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Introduction: Artificial intelligence (AI) is increasingly used in healthcare. AI-based chatbots can act as automated conversational agents, capable of promoting health and providing education at any time. The objective of this study was to develop and evaluate a user-friendly medical chatbot (prostate cancer communication assistant (PROSCA)) for provisioning patient information about early detection of prostate cancer (PC). Methods: The chatbot was developed to provide information on prostate diseases, diagnostic tests for PC detection, stages, and treatment options. Ten men aged 49 to 81 years with suspicion of PC were enrolled in this study. Nine of ten patients used the chatbot during the evaluation period and filled out the questionnaires on usage and usability, perceived benefits, and potential for improvement. Results: The chatbot was straightforward to use, with 78% of users not needing any assistance during usage. In total, 89% of the chatbot users in the study experienced a clear to moderate increase in knowledge about PC through the chatbot. All study participants who tested the chatbot would like to re-use a medical chatbot in the future and support the use of chatbots in the clinical routine. Conclusions: Through the introduction of the chatbot PROSCA, we created and evaluated an innovative evidence-based health information tool in the field of PC, allowing targeted support for doctor-patient communication and offering great potential in raising awareness, patient education, and support. Our study revealed that a medical chatbot in the field of early PC detection is readily accepted and benefits patients as an additional informative tool.
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Two eyes are better than one at detecting a pattern, an advantage termed binocular summation. It is widely believed that binocular summation is mediated by neurons that sum the two eyes' inputs. Here we suggest an alternative model based on a model of binocular interactions proposed by Cohn, Leong & Lasley (Vision Research, 1981, 21, 1017-1023) and further motivated by the efficient coding framework proposed by Li & Atick (Network: Computation in Neural Systems, 1994, 5, 157-174). In the model, termed MAX(S+S-), binocular summation is mediated by channels that compute the sum, S+, and difference, S-, of the two eyes' monocular signals. The S+ and S- signals are assumed to be perturbed by independent noise, have independent gains and contribute independently to detection via the MAX rule. To test the model we measured binocular summation for horizontally-oriented Gabor patches at a range of spatial-frequencies and bandwidths, at both contrast detection threshold and for increment thresholds on binocular pedestals at contrasts set to 10x detection threshold. The model's performance was compared to that of two conventional models of binocular summation, one in which the two eyes' signals remain separate at the decision stage, termed MAX(LR), the other in which the two eye's signals are summed by a single channel, termed B+, with both models incorporating interocular inhibition. The MAX(S+S-) model gave as good a performance as the other two models. Together with the evidence for the involvement of separately gain controlled S+ and S- signals underpinning a wide range of binocular behaviors, we conclude that the MAX(S+S-) model can and should be considered as a viable model for binocular summation.
