A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics

By Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria. Information Fusion, 2023

339 citations recorded by the source corpus.

The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern due to their ability to effectively respond to freetext queries with certain professional knowledge. This survey outlines the capabilities of the currently developed LLMs for Healthcare and explicates their development process, with the aim of providing an overview of the development roadmap from traditional Pretrained Language Models (PLMs) to LLMs. Specifically, we first explore the potential of LLMs to enhance the efficiency and effectiveness of various Healthcare applications highlighting both the strengths and limitations. Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, as well as comparing various LLMs with each other. Then we summarize related Healthcare training data, training methods, optimization strategies, and usage. Finally, the unique concerns associated with deploying LLMs in Healthcare settings are investigated, particularly regarding fairness, accountability, transparency and ethics. Our survey provide a comprehensive investigation from perspectives of both computer science and Healthcare specialty. Besides the discussion about Healthcare concerns, we supports the computer science community by compiling a collection of open source resources, such as accessible datasets, the latest methodologies, code implementations, and evaluation benchmarks in the Github. Summarily, we contend that a significant paradigm shift is underway, transitioning from PLMs to LLMs. This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to data-centered methodologies. Also, we determine that the biggest obstacle of using LLMs in Healthcare are fairness, accountability, transparency and ethics.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/c7492913370b5726eaa6ced163a60de6c9d4bb7f

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. Mickael Rouvier
2. Wei Peng
3. Zabir Al Nazi

Citations in the corpus

• A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, neutral): “In addition, cognitive psychology has been studied for a long time, so many effective human memory theories and architectures have been accumulated, which can support more advanced capabilities of the agents [147].”
• BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains (2024, background): “However, integrating LLMs into healthcare and medicine presents unique challenges and opportunities (He et al., 2023; Zhou et al., 2024).”
• Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook (2024, neutral): “One study showed that CNNs, when trained on publicly available chest x-ray data sets, may show a tendency to underdiagnose specific populations, such as individuals from marginalized communities (eg, Black and Hispanic patients), women, and Medicaid recipients [181].”
• Large language models in healthcare and medical domain: A review (2023, background): “This transformative potential has ignited considerable enthusiasm within the healthcare community [5, 6, 7].”
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (2025, neutral): “Efficient reasoning LLMs would improve healthcare [36] by helping doctors and researchers work with large amounts of medical data more easily.”