The paper proposes possible use of interactive robots in the remedial practice for children with autism, who have difficulties mainly in interpersonal communication. For this purpose, we built a small creature-like robot, Keepon, which was carefully designed to get autistic and non-autistic children involved in playful interaction. We observed how autistic children (2-4 years old) interacted with Keepon without any experimental setting or instructions in a playroom at a day-care center for children with special needs. From the longitudinal observation for a year and a half (totally, over 500 child-sessions), we found that Keepon's simple appearance and predictable responses gave the autistic children a playful and relaxed mood, in which they spontaneously engaged in dyadic play with Keepon, which would then expand into interpersonal communication where Keepon worked as the pivot of triadic play with adults or other children. Each child showed a different style and a different unfolding of interaction over time, which tell us a "story" of his or her personality and developmental profile, which would not be explained thoroughly by a diagnostic label like "autism".

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Background and Aim It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer‐aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection. Methods The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. Trained classifiers automatically diagnosed HP‐positive and ‐negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post‐eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis. Results Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. Accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system. Conclusions The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post‐eradication patients. By learning more images and considering a diagnosis algorithm for post‐eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians.

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This paper describes the design principle of our robot, Keepon, and reports the longitudinal observation of the interactions between the robot and children with developmental disorders. The robot, Keepon, is a small (12cm tall), simple (like a yellow snowman), soft (made of silicone rubber), creature-like robot, which was designed for studies on human social development and possible remedies for developmental disorders. We observed how children with developmental disorders interacted with the robot in an unconstrained playroom for more than a year (over 500 child-sessions). From these observations, we found that the children changed their ontological understanding of the robot, and consequently their way of interaction, as the interaction unfolded. We conclude that the robot's rather predictable responses gave the children a relaxed mood for spontaneous play, from which social communication with the robot and with another person would naturally emerge.