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Background: Changes in the speech and language of patients with Alzheimer's disease (AD) have been reported. Using machine learning to characterize these irregularities may contribute to the early, non-invasive diagnosis of AD. Methods: We conducted cognitive function assessments, including the Mini-Mental State Examination, with 83 patients with AD and 75 healthy elderly participants, and recorded pre- and post-assessment conversations to evaluate participants' speech. We analyzed the characteristics of the spectrum, intensity, fundamental frequency, and minute temporal variation (∆) of the intensity and fundamental frequency of the speech and compared them between patients with AD and healthy participants. Additionally, we evaluated the performance of the speech features that differed between the two groups as single explanatory variables. Results: We found significant differences in almost all elements of the speech spectrum between the two groups. Regarding the intensity, we found significant differences in all the factors except for the standard deviation between the two groups. In the performance evaluation, the areas under the curve revealed by logistic regression analysis were higher for the center of gravity (0.908 ± 0.036), mean skewness (0.904 ± 0.023), kurtosis (0.932 ± 0.023), and standard deviation (0.977 ± 0.012) of the spectra. Conclusions: This study used machine learning to reveal speech features of patients diagnosed with AD in comparison with healthy elderly people. Significant differences were found between the two groups in all components of the spectrum, paving the way for early non-invasive diagnosis of AD in the future. [ABSTRACT FROM AUTHOR]

Background: According to the National Clinical Database of Japan, the incidence of bile duct injury (BDI) during laparoscopic cholecystectomy has hovered around 0.4% for the last 10 years and has not declined. On the other hand, it has been found that about 60% of BDI occurrences are due to misidentifying anatomical landmarks. However, the authors developed an artificial intelligence (AI) system that gave intraoperative data to recognize the extrahepatic bile duct (EHBD), cystic duct (CD), inferior border of liver S4 (S4), and Rouviere sulcus (RS). The purpose of this research was to evaluate how the AI system affects landmark identification. Methods: We prepared a 20-s intraoperative video before the serosal incision of Calot's triangle dissection and created a short video with landmarks overwritten by AI. The landmarks were defined as landmark (LM)-EHBD, LM-CD, LM-RS, and LM-S4. Four beginners and four experts were recruited as subjects. After viewing a 20-s intraoperative video, subjects annotated the LM-EHBD and LM-CD. Then, a short video is shown with the AI overwriting landmark instructions; if there is a change in each perspective, the annotation is changed. The subjects answered a three-point scale questionnaire to clarify whether the AI teaching data advanced their confidence in verifying the LM-RS and LM-S4. Four external evaluation committee members investigated the clinical importance. Results: In 43 of 160 (26.9%) images, the subjects transformed their annotations. Annotation changes were primarily observed in the gallbladder line of the LM-EHBD and LM-CD, and 70% of these shifts were considered safer changes. The AI-based teaching data encouraged both beginners and experts to affirm the LM-RS and LM-S4. Conclusion: The AI system provided significant awareness to beginners and experts and prompted them to identify anatomical landmarks linked to reducing BDI. [ABSTRACT FROM AUTHOR]

Objective: The medication-overuse headache (MOH) prevalence has not been investigated in a general Japanese population. We performed questionnaire-based survey and revealed MOH prevalence and its characteristics. We also performed clustering to obtain insight for MOH subgrouping. Methods: In this cross-sectional study, the 15–64-year-old population was investigated in Itoigawa during their COVID-19 vaccination under the national policy. MOH was defined as ≥ 15 days/month plus self-report of use of pain medications ≥ 10 or 15 days/month in the last 3 months. Ward method and k-means + + were used to perform clustering MOH patients. Results: Among 5865 valid responses, MOH prevalence was 2.32%. MOH was common among females and the middle-aged. Combination-analgesic is the most overused as 50%. MOH had aggravation by routine physical activity, moderate or severe pain, and migraine-like, compared to non-MOH. The 136 MOH patients could be grouped into 3 clusters. Age and frequency of acute medication use were essential factors for clustering. Conclusions: This is the first study of MOH prevalence in Japan. Most MOH characteristics were similar to previous reports worldwide. Public awareness of proper headache treatment knowledge is still needed. Clustering results may be important for subtype grouping from a social perspective apart from existing clinical subtypes. [ABSTRACT FROM AUTHOR]

We have examined the attitude and moral perception of 228 college students (63 Japanese and 165 non-Japanese) towards artificial intelligence (AI) in an international university in Japan. The students were asked to select a single most significant ethical issue associated with AI in the future from a list of nine ethical issues suggested by the World Economic Forum, and to explain why they believed that their chosen issues were most important. The majority of students (n = 149, 65%) chose unemployment as the major ethical issue related to AI. The second largest group of students (n = 29, 13%) were concerned with ethical issues related to emotional AI, including the impact of AI on human behavior and emotion. The paper discusses the results in detail and concludes that, while policymakers must consider how to ameliorate the impact of AI on employment, AI engineers need to consider the emotional aspects of AI in research and development, as well. [ABSTRACT FROM AUTHOR]

Purpose: To assess the performance of GPT-4 Turbo with Vision (GPT-4TV), OpenAI's latest multimodal large language model, by comparing its ability to process both text and image inputs with that of the text-only GPT-4 Turbo (GPT-4 T) in the context of the Japan Diagnostic Radiology Board Examination (JDRBE)., Materials and Methods: The dataset comprised questions from JDRBE 2021 and 2023. A total of six board-certified diagnostic radiologists discussed the questions and provided ground-truth answers by consulting relevant literature as necessary. The following questions were excluded: those lacking associated images, those with no unanimous agreement on answers, and those including images rejected by the OpenAI application programming interface. The inputs for GPT-4TV included both text and images, whereas those for GPT-4 T were entirely text. Both models were deployed on the dataset, and their performance was compared using McNemar's exact test. The radiological credibility of the responses was assessed by two diagnostic radiologists through the assignment of legitimacy scores on a five-point Likert scale. These scores were subsequently used to compare model performance using Wilcoxon's signed-rank test., Results: The dataset comprised 139 questions. GPT-4TV correctly answered 62 questions (45%), whereas GPT-4 T correctly answered 57 questions (41%). A statistical analysis found no significant performance difference between the two models (P = 0.44). The GPT-4TV responses received significantly lower legitimacy scores from both radiologists than the GPT-4 T responses., Conclusion: No significant enhancement in accuracy was observed when using GPT-4TV with image input compared with that of using text-only GPT-4 T for JDRBE questions., (© 2024. The Author(s).)

To evaluate the moral awareness of college students regarding artificial intelligence (AI) systems, we have examined 467 surveys collected from 152 Japanese and 315 non-Japanese students in an international university in Japan. The students were asked to choose a most significant moral problem of AI applications in the future from a list of ten ethical issues and to write an essay about it. The results show that most of the students (n = 269, 58%) considered unemployment to be the major ethical issue related to AI. The second largest group of students (n = 54, 12%) was concerned with ethical issues related to emotional AI, including the impact of AI on human behavior and emotion and robots' rights and emotions. A relatively small number of students referred to the risk of social control by AI (6%), AI discrimination (6%), increasing inequality (5%), loss of privacy (4%), AI mistakes (3%), malicious AI (3%), and AI security breaches (3%). Calculation of the z score for two population proportions shows that Japanese students were much less concerned about AI control of society (− 3.1276, p < 0.01) than non-Japanese students, but more concerned about discrimination (2.2757, p < 0.05). Female students were less concerned about unemployment (− 2.6108, p < 0.01) than males, but more concerned about discrimination (2.4333, p < 0.05). The study concludes that the moral awareness of college students regarding AI technologies is quite limited and recommends including the ethics of AI in the curriculum. [ABSTRACT FROM AUTHOR]

In 1982, Japan launched its Fifth Generation Computer Systems project (FGCS), designed to develop intelligent software that would run on novel computer hardware. As the first national, large-scale artificial intelligence (AI) research and development (R&D) project to be free from military influence and corporate profit motives, the FGCS was open, international, and oriented around public goods. Although the FGCS did not plan any commercialized technologies, many American computer experts portrayed it as an economic threat to U.S. dominance in computing and the global economy--and policymakers around the developed world believed them and funded AI projects of their own. Later, however, the FGCS was remembered as a failure. Why? This article recasts the FGCS as an interstice in the shift from a state-funded regime of American science organization to the neoliberal privatized regime of R&D now ascendant around the world. By exploring how notions of economic competitiveness and national security shaped R&D, this article reveals AI to be a product of contingent choices by multiple actors--nation-states, government bureaucracies, corporations, and individuals--rather than the outcome of deterministic technological forces. [ABSTRACT FROM AUTHOR]

Despite having one of the most advanced healthcare systems in the world, Japan is expected to experience a shortage of nearly half a million healthcare workers by 2025 due to its rapidly aging population. In response, government authorities plan to implement a wide range of AI-driven healthcare solutions. These include care robots that assist the physically handicapped or elderly, chatbots that provide anonymous online mental health consultation, and diagnostic software utilizing machine learning. Yet one of the most popular smart technologies to augment the nation's already overstrained and undermanned healthcare system is a little known but emerging emotional AI technologies, i.e., deep learning systems trained to read, classify, and respond to human emotions. These technologies are being sold on a commercial level not only to the public but also to rehabilitation centers, local hospitals, and senior citizen residences. Although the augmentation of healthcare services to intelligent machines may seem like a logical step in a country well-known for its long-standing affection toward robots, Japanese society is also known for its adherence to established social relations and traditional institutional practices, especially, in the realm of medical care. In order to gauge Japanese acceptance of emotion-sensing technology, we analyze a dataset of 245 visitors to clinics and hospitals in a typical suburban area in Japan using multiple linear regression. The results show that in general, senior and male patients perceive the emotional AI technology more negatively. For behavioral variables, patients' level of familiarity has positive correlations with attitudes toward emotional AI-based applications in private setting (β Familiarity_AttitudePri =0.346, p<0.001) and public setting (β Familiarity_PublicAttitude =0.297, p<0.001); while concern for losing control to AI has negative correlations with the attitudes' variables: private setting (β LosingControl_AttitudePri =−0.262,p=0.002) and public setting (β LosingControl_AttitudePub =-0.188, p=0.044). Interestingly, concerns over violation of privacy and discrimination are non-significant correlates, which contradict the emerging literature on this subject. We further contextualize the findings with insights afforded by an understanding of Japanese culture as well as the relevant literature on care robots in Japan. Finally, policy and education implications to promote emotional AI acceptance to the general and senior members of the society are provided. • Attitude toward emotional AI use in Japanese healthcare positively correlates with familiarity with the technology. • Fear of losing control to AI has significant negative correlation to the perception of EAI in both private and public setting. • Privacy and discriminatory concerns are non-significant correlates of the attitude toward emotional AI use in healthcare. • The results necessitate the cultural framing of technological acceptance behaviors. [ABSTRACT FROM AUTHOR]

• WTP in Japan for an auto-driving system is estimated insufficient in car markets. • A social dilemma will occur: people may not buy AVs that they consider moral. • Credibility regarding autonomous vehicles plays a key role in the social dilemma. Autonomous driving technologies are advancing rapidly, and determining when consumers ride in driverless vehicles on a daily basis is becoming essential. Using choice experiments, first, we elicit consumers' willingness to pay (WTP) for autonomous driving systems in Japan along with their WTP for hybrid and electric engines. We found in this study that the Japanese consumers' WTP is, on average, lower than the estimates for the US market and is not sufficient to enable autonomous vehicles (AVs) to capture a meaningful share of the existing car market. Second, compared with a previous study in the US, we propose and discuss an expected social dilemma caused by the ethical problems that AVs will face, known as the Trolley Problems. We find that social dilemma may occur because people tend not want to buy "moral" AVs. Third, we explored the determinants of the WTP and social dilemma to find that the credibility of AVs is determined to be a critical factor for the social dilemma. [ABSTRACT FROM AUTHOR]

Background: To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women., Methods: The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions., Results: The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11; P < 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively., Conclusions: Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.