative platform. • Omni-channel healthcare supply chain management. • Data analytics and artificial intelligence for capacity and resource management. • Prior knowledge on BDA-AI is important for better absorptive capacity. The healthcare supply chain involves the manufacturing and delivery of medicines at the right time, at the right place, and in the correct quantity. In the world of uncertainties, especially deadly pandemics, the digitalization of the healthcare supply chain has emerged as one of the urgent phenomena to implement, for which organizations are focussing on the omnichannel healthcare approach. This paper explores (a) the antecedents of big data analytics and artificial intelligence (BDA-AI) technology-based collaborative platform for empowering absorptive capacity in omnichannel health care processes; (b) the effect of BDA-AI collaborative platform powered absorptive capacity in omnichannel health care processes and organization performance. The data is collected using a structured questionnaire from healthcare supply chain executives working in South Africa. The findings indicate that the involvement of managerial factors will improve the capacity of health care organizations to develop a BDA-AI technology-driven collaborative platform to assimilate, transfer and exploit critical information from large data sets. It will capacitate healthcare supply chains to deliver innovative performance to healthcare businesses. This work is the first of its kind to examine big data-based knowledge gained in the context of the omnichannel supply chain. 

Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

/pdf/human-all-too-human-an-all-around-appraisal-of-the-58sb421vry.pdf

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

Artificial intelligence (AI) is becoming increasingly important for the public sector. As the number of studies in this field increases, this study provides a systematic overview of the current lit...

Artificial Intelligence in the Public Sector - a Research Agenda

Artificial intelligence or AI has been heralded as the most transformative technology in healthcare, including critical care medicine. Globally, healthcare specialists and health ministries are being pressured to create and implement a roadmap to incorporate applications of AI into care delivery. To date, the majority of Japan’s approach to AI has been anchored in industry, and the challenges that have occurred therein offer important lessons for nations developing new AI strategies. Notably, the demand for an AI-literate workforce has outpaced training programs and knowledge. This is particularly observable within medicine, where clinicians may be unfamiliar with the technology. National policy and private sector involvement have shown promise in developing both workforce and AI applications in healthcare. In combination with Japan’s unique national healthcare system and aggregable healthcare and socioeconomic data, Japan has a rich opportunity to lead in the field of medical AI.

/pdf/the-advent-of-medical-artificial-intelligence-lessons-from-1jizw9e6m5.pdf

The advent of medical artificial intelligence: lessons from the Japanese approach

Releasing preprints is a popular way to hasten the speed of research but may carry hidden risks for public discourse. The COVID-19 pandemic caused by the novel SARS-CoV-2 infection highlighted the risk of rushing the publication of unvalidated findings, leading to damaging scientific miscommunication in the most extreme scenarios. Several high-profile preprints, later found to be deeply flawed, have indeed exacerbated widespread skepticism about the risks of the COVID-19 disease - at great cost to public health. Here, preprint article quality during the pandemic is examined by distinguishing papers related to COVID-19 from other research studies. Importantly, our analysis also investigated possible factors contributing to manuscript quality by assessing the relationship between preprint quality and gender balance in authorship within each research discipline. Using a comprehensive data set of preprint articles from medRxiv and bioRxiv from January to May 2020, we construct both a new index of manuscript quality including length, readability, and spelling correctness and a measure of gender mix among a manuscripts authors. We find that papers related to COVID-19 are less well-written than unrelated papers, but that this gap is significantly mitigated by teams with better gender balance, even when controlling for variation by research discipline. Beyond contributing to a systematic evaluation of scientific publishing and dissemination, our results have broader implications for gender and representation as the pandemic has led female researchers to bear more responsibility for childcare under lockdown, inducing additional stress and causing disproportionate harm to women in science.

https://medrxiv.org/content/10.1101/2021.06.14.21258917v1.full.pdf

Gender Balance and Readability of COVID-19 Scientific Publishing: A Quantitative Analysis of 90,000 Preprint Manuscripts

Background: The COVID-19 pandemic modified the Residency Match process for fourth-year medical students. In-person away rotations were discouraged, interviews were virtual, and traditional factors used to rank programs were absent. Here, we compare survey results administered to both the 2020 and 2021 Match applicants to assess the influence of the pandemic on the RO Match process. Methods: An Institutional Review Board approved prospective cross-sectional study was conducted. The 2020 and 2021 RO Match applicants at a large RO program were invited to participate. Descriptive summary statistics were assessed. Results: The 2020 and 2021 Matches each had seventy-six applicants complete the survey with response rates of 54% and 57%, respectively. The two groups were predominantly white, cisgender male, single, and without children. While 11% of 2020 applicants did not complete away rotations, 45% of 2021 applicants did not. For 2021 Match applicants, 65% of away rotations were performed virtually while 51% were not for medical school credit. 84% of applicants were satisfied with virtual interviews and 72% felt cost savings were worth not having in-person interviews. While 49% of Match 2020 applicants spent >$5,000 in interview costs, 0% of the Match 2021 applicants did so, with 45% spending <$100. Post-interview communications from programs increased during the pandemic from 36% to 42% in 2020 Match and 2021 Match, respectively. While program culture was the most common factor influencing 2021 Match applicants program rankings, half of applicants did not gain a sense of program culture during virtual interviews. Conclusions: We find 2021 Match applicants completed fewer away rotations, were satisfied with virtual interviews/reduced costs, and did not gain a sense of program culture through virtual rotations/interviews despite it being the most important ranking factor reported. This study supports further exploration of virtual away rotations and virtual interviews moving forward beyond the pandemic.

The Impact of COVID-19 on Radiation Oncology Residency Applicant Away Rotations, Interviews, and Rank Lists: A Comparison Between the 2020 Match and 2021 Match.

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The advent of medical artificial intelligence: lessons from the Japanese approach

Gender Balance and Readability of COVID-19 Scientific Publishing: A Quantitative Analysis of 90,000 Preprint Manuscripts

The Impact of COVID-19 on Radiation Oncology Residency Applicant Away Rotations, Interviews, and Rank Lists: A Comparison Between the 2020 Match and 2021 Match.