Artificial Intelligence (ai) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between mds and ai enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human–ai collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an ai support system. Endoscopists were influenced by ai ($$\textsc {or}=3.05$$ O R = 3.05 ), but not erratically: they followed the ai advice more when it was correct ($$\textsc {or}=3.48$$ O R = 3.48 ) than incorrect ($$\textsc {or}=1.85$$ O R = 1.85 ). Endoscopists achieved this outcome through a weighted integration of their and the ai opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human–ai hybrid team to outperform both agents taken alone. We discuss the features of the human–ai interaction that determined this favorable outcome.

Abstract. Patients with uterine leiomyosarcoma (LMS)typicallyUterine mesenchymal tumours have been traditionally divided into benign leiomyomas (LMA) and malignant leiomyosarcomas (LMS), based on cytological atypia, mitotic activity, and other criteria. Globally, uterine LMSs, which are some of the most common neoplasms in the female genital tract, are relatively rare mesenchymal tumours, having an estimated annual incidence of approximately one per 160,000 women (1). They account for approximately one-third of uterine sarcomas and 1.3% of all uterine malignancies, and are considered aggressive malignancies, with a 5-year survival rate of only 50% for patients with tumours confined to the uterus (2, 3). It is noteworthy that when adjusting for 4997 This article is freely accessible online.

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The formation of Schardinger ß‐dextrin from starch by an alkalophilic Bacillus sp. (ATCC 21783) has been studied. Factors affecting the yield of cyclodextrins include the type, concentration, and dispersion of the starch; the time of the enzymolysis, the amount of enzyme used. A method for the preparation of Schardinger ß‐dextrin on a industrial scale without using precipitants was devised. The final yield of ß‐cyclodextrin was 3.6 kg from 15 kg of potato starch.