Natural language processing (NLP) systems have long relied on pre-trained language models (PLM) for a variety of tasks, including speech recognition, metaphor processing, sentiment analysis, information extraction, and automatic translation. With recent advancements, PLMs are changing rapidly and new developments show that they can function as standalone systems. Great progress has been made in this approach with the development of large language models (LLMs) by OpenAI, such as GPT-4, which have shown improved performance on NLP tasks, as well as in subjects such as biology, chemistry and medical tests. A new era of possibilities has begun with Google’s Med-PaLM 2, which is designed specifically for the medical sector and has achieved “expert” level performance on medical question datasets.
LLMs have the power to revolutionize the healthcare industry by improving the effectiveness and efficiency of numerous applications. These models can offer in-depth analysis and answers to medical questions as they have in-depth knowledge of medical ideas and terminologies. They can help with patient interactions, clinical decision support, and even medical image interpretation. LLMs also have certain drawbacks, including the requirement for substantial amounts of training data and the potential for biases to propagate in that data.
In recent research, a team of researchers surveyed the capabilities of LLMs in healthcare. It is necessary to contrast these two types of linguistic models to understand the significant improvement from PLMs to LLMs. Although PLMs are critical components, LLMs have a broader range of capabilities that enable them to produce cohesive and context-aware responses in healthcare contexts. In the shift from PLM to LLM can be seen a shift from discriminative ai approaches, in which models categorize or predict events, to generative ai approaches, in which models produce language-based responses. This shift further highlights the move from model-centric to data-centric approaches.
There are many different models in the LLM world, each suitable for a certain specialty. Notable models that have been designed especially for the healthcare industry include HuatuoGPT, Med-PaLM 2, and Visual Med-Alpaca. HuatuoGPT, for example, asks questions to actively engage patients, while Visual Med-Alpaca works with visual experts to perform tasks such as interpreting radiological images. Because of their multiplicity, LLMs can address a variety of healthcare-related issues.
The training set, techniques and optimization strategies used have a significant impact on the performance of LLMs 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 LLM in healthcare settings. It is essential to ensure fairness, accountability, openness and ethics when using LLM. Requests for medical care should be free of bias, follow moral guidelines, and give clear justifications for your responses, especially when it comes to patient care.
The main contributions have been summarized by the team as follows.
- A transition path from PLM to LLM has been shared, providing updates on new developments.
- The focus has been on bringing together training materials, assessment tools, and data resources for LLMs in the healthcare industry and on helping medical researchers choose the best LLMs for their individual needs.
- Moral issues have been examined, including impartiality, fairness and openness.
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