How can LLMs be augmented to adhere to clinical practice guidelines?5 answersLarge language models (LLMs) can be augmented to adhere to clinical practice guidelines by leveraging their advanced natural language generation capabilities to improve compatibility between Electronic Health Records (EHRs) and clinical trial descriptions. One approach is the development of Augmented Interpretable Models (Aug-imodels), which use LLMs during fitting but not during inference, ensuring transparency and efficiency. These models, such as Aug-GAM and Aug-Tree, augment traditional models with knowledge learned by LLMs, leading to interpretable and efficient frameworks that outperform non-augmented counterparts in text-classification tasks. By integrating LLMs in a privacy-aware data augmentation approach like LLM-based patient-trial matching (LLM-PTM), the performance in matching patients with suitable clinical trials can be significantly improved, showcasing the potential of LLMs in enhancing adherence to clinical practice guidelines.
What is the potential of generative AI in revolutionizing medical education?5 answersGenerative Artificial Intelligence (GAI) holds significant potential in revolutionizing medical education by offering realistic simulations, personalized feedback, and immersive learning environments. Integrating GAI models, such as Chat Generative Pre-trained Transformer (ChatGPT), can enhance medical education by providing interactive and adaptive learning experiences. These advancements can help medical students understand anatomical structures better through virtual reality environments with generative AI assistants, ultimately improving educational outcomes. However, challenges like ensuring content quality, addressing biases, and managing ethical concerns need to be addressed through collaboration among educators, researchers, and practitioners. By developing guidelines and transparent AI models, the ethical and responsible use of GAI in medical education can be encouraged, contributing to enhanced learning experiences and ultimately improving patient care.
What are some papers related to applying LLMs to medical diagnostics?5 answersSeveral papers discuss the application of Large Language Models (LLMs) in medical diagnostics. Karabacak and Margetis propose the TEMED-LLM methodology, which leverages LLMs to extract structured tabular data from medical reports, enhancing diagnostic accuracy. They demonstrate superior performance compared to traditional text classification models. Additionally, Yang, Li, and Wei explore the transformative potential of LLMs, like OpenAI ChatGPT, in medical imaging, improving radiologists' interpretive skills and streamlining clinical workflows. These studies highlight the significant impact LLMs can have on enhancing diagnostic processes in the medical field, showcasing the potential for improved patient care and outcomes.
How can search results be used to augment LLM generation?4 answersSearch results can be used to augment LLM generation in two major ways. Firstly, an adaptive search engine assisted learning method can be employed to evaluate the confidence level of LLM's predictions and determine when to refer to the web for more data. This helps avoid useless or noisy augmentation from the web. Secondly, a retrieval-augmented LLM toolkit can be utilized to create a complete pipeline for building customized in-domain LLM-based systems. This toolkit includes modules such as request rewriting, document retrieval, passage extraction, answer generation, and fact checking, which facilitate better interaction between IR systems and LLMs. By incorporating search results, LLMs can access broader, more comprehensive, and constantly updated information from the web, enhancing their capacity for knowledge-intensive tasks.
Are LLMs a subset of generative AI?5 answersYes, LLMs are a subset of generative AI. LLMs, or large language models, are emerging in various fields such as programming, algorithm discovery, and theorem proving. They are considered pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. LLMs have shown promising performance in text annotation tasks, but their performance varies depending on the dataset and annotation task. Additionally, LLMs have demonstrated impressive reasoning and generation abilities in the 3D space, making them valuable for language-guided interactive 3D generation systems. Therefore, LLMs play a significant role in generative AI by utilizing language models to generate and interpret content in various domains.
What are the newest papers for retrieval augmented generation with LLMs?5 answersRetrieval-augmented generation with large language models (LLMs) has been a topic of recent research. One paper by Arabzadeh and Clarke proposes a method to automatically verify generated answers against a corpus, using a combination of the question, generated answer, and retrieved answer. Another paper by Cheng et al. introduces a framework called selfmem, which leverages the model's own output as memory for improved generation. Additionally, Jiang et al. present FLARE, a retrieval-augmented generation method that actively retrieves information throughout the generation process. These papers contribute to the advancement of retrieval-augmented generation with LLMs, addressing challenges such as hallucination and the quality of retrieved memory.