Clinical summarization is crucial in healthcare as it distills complex medical data into digestible information, enhancing patient understanding and care management. Large language models (LLMs) have shown significant potential in automating and improving the accuracy of such summarizations due to their advanced natural language understanding capabilities. These models are particularly applicable in the context of summarizing medical/clinical texts, where precise and concise information transfer is essential. In this paper, we investigate the effectiveness of open-source LLMs in extracting key events from discharge reports, including admission reasons, major in-hospital events, and critical follow-up actions. In addition, we also assess the prevalence of various types of hallucinations in the summaries produced by these models. Detecting hallucinations is vital as it directly influences the reliability of the information, potentially affecting patient care and treatment outcomes. We conduct comprehensive simulations to rigorously evaluate the performance of these models, further probing the accuracy and fidelity of the extracted content in clinical summarization. Our results reveal that while the LLMs (e.g., Qwen2.5 and DeepSeek-v2) perform quite well in capturing admission reasons and hospitalization events, they are generally less consistent when it comes to identifying follow-up recommendations, highlighting broader challenges in leveraging LLMs for comprehensive summarization.

The progression to "Pervasive Augmented Reality" envisions easy access to multimodal information continuously. However, in many everyday scenarios, users are occupied physically, cognitively or socially. This may increase the friction to act upon the multimodal information that users encounter in the world. To reduce such friction, future interactive interfaces should intelligently provide quick access to digital actions based on users' context. To explore the range of possible digital actions, we conducted a diary study that required participants to capture and share the media that they intended to perform actions on (e.g., images or audio), along with their desired actions and other contextual information. Using this data, we generated a holistic design space of digital follow-up actions that could be performed in response to different types of multimodal sensory inputs. We then designed OmniActions, a pipeline powered by large language models (LLMs) that processes multimodal sensory inputs and predicts follow-up actions on the target information grounded in the derived design space. Using the empirical data collected in the diary study, we performed quantitative evaluations on three variations of LLM techniques (intent classification, in-context learning and finetuning) and identified the most effective technique for our task. Additionally, as an instantiation of the pipeline, we developed an interactive prototype and reported preliminary user feedback about how people perceive and react to the action predictions and its errors.

With an annual income of €4 bn per year, Manpower France collects 1.3 million receivables from 80,000 companies. To handle this volume, and increasingly complex payment procedures, Manpower's Finance department started using Sidetrade technology in 2013. Sidetrade accelerates automation of the order-to-cash process, and models collection strategies for different segments of clientele. As a result, Manpower France improved their efficiency with a significant reduction in days sales outstanding. Despite this excellent performance, considering the complexity of the purchasing process, and exponential growth in data, Sidetrade decided to enrich their platform with Artificial Intelligence technology.

Manpower France anticipates and drives change in the world of work by breaking new ground with Aimie, Sidetrade's cutting edge Artificial Intelligence system. To optimize Credit Management, Manpower equipped their Finance team with Sidetrade's ground breaking technology, now available to all of Sidetrade's customers. With an annual income of €4 bn per year, Manpower France collects 1.3 million receivables from 80,000 companies. To handle this volume, and increasingly complex payment procedures, Manpower's Finance department started using Sidetrade technology in 2013. Sidetrade accelerates automation of the order-to-cash process, and models collection strategies for different segments of clientele.