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

TL;DR

This paper reviews the development of ChatGPT for biomedical question answering, emphasizing the integration of NLP and multimodal data to improve medical diagnosis, treatment, and healthcare support through advanced language models.

Contribution

It provides a comprehensive overview of current methods, tasks, and challenges in applying ChatGPT and multimodal paradigms to biomedical question answering, guiding future research directions.

Findings

Enhanced medical QA through multimodal data integration

Advancements in language models for medical diagnosis

Identification of current challenges and future opportunities

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.