Overview of BioASQ 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

By A. Nentidis, Anastasia Krithara, K. Bougiatiotis, Martin Krallinger, C. R. Penagos, Marta Villegas, G. Paliouras. 2021

53 citations recorded by the source corpus.

Advancing the state-of-the-art in large-scale biomedical semantic indexing and question answering is the main focus of the BioASQ challenge. BioASQ organizes respective tasks where different teams develop systems that are evaluated on the same benchmark datasets that represent the real information needs of experts in the biomedical domain. This paper presents an overview of the ninth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2021. In this year, a new question answering task, named Synergy, is introduced to support researchers studying the COVID-19 disease and measure the ability of the participating teams to discern information while the problem is still developing. In total, 42 teams with more than 170 systems were registered to participate in the four tasks of the challenge. The evaluation results, similarly to previous years, show a performance gain against the baselines which indicates the continuous improvement of the state-of-the-art in this field. © 2021, Springer Nature Switzerland AG.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Genetics

Peter Szolovits

In Molecular Biology

Citations in the corpus

A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “Furthermore, on BioASQ [208] and USMLE [205], Galactica’s performance was close to the state-of-the-art results achieved by fine-tuned models (94.8% and 44.6”
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams (2020, methodology): “BioASQ (Nentidis et al. 2020) is similar to the SQuAQ dataset (Rajpurkar et al. 2016), where a span of text in the given context is used as the answer and thus no external knowledge source is needed. emrQA (Pampari et al. 2018) aims to rank a list of Electronic Medical Record (EMR) text lines to…”