Can Large Language Models Efficiently Assist Physicians?
The Role of Large Language Models in Physician Support: A Critical Evaluation
Introduction
On the one hand, large language models have the potential to provide clinicians with quick and easy access to a vast amount of medical information, including research studies, clinical guidelines, and electronic health records. This can help clinicians make more informed decisions, improve patient outcomes, and save time.
On the other hand, large language models are not perfect and may still contain inaccuracies or biases that could potentially impact clinical decision-making. Moreover, the information provided by large language models may not always be tailored to the specific needs of individual clinicians or patients, and clinicians may still need to use their judgment and expertise to interpret and apply the information provided.
Some Examples
- Natural Language Understanding:
LLMs can understand and generate human-like text, making them valuable for transcribing medical notes, summarizing patient histories, and generating reports. This can save physicians time on administrative tasks, allowing them to focus more on patient care. - Graph Neural Network
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