BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks

• URL: http://arxiv.org/abs/2305.17100v4
• Date: Sun, 11 Aug 2024 20:03:12 GMT
• Title: BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
• Authors: Kai Zhang, Rong Zhou, Eashan Adhikarla, Zhiling Yan, Yixin Liu, Jun Yu, Zhengliang Liu, Xun Chen, Brian D. Davison, Hui Ren, Jing Huang, Chen Chen, Yuyin Zhou, Sunyang Fu, Wei Liu, Tianming Liu, Xiang Li, Yong Chen, Lifang He, James Zou, Quanzheng Li, Hongfang Liu, Lichao Sun,
• Abstract summary: Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
• Score: 68.39821375903591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners, and patients. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation, and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency.

Related papers

• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments [112.25067497985447]
  We introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions. BioDiscoveryAgent can uniquely design new experiments without the need to train a machine learning model. It achieves an average of 21% improvement in predicting relevant genetic perturbations across six datasets.
  arXiv  Detail & Related papers  (2024-05-27T19:57:17Z)
• Does Biomedical Training Lead to Better Medical Performance? [2.3814275542331385]
  Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. This study investigates the effect of biomedical training in the context of six practical medical tasks evaluating $25$ models.
  arXiv  Detail & Related papers  (2024-04-05T12:51:37Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• Towards Generalist Biomedical AI [28.68106423175678]
  We introduce Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system. Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data. We conduct a radiologist evaluation of model-generated (and human) chest X-ray reports and observe encouraging performance across model scales.
  arXiv  Detail & Related papers  (2023-07-26T17:52:22Z)
• Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking [4.8882241537236455]
  This research investigates a method that exploits the in-context learning capabilities of large models for biomedical concept linking. The proposed approach adopts a two-stage retrieve-and-rank framework. It achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7% in chemical entity normalization.
  arXiv  Detail & Related papers  (2023-07-03T16:19:50Z)
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day [85.19963303642427]
  We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics. This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
  arXiv  Detail & Related papers  (2023-06-01T16:50:07Z)
• BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs [48.376109878173956]
  We present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets. PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles. Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing.
  arXiv  Detail & Related papers  (2023-03-02T02:20:04Z)
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining [140.61707108174247]
  We propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA.
  arXiv  Detail & Related papers  (2022-10-19T07:17:39Z)
• BIOS: An Algorithmically Generated Biomedical Knowledge Graph [4.030892610300306]
  We introduce the Biomedical Informatics Ontology System (BIOS), the first large scale publicly available BioMedKG that is fully generated by machine learning algorithms. BIOS contains 4.1 million concepts, 7.4 million terms in two languages, and 7.3 million relation triplets. Results suggest that machine learning-based BioMedKG development is a totally viable solution for replacing traditional expert curation.
  arXiv  Detail & Related papers  (2022-03-18T14:09:22Z)
• BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition [9.05154470433578]
  Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models. We propose biomedical ALBERT, an effective domain-specific language model trained on large-scale biomedical corpora.
  arXiv  Detail & Related papers  (2020-09-19T12:58:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.