Natural language processing (NLP) systems have long relied heavily on Pretrained Language Models (PLMs) for a variety of tasks, including speech recognition, metaphor processing, sentiment analysis, information extraction, and machine translation. With recent developments, PLMs are changing quickly, and new developments are showing that they can function as stand-alone systems. A major stride in this approach has been made with OpenAI’s development of Large Language Models (LLMs), such as GPT-4, which have shown improved performance in NLP tasks as well as in subjects like biology, chemistry, and medical tests. A new era of possibilities has begun with Google’s Med-PaLM 2, which is specifically designed for the medical sector and has attained “expert” level performance on medical question datasets.
LLMs have the power to revolutionize the healthcare industry by improving the efficacy and efficiency of numerous applications. These models can offer insightful analysis and answers to medical questions since they have a thorough understanding of medical ideas and terminologies. They can help with patient interactions, clinical decision support, and even the interpretation of medical imaging. There are also certain drawbacks to LLMs, including the requirement for substantial amounts of training data and the potential for biases in that data to be propagated.
In a recent research, a team of researchers surveyed about the capabilities of LLMs in healthcare. It is necessary to contrast these two types of language models in order to understand the significant improvement from PLMs to LLMs. Although PLMs are fundamental building blocks, LLMs have a wider range of capabilities that allow them to produce cohesive, context-aware responses in healthcare contexts. A change from discriminative AI approaches, in which models categorize or forecast events, to generative AI approaches, in which models produce language-based answers, may be seen in the switch from PLMs to LLMs. This shift further highlights the shift from model-centered to data-centered approaches.
There are many different models in the LLM world, each suited to a certain specialty. Notable models that have been specially tailored for the healthcare industry include HuatuoGPT, Med-PaLM 2, and Visual Med-Alpaca. HuatuoGPT, for example, asks questions to actively involve patients, whereas Visual Med-Alpaca works with visual experts to do duties like radiological picture interpretation. Because of their multiplicity, LLMs are able to tackle a variety of healthcare-related issues.
The training set, techniques, and optimization strategies used all have a significant impact on how well LLMs perform in healthcare applications. The survey explores the technical elements of creating and optimizing LLMs for use in medical settings. There are practical and ethical issues with the use of LLMs in healthcare settings. It is crucial to guarantee justice, responsibility, openness, and ethics when using LLM. Applications for Healthcare must be free from bias, follow moral guidelines, and give clear justifications for their answers—especially when patient care is involved.
The primary contributions have been summarized by the team as follows.
A transitional path from PLMs to LLMs has been shared, providing updates on new developments.
Focus has been put on assembling training materials, assessment tools, and data resources for LLMs in the healthcare industry and to help medical researchers choose the best LLMs for their individual requirements.
Moral issues, including impartiality, equity, and openness, have been examined.
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Natural language processing (NLP) systems have long relied heavily on Pretrained Language Models (PLMs) for a variety of tasks, including speech recognition, metaphor processing, sentiment analysis, information extraction, and machine translation. With recent developments, PLMs are changing quickly, and new developments are showing that they can function as stand-alone systems. A major stride in this
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