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Auflistung Künstliche Intelligenz 38(3) - November 2024 nach Erscheinungsdatum
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- ZeitschriftenartikelBuilding an AI Support Tool for Real-Time Ulcerative Colitis Diagnosis(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Møller, Bjørn Leth; Lo, Bobby Zhao Sheng; Burisch, Johan; Bendtsen, Flemming; Vind, Ida; Ibragimov, Bulat; Igel, ChristianUlcerative Colitis (UC) is a chronic inflammatory bowel disease decreasing life quality through symptoms such as bloody diarrhoea and abdominal pain. Endoscopy is a cornerstone of diagnosis and monitoring of UC. The Mayo endoscopic subscore (MES) index is the standard for measuring UC severity during endoscopic evaluation. However, the MES is subject to high inter-observer variability leading to misdiagnosis and suboptimal treatment. We propose using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability. The system runs real-time in the clinic and augments doctors’ decision-making during the endoscopy. This project report outlines the process of designing, creating and evaluating our system. We describe our initial evaluation, which is a combination of a standard non-clinical model test and a first clinical test of the system on a real patient.
- ZeitschriftenartikelAI in Healthcare and the Public Sector(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Braun, Tanya; Möller, Ralf
- ZeitschriftenartikelAuditive Emotion Recognition for Empathic AI-Assistants(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Duwenbeck, Roswitha; Kirchner, Elsa AndreaThis paper briefly introduces the Project “AudEeKA”, whose aim is to use speech and other bio signals for emotion recognition to improve remote, but also direct, healthcare. This article takes a look at use cases, goals and challenges, of researching and implementing a possible solution. To gain additional insights, the main-goal of the project is divided into multiple sub-goals, namely speech emotion recognition, stress detection and classification and emotion detection from physiological signals. Also, similar projects are considered and project-specific requirements stemming from use-cases introduced. Possible pitfalls and difficulties are outlined, which are mostly associated with datasets. They also emerge out of the requirements, their accompanying restrictions and first analyses in the area of speech emotion recognition, which are shortly presented and discussed. At the same time, first approaches to solutions for every sub-goal, which include the use of continual learning, and finally a draft of the planned architecture for the envisioned system, is presented. This draft presents a possible solution for combining all sub-goals, while reaching the main goal of a multimodal emotion recognition system.
- ZeitschriftenartikelMachine Learning and AI in the Sciences(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Stühmer, Jan
- ZeitschriftenartikelHuman-Centered Explanations: Lessons Learned from Image Classification for Medical and Clinical Decision Making(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Finzel, BettinaTo date, there is no universal explanatory method for making decisions of an AI-based system transparent to human decision makers. This is because, depending on the application domain, data modality, and classification model, the requirements for the expressiveness of explanations vary. Explainees, whether experts or novices (e.g., in medical and clinical diagnosis) or developers, have different information needs. To address the explanation gap, we motivate human-centered explanations and demonstrate the need for combined and expressive approaches based on two image classification use cases: digital pathology and clinical pain detection using facial expressions. Various explanatory approaches that have emerged or been applied in the three-year research project “Transparent Medical Expert Companion” are shortly reviewed and categorized in expressiveness according to their modality and scope. Their suitability for different contexts of explanation is assessed with regard to the explainees’ need for information. The article highlights open challenges and suggests future directions for integrative explanation frameworks.
- ZeitschriftenartikelAutomated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Luttermann, Malte; Baake, Edgar; Bouchagiar, Juljan; Gebel, Benjamin; Grüning, Philipp; Manikwadura, Dilini; Schollemann, Franziska; Teifke, Elisa; Rostalski, Philipp; Möller, RalfFailure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients.
- ZeitschriftenartikelAnalyzing Semantically Enriched Trajectories(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Seep, JanaIn order to understand what influences the movement of an object or person it is important to consider a variety of factors. These could be the visibility of certain landmarks, the current temperature or the presence of a crowded area to be avoided. These insights then can be used to understand movement in the public sector and improve our build environment, e.g. to reduce street traffic accidents or orientation in complex buildings. The following extended abstract is a summary of a doctoral thesis submitted to the University of Münster. The thesis was successfully defended in February 2023 [ 16 ]. The dissertation focuses on the analysis of so-called semantically enriched trajectories , which are used to describe observed movement. It proposes a new model based on an extended finite state machine, which allows for the representation and consideration of the information about the context of the trajectory. With the new model, we consider two main steps in trajectory analysis: First, we aim to infer a semantically enriched representative trajectory for a given cluster of trajectories. Second, we introduce a variation of the well-known k-means algorithm to calculate clusters based on the given context of trajectories. To show semantic feasibility of our approach, we conclude this work by evaluating the possibility to provide decision support for domain experts in two different public sector related contexts.
- ZeitschriftenartikelProject Report: Requirements for a Social Robot as an Information Provider in the Public Sector(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Sievers, Thomas; Russwinkel, NeleIs it possible to integrate a humanoid social robot into the work processes or customer care in an official environment, e.g. in municipal offices? If so, what could such an application scenario look like and what skills would the robot need to have when interacting with human customers? What are requirements for this kind of interactions? We have devised an application scenario for such a case, determined the necessary or desirable capabilities of the robot, developed a corresponding robot application and carried out initial tests and evaluations in a project together with the Kiel City Council. One of the most important insights gained in the project was that a humanoid robot with natural language processing capabilities based on large language models as well as human-like gestures and posture changes (animations) proved to be much more preferred by users compared to standard browser-based solutions on tablets for an information system in the City Council. Furthermore, we propose a connection of the ACT-R cognitive architecture with the robot, where an ACT-R model is used in interaction with the robot application to cognitively process and enhance a dialogue between human and robot.
- ZeitschriftenartikelLifting in Support of Privacy-Preserving Probabilistic Inference(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Gehrke, Marcel; Liebenow, Johannes; Mohammadi, Esfandiar; Braun, TanyaPrivacy-preserving inference aims to avoid revealing identifying information about individuals during inference. Lifted probabilistic inference works with groups of indistinguishable individuals, which has the potential to prevent tracing back a query result to a particular individual in a group. Therefore, we investigate how lifting, by providing anonymity, can help preserve privacy in probabilistic inference. Specifically, we show correspondences between k -anonymity and lifting and present s-symmetry as an analogue as well as PAULI, a privacy-preserving inference algorithm that ensures s-symmetry during query answering.
- ZeitschriftenartikelAI in Healthcare and the Public Sector: How to Face the Challenges of High-Risk Applications and What AI Research Can Get Out of It(KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Braun, Tanya; Möller, RalfApplication projects, may it be in healthcare and the public sector or elsewhere, have the potential to advance foundational (“genuine”) artificial intelligence (AI) research. Unfortunately, insights from specific application projects are rarely propagated back to AI research. This article argues for ways to facilitate such backpropagation and how the contributions in this special issue enable exactly this backpropagation. It also addresses the challenges that come along with high-risk application project, which frequently occur in the area of healthcare and the public sector due to the sensitivity of the subjects.