Left: Examples from HealthVer [1]; Right: Example of a claim that is supported and refuted by different evidence [2] With the rapid growth of biomedical research and the concurrent rise in misinformation, ensuring the accuracy of claims about treatment effectiveness is increasingly critical. Inaccurate or misleading information can have profound Read more…
Example from n2c2 of relation extraction [1] This work explores the effectiveness of employing Clinical BERT for Relation Extraction (RE) tasks in medical texts within an Active Learning (AL) framework. Our main objective is to optimize RE in medical texts through AL while examining the trade-offs between performance and computation Read more…
Clinical Named Entity Recognition (NER) is essential for extracting important medical insights from clinical narratives. Given the challenges in obtaining expert training datasets for real-world clinical applications related to data protection regulations and the lack of standardised entity types, this work represents a collaborative initiative aimed at building a German Read more…
The biodiversity crisis is maybe the most pressing issue of our time, which makes measuring the biodiversity an important precursor for improvement. Biodiversity assessment is limited by data management and analysis tools. Prior work has approached this problem by exploring the space of tools that can aide workflows of biodiversity Read more…
Ophthalmo-AI Age-related macular degeneration (AMD) is the primary cause of severe vision loss in the western world. One cause is the retinal accumulation of harmful byproducts of metabolism processes occurring in the eye. Treatment aims at slowing down disease progression and is performed through the injection of anti-VEGF agents into Read more…
Medical AI is about to become more powerful and versatile thanks to the fast creation of reusable AI models that can learn from different types of data. We call this new approach a foundation model for medical AI (FMAI). FMAI models will be able to do many different tasks with Read more…
Detecting animal sounds is an essential aspect of scientific research and conservation efforts. It provides valuable insights into biodiversity, species behavior, and ecosystem health. Monitoring of terrestrial and marine wildlife is a way to decrease biodiversity loss and improve species conservation. To achieve this goal passive acoustic monitoring (PAM) is Read more…
Passive acoustic monitoring (PAM), the recording of sounds using microphones (e.g. in biosphere reserves), is an increasingly popular method for continuous, reproducible, scalable and cost-effective monitoring of wildlife [Sugai et al., 2018]. It is widely employed in various fields, including ecology, marine biology, and conservation, to study animal behavior, biodiversity, Read more…
Figure 1. An overview of the transfer learning framework with BERT-SNER. Information extraction from clinical text has the potential for clinical research and personalized care, but annotating large data for customized requirements is prohibitive. We present a German medical Named Entity Recognition (NER) system (Liang et al., 2023) that transfers Read more…
Explainable AI (XAI) has demonstrated its potential in deciphering discriminatory features in machine learning (ML) decision-making processes. Specifically, XAI’s feature attribution methods shed light on individual decisions made by ML models. However, despite their visual appeal, these attributions can be unfaithful. To ensure the faithfulness of feature attributions, it is Read more…