This survey reviews deep learning methods for cross-subject EEG decoding, addressing the challenge of high inter-subject variability and domain shift. It categorizes current literature into methodological families like feature alignment and contrastive learning, emphasizing rigorous evaluation and theoretical considerations.
A large-scale study re-evaluates Retrieval-Augmented Generation (RAG) in medical question answering, finding only small and inconsistent improvements over no-retrieval baselines. It suggests that the choice of the backbone model is more critical than retrieval methods, and the main bottleneck is the model's ability to effectively use retrieved evidence.
This paper introduces Artificial Special Intelligence, a new concept enabling error-free training of Machine Learning models for classification, preventing repeated mistakes. The method was successfully applied to 18 MedMNIST biomedical datasets, achieving perfection in most cases despite some double-labeling issues.
BioELX is a two-stage cross-lingual biomedical entity linking framework that does not require task-specific annotated training corpora. It improves candidate retrieval using multilingual aliases from Wikidata and performs context-aware disambiguation with a pre-trained LLM ranker.
This research explores the use of lightweight Large Language Models (LLMs) for Biomedical Named Entity Recognition, demonstrating their competitive performance against larger models. The study highlights their potential as resource-efficient alternatives and identifies specific output formats that consistently improve performance.
This research evaluates large language models (LLMs) in biomedical question answering, specifically addressing their reliability when faced with conflicting or incomplete evidence. It reveals that LLM accuracy significantly drops, and predictions flip, when the order of correct and contradictory documents is reversed, highlighting issues with order effects and the need for conflict-aware abstention.
This protocol evaluates ChatGPT's ability to generate and verify disease-centric biomedical associations, using biomedical ontologies and literature. It employs a self-consistency strategy and a RAG-enabled workflow with open-source LLMs to address exact-match limitations and detect hallucination.
This article, originally from rachel.fast.ai, follows an AI researcher exploring how artificial intelligence can analyze microscope slides. It details her journey back to school for immunology, applying AI to scientific and medical imaging.
