The promise of large language models in health care

Arora, Anmol; Arora, Ananya

doi:10.1016/s0140-6736(23)00216-7

Cited by 81 publications

(46 citation statements)

References 3 publications

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“…The excitement and speculation generated by recent reports and media coverage on the potential of LLMs has led to additional questions posed in the public sphere surrounding their appropriate use. 1-3 One of the primary reasons for the surging public interest is the ability of LLMs to generate text that is on-par, if not better than humans, when prompted with questions. For example, GPT-4, OpenAI's latest LLM, has scored high enough to pass all 3 parts of the US Medical Licensing Examination.…”

Section: Introductionmentioning

confidence: 99%

Large Language Models in Neurology Research and Future Practice

Romano,

Shih,

Paschalidis

et al. 2023

Neurology

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Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. Additionally, we explore use cases for LLMs such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurological disorders.

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Section: Introductionmentioning

confidence: 99%

Large Language Models in Neurology Research and Future Practice

Romano,

Shih,

Paschalidis

et al. 2023

Neurology

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“…With the recent advancements in LLMs, their integration within healthcare has been evaluated in a diverse set of tasks such as summarizing patients' health records, writing discharge summaries, getting assistance for medical research and evaluating clinical scenarios to formulate differential diagnoses [24], [46], [50], [51]. In our perspective, the eligibility of LLMs for deployment in clinical related tasks hinges on their proficiency in two critical dimensions: their capability to serve as a robust and reliable knowledge repository and their capacity to effectively function as intelligent processors of natural language.…”

Section: Discussionmentioning

confidence: 99%

Occurrence of methicillin-resistant staphylococci in Veterinary Teaching Hospital, University of Peradeniya

Manathunga

Dissanayake

2022

Sri Lankan J Infec Dis

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“…Recently, Large Language Models (LLMs) have attracted increasing attention due to their high performance and capability to understand natural language. Consequently, researchers and practitioners are exploring the use of LLMs to assist experts in a variety of domains, including education [21], healthcare [22,23], and content creation [24]. OpenAI GPT-3 [7] series has been a breakthrough for LLMs, which is trained by generating the next word in a sequence given the preceding words.…”

Section: Related Workmentioning

confidence: 99%

Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Tang¹,

Han²,

Jiang³

et al. 2023

Preprint

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Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance in a variety of tasks, such as question answering, essay composition, and code generation. However, their effectiveness in the healthcare sector remains uncertain. In this study, we seek to investigate the potential of LLMs to aid in clinical text mining by examining their ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction. However, our preliminary results indicate that employing LLMs directly for these tasks resulted in poor performance and raised privacy concerns associated with uploading patients' information to the LLM API. To overcome these limitations, we propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data with labels utilizing LLMs and fine-tuning a local model for the downstream task. Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23.37% to 63.99% for the named entity recognition task and from 75.86% to 83.59% for the relation extraction task. Furthermore, generating data using LLMs can significantly reduce the time and effort required for data collection and labeling, as well as mitigate data privacy concerns. In summary, the proposed framework presents a promising solution to enhance the applicability of LLM models to clinical text mining.

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The promise of large language models in health care

Cited by 81 publications

References 3 publications

Large Language Models in Neurology Research and Future Practice

Large Language Models in Neurology Research and Future Practice

Occurrence of methicillin-resistant staphylococci in Veterinary Teaching Hospital, University of Peradeniya

Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

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