Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering

• URL: http://arxiv.org/abs/2401.07510v3
• Date: Sat, 20 Jan 2024 22:08:18 GMT
• Title: Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering
• Authors: Qing Li, Lei Li, Yu Li
• Abstract summary: ChatGPT explores a strategic blueprint of question answering (QA) in delivering medical diagnosis, treatment recommendations, and other healthcare support. This is achieved through the increasing incorporation of medical domain data via natural language processing (NLP) and multimodal paradigms.
• Score: 25.569980942498347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: ChatGPT explores a strategic blueprint of question answering (QA) in delivering medical diagnosis, treatment recommendations, and other healthcare support. This is achieved through the increasing incorporation of medical domain data via natural language processing (NLP) and multimodal paradigms. By transitioning the distribution of text, images, videos, and other modalities from the general domain to the medical domain, these techniques have expedited the progress of medical domain question answering (MDQA). They bridge the gap between human natural language and sophisticated medical domain knowledge or expert manual annotations, handling large-scale, diverse, unbalanced, or even unlabeled data analysis scenarios in medical contexts. Central to our focus is the utilizing of language models and multimodal paradigms for medical question answering, aiming to guide the research community in selecting appropriate mechanisms for their specific medical research requirements. Specialized tasks such as unimodal-related question answering, reading comprehension, reasoning, diagnosis, relation extraction, probability modeling, and others, as well as multimodal-related tasks like vision question answering, image caption, cross-modal retrieval, report summarization, and generation, are discussed in detail. Each section delves into the intricate specifics of the respective method under consideration. This paper highlights the structures and advancements of medical domain explorations against general domain methods, emphasizing their applications across different tasks and datasets. It also outlines current challenges and opportunities for future medical domain research, paving the way for continued innovation and application in this rapidly evolving field.

Related papers

• MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations [23.437292621092823]
  We introduce MediTOD, a dataset of doctor-patient dialogues in English for the medical history-taking task. We devise a questionnaire-based labeling scheme tailored to the medical domain. Then, medical professionals create the dataset with high-quality comprehensive annotations.
  arXiv  Detail & Related papers  (2024-10-18T06:38:22Z)
• Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
  We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data [41.8344712915454]
  Automatic radiology report generation can alleviate the workload for physicians and minimize regional disparities in medical resources. It is a challenging task, as the computational model needs to mimic physicians to obtain information from multi-modal input data. Recent works emerged to address this issue using deep learning-based methods, such as transformers, contrastive learning, and knowledge-base construction. This survey summarizes the key techniques developed in the most recent works and proposes a general workflow for deep learning-based report generation.
  arXiv  Detail & Related papers  (2024-05-21T14:37:35Z)
• COGNET-MD, an evaluation framework and dataset for Large Language Model benchmarks in the medical domain [1.6752458252726457]
  Large Language Models (LLMs) constitute a breakthrough state-of-the-art Artificial Intelligence (AI) technology. We outline Cognitive Network Evaluation Toolkit for Medical Domains (COGNET-MD) We propose a scoring-framework with increased difficulty to assess the ability of LLMs in interpreting medical text.
  arXiv  Detail & Related papers  (2024-05-17T16:31:56Z)
• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• Contributions to the Improvement of Question Answering Systems in the Biomedical Domain [0.951828574518325]
  This thesis work falls within the framework of question answering (QA) in the biomedical domain. We propose four contributions to improve the performance of QA in the biomedical domain. We develop a fully automated semantic biomedical QA system called SemBioNLQA.
  arXiv  Detail & Related papers  (2023-07-25T16:31:20Z)
• A scoping review on multimodal deep learning in biomedical images and texts [29.10320016193946]
  Multimodal deep learning has the potential to revolutionize the analysis and interpretation of biomedical data. This study reviewed the current uses of multimodal deep learning on five tasks.
  arXiv  Detail & Related papers  (2023-07-14T14:08:54Z)
• 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)
• PMC-LLaMA: Towards Building Open-source Language Models for Medicine [62.39105735933138]
  Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. LLMs struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. We describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
  arXiv  Detail & Related papers  (2023-04-27T18:29:05Z)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)
• MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation [86.38736781043109]
  We build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG. We propose two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset.
  arXiv  Detail & Related papers  (2020-10-15T03:34:33Z)

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.