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Driven by the rapid development of artificial intelligence (AI) and anthropomorphic robotic systems, the various possibilities and risks of such technologies have become a topic of urgent discussion. Although science fiction (SF) works are often cited as references for visions of future developments, this framework of discourse may not be appropriate for serious discussions owing to technical inaccuracies resulting from its reliance on entertainment media. However, these science fiction works could help researchers understand how people might react to new AI and robotic systems. Hence, classifying depictions of artificial intelligence in science fiction may be expected to help researchers to communicate more clearly by identifying science fiction elements to which their works may be similar or dissimilar. In this study, we analyzed depictions of artificial intelligence in SF together with expert critics and writers. First, 115 AI systems described in SF were selected based on three criteria, including diversity of intelligence, social aspects, and extension of human intelligence. Nine elements representing their characteristics were analyzed using clustering and principal component analysis. The results suggest the prevalence of four distinctive categories, including human-like characters, intelligent machines, helpers such as vehicles and equipment, and infrastructure, which may be mapped to a two-dimensional space with axes representing intelligence and humanity. This research contributes to the public relations of AI and robotic technologies by analyzing shared imaginative visions of AI in society based on SF works.
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PURPOSE: Self-stigma negatively influences self-esteem, quality of life, self-efficacy, treatment adherence, and recovery in psychiatric patients. By revealing personality traits that influence self-stigma, we can gain useful knowledge for the management of self-stigma. A previous meta-analysis indicated that patients with schizophrenia have higher scores on the Autism-Spectrum Quotient (AQ) than healthy controls. However, the relationship between autistic symptoms and self-stigma in patients with schizophrenia spectrum disorders remains unclear. Therefore, the present study aimed to reveal the association between autistic symptoms and self-stigma in patients with schizophrenia spectrum disorders. PATIENTS AND METHODS: We recruited 127 patients with schizophrenia spectrum disorders (schizophrenia, schizoaffective disorder, and delusional disorder). We assessed participants' self-stigma and autistic symptoms using the Internalized Stigma for Mental Illness (ISMI) scale and the Autism-Spectrum Quotient (AQ), respectively. The differences in the scores of ISMI and AQ according to patient characteristics were investigated. Multiple regression analysis controlling for age and gender was performed to determine the relationship between the total scores on the AQ and IMSI scale. RESULTS: Female patients showed a higher level of self-stigma than males. Unmarried patients showed a significantly higher score on the AQ than married patients. Multiple regression analysis adjusted for age and gender indicated that the total score on AQ might be a predictor of the overall rating on ISMI in patients with schizophrenia spectrum disorders. CONCLUSION: This study is the first to reveal the association between autistic symptoms and self-stigma in patients with schizophrenia spectrum disorders. Our results highlight the importance of considering autistic symptoms in the assessment and management of self-stigma in patients with schizophrenia spectrum disorders.
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BACKGROUND: Despite advances in diagnostic imaging techniques, the differentiation between pancreatic cancer and focal pancreatitis remains difficult. This study evaluated the effectiveness of EUS-guided FNA in the differential diagnosis between pancreatic cancer and focal pancreatitis, with particular reference to detection of the K-ras point mutation. METHODS: The study included 62 consecutive patients with pancreatic ductal cancer and 15 patients with focal pancreatitis demonstrated as a pancreatic mass lesion by EUS. RESULTS: Sensitivity, specificity, overall accuracy, positive predictive value, and negative predictive value of cytopathologic diagnosis were 82%, 100%, 86%, 100%, and 58%, respectively. Sensitivity, specificity, overall accuracy, positive predictive value, and negative predictive value of histopathologic diagnosis were 44%, 100%, 55%, 100%, and 32%, respectively. The K-ras point mutation was found in 74% of pancreatic cancers and 0% of focal pancreatitis lesions. No complication of EUS-guided FNA was observed. CONCLUSIONS: EUS-guided FNA is useful for the differential diagnosis of pancreatic mass lesions caused by pancreatic cancer and focal pancreatitis. Analysis for the K-ras point mutation in specimens obtained by EUS-guided FNA may enhance diagnostic accuracy in indeterminate cases.
