ERVQA : A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments (2024)

Abstract: The global shortage of healthcare workers has demanded the development of smart healthcare assistants, which can help monitor and alert healthcare workers when necessary. We examine the healthcare knowledge of existing Large Vision Language Models (LVLMs) via the Visual Question Answering (VQA) task in hospital settings through expert annotated open-ended questions. We introduce the Emergency Room Visual Question Answering (ERVQA) dataset, consisting of <image, question, answer> triplets covering diverse emergency room scenarios, a seminal benchmark for LVLMs. By developing a detailed error taxonomy and analyzing answer trends, we reveal the nuanced nature of the task. We benchmark state-of-the-art open-source and closed LVLMs using traditional and adapted VQA metrics: Entailment Score and CLIPScore Confidence. Analyzing errors across models, we infer trends based on properties like decoder type, model size, and in-context examples. Our findings suggest the ERVQA dataset presents a highly complex task, highlighting the need for specialized, domain-specific solutions.

## introduction the global shortage of healthcare personnel is a well-known issue @xcite , especially severe in densely populated areas, and highlighted by crises like the covid-19 pandemic. with the rapid growth of autonomous and remote medical agents @xcite and the increasing popularity of vision language models @xcite , developing smart healthcare assistants is essential. our work explores the question: 'are the existing large vision language models (lvlms) ready to be used in the healthcare environments?', focusing on their use for automated monitoring and alerting, which requires sufficient knowledge of healthcare protocols and medical diagnostic processes. a significant challenge in this research area is the lack of publicly available datasets. while many visual question answering (vqa) datasets exist in radiology and pathology @xcite , realworld datasets featuring patient and hospital environment images are scarce due to data acquisition and publication difficulties. to address this, we introduce the emergency room visual question answering (ervqa) dataset, featuring 4355 expert-annotated question-answer pairs based on visually plausible scenarios in emergency rooms and wards. we select images showing various patientrelated scenarios, and our expert annotators formulate questions that a medical expert would typically consider and act upon. we show an example in figure 1 . using our benchmark, we investigate how current state-of-the-art lvlms (both general purpose and medical domain specific) answer the questions posed in the dataset. we observe that answers to such open-ended questions in a hospital setting should not only be relevant, but also exhibit appropriate clarity and measured caution. a failure to meet this criterion often leads to erroneous generations. hence, to formally assess the performance of the generative models, we also establish a detailed error taxonomy based on common patterns, and manually annotate generated answers with error labels. we study the co-occurrence patterns of these errors and gain useful insights into the nontrivialities of this problem such as the tendencies of the models to double down on erroneous generations, and the close relationships between various errors (such as perception and hallucination) leading to their co-occurrences. keeping with the complexities associated with answering such questions and the vqa task in general, we propose two adapted evaluation metrics: entailment score and clipscore confidence, useful for comparing ground truth and generated answers in this problem domain. in order to understand the inherent capabilities and tendencies of such models in these settings, we perform a benchmark evaluation of the dataset using various open-sourced as well as closed models, in both zero shot, as well as few-shot/in-context settings. further analyzing error occurrences across models using finetuned blip-2 model-based silver labeling, we try to infer performance trends across inherent properties of lvlms, such as decoder type, model size and in context examples. to summarize, our major contributions are: 1) presenting the ervqa dataset for visual question answering in healthcare, focused on emergency rooms and patient wards; 2) establishing an error taxonomy for healthcare question answering and conducting a human-annotated study on stateof-the-art models; 3) benchmarking the ervqa dataset on various state-of-the-art lvlms using standard and proposed metrics, as well as error analysis, providing insights for future research. ## ervqa dataset the emergency room visual question answering (ervqa) dataset consists of 4355 @xmath0,question,@xmath1, containing real world images collected from hospital environments, annotated with hypothetical, visually plausible question-answer pairs. the dataset statistics is provided in ## error analysis answers produced by generative models for questions from the ervqa dataset are open ended, and hence, are prone to various types of errors. these errors can be both visual or textual, leading to incorrect answers for diverse reasons. as quantifying such errors are critical for real-world scenarios, we conduct a thorough error analysis on a set of generated answers from questions from the ervqa dataset. ## benchmark in order to benchmark models against the ervqa dataset, we choose to test on models which offer a wide range of variation in the vision encoder, llm decoder, model size and finetuning strategies. we aim to explore the inherent capabilities of these models to provide error-free meaningful answers which are lexically and semantically close to the ground truth answers, void of the errors defined in section 4, which is essential for their application in the healthcare domain. ## conclusion and future work finally, we revisit our question: "are the existing large vision language models (lvlms) ready to be used in healthcare environments?" the ervqa dataset is proposed as the first step towards addressing this. our extensive error taxonomy and human evaluation reveal the complexity of generating accurate answers due to dense occurrence and co-occurrence patterns. despite adapting semantically relevant vqa-specific evaluation metrics, improvement across these metrics doesn't necessarily reduce errors. the best-performing model, gemini vision pro, still has an average error rate of 33% across all error classes, which is significant given the sensitive domain. therefore, we conclude that existing lvlms are not yet ready for healthcare environments. our dataset is a valuable resource for evaluating and improving models and techniques in this field. we hope that the future work would focus on building more errorsensitive lvlms, developing effective error mitigation strategies, and exploring video datasets for further advancement.