Cognitive assistance may be valuable in applications that reduce costs and improve quality in healthcare systems. Use cases and scenarios include persuasion, i.e., the design, development and evaluation of interactive technologies aimed at changing users' attitudes or behaviours through persuasion, but not through coercion or deception. We motivate persuasion for healthcare systems and propose solutions from an artificial intelligence (AI) perspective for conceptual design and system implementation. The goal is to develop an IoT (Internet-Of-Things) toolbox towards AI-based persuasive technologies for healthcare systems.

With advancements in technology, smartphones can already serve as memory aids. Electronic calendars are of great use in time-based memory tasks. In this project, we enter the mixed reality realm for helping dementia patients. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Memory loss is an example. Here, mixed reality refers to the merging of real and virtual worlds to produce new episodic memory visualisations where physical and digital objects co-exist and interact in real-time. Cognitive models are approximations of a patient's mental abilities and limitations involving conscious mental activities (such as thinking, understanding, learning, and remembering). External representations of episodic memory help patients and caregivers coordinate their actions with one another. We advocate distributed cognition, which involves the coordination between individuals, artefacts and the environment, in four main implementations of artificial intelligence technology in the Kognit storyboard: (1) speech dialogue and episodic memory retrieval; (2) monitoring medication management and tracking an elder's behaviour (e.g., drinking water); (3) eye tracking and modelling cognitive abilities; and (4) serious game development towards active memory training. We discuss the storyboard, use cases and usage scenarios, and some implementation details of cognitive models and mixed reality hardware for the patient. The purpose of future studies is to determine the extent to which cognitive enhancement technology can be used to decrease caregiver burden.

Future radiology practices assume that the radiology reports should be uniform, comprehensive, and easily managed. This means that reports must be "readable" to humans and machines alike. In order to improve reporting practices in breast imaging, we allow the radiologist to write structured reports with a special pen on paper with an invisible dot pattern. In this way, we provide a knowledge acquisition system for printed mammography patient forms for the combined work with printed and digital documents. In this domain, printed documents cannot be easily replaced by computer systems because they contain free-form sketches and textual annotations, and the acceptance of traditional PC reporting tools is rather low among the doctors. This is due to the fact that current electronic reporting systems significantly add to the amount of time it takes to complete the reports. We describe our real-time digital paper application and focus on the use case study of our deployed application. We think that our results motivate the design and implementation of intuitive pen based user interfaces for the medical reporting process and similar knowledge work domains. Our system imposes only minimal overhead on traditional form-filling processes and provides for a direct, ontology-based structuring of the user input for semantic search and retrieval applications, as well as other applied artificial intelligence scenarios which involve manual form-based data acquisition.

Over the last several years, the market for speech technology has seen significant developments (Pieraccini and Huerta We learned some lessons which we use as guidelines 2005) and powerful commercial off-the-shelf solutions for in the development of multimodal dialogue systems where speech recognition (ASR) or speech synthesis (TTS). Further users can combine speech and gestures when using multiple application scenarios, more diverse and dynamic information interaction devices. In earlier projects (Wahlster 2003; Reithinger sources, and more complex prototype systems need et al. 2005) we integrated different sub-components to be addressed in the context of QA. Dialogue-based QA allows to multimodal interaction systems. Other lessons served as a user to pose questions in natural speech, followed by guidelines in the development of semantic dialogue systems answers presented in a concise form (Sonntag et al. 2007).

This paper examines first ideas on the applicability of Linked Data, in particular a subset of the Linked Open Drug Data (LODD), to connect radiology, human anatomy, and drug information for improved medical image annotation and subsequent search. One outcome of our ontology engineering methodology is the alignment between radiology-related OWL ontologies (FMA and RadLex). These can be used to provide new connections in the medicine-related linked data cloud. A use case scenario is provided that demonstrates the benefits of the approach by enabling the radiologist to query and explore related data, e.g., medical images and drugs. The diagnosis is on a special type of cancer (lymphoma).