Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health

By Shubo Tian, Qiao Jin, Lana Yeganova, Po-Ting Lai, Qingqing Zhu, Xiuying Chen, Yifan Yang, Qingyu Chen, Won Kim, Donald C. Comeau and 4 more. Briefings Bioinform., 2023

408 citations recorded by the source corpus.

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction, and medical education, and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/aff0fe00ed7892d1185e8c5b66d318d3892abe6e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiao Jin
2. Guangzhi Xiong
3. Zhiyong Lu
4. Aidong Zhang
5. Wei Peng
6. Zabir Al Nazi
7. Zhuosheng Zhang
8. Yilun Zhao

Citations in the corpus

• Benchmarking Retrieval-Augmented Generation for Medicine (2024, background): “These issues can be especially dangerous in high-stakes domains such as healthcare (Tian et al., 2024; Hersh, 2024), and will affect the overall performance of LLMs on domain-specific QA tasks.”
• MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (2023, background): “Such failures in medical question-answering originate from a lack of domain knowledge (Harris, 2023; Kung et al., 2023; Tian et al., 2024) instead of reasoning rationale.”
• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, neutral): “Initially, Pretrained Language Models (PLMs) include BERT [2] and RoBERTa [3] were developed for general NLP tasks and later adapted for healthcare applications.”
• Large language models in healthcare and medical domain: A review (2023, background): “The consequences of relying on hallucinatory information in healthcare contexts can be profoundly detrimental, potentially leading to harmful or even catastrophic outcomes [133].”