AI for Biomedicine in the Era of Large Language Models

TL;DR

This paper surveys how large language models can be applied to biomedical data types like text, sequences, and signals, highlighting their potential and challenges in advancing biomedical research.

Contribution

It provides a comprehensive overview of applying large language models to various biomedical data, identifying key challenges and future directions.

Findings

Large language models can process biomedical text, sequences, and signals effectively.

Challenges include trustworthiness, personalization, and multi-modal data integration.

Potential to accelerate biomedical discoveries through LLM applications.

Abstract

The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and further extending to societal predictions like infectious disease outbreaks. Recent advancements in large language models, exemplified by models like ChatGPT, have showcased significant prowess in natural language tasks, such as translating languages, constructing chatbots, and answering questions. When we consider biomedical data, we observe a resemblance to natural language in terms of sequences: biomedical literature and health records presented as text, biological sequences or sequencing data arranged in sequences, or sensor data like brain signals as time series. The question arises: Can we harness the potential of recent large language models to drive biomedical knowledge discoveries? In this survey, we will explore the application of large language models to three crucial categories of biomedical data: 1) textual data, 2) biological sequences, and 3) brain signals. Furthermore, we will delve into large language model challenges in biomedical research, including ensuring trustworthiness, achieving personalization, and adapting to multi-modal data representation