AI model NYUTron shows remarkable accuracy in clinical tasks using handwritten physician notes Clinical Healthcare AI LanguageModels HealthTech PatientCare HealthData NaturalLanguage HealthInformatics Nature
Study: Health system-scale language models are all-purpose prediction engines. Image Credit: Elnur / Shutterstock
On the other hand, artificial intelligence -based large language models rely on reading and interpreting human language. Thus, the researchers theorized that LLMs could read handwritten physician notes to solve the last-mile problem. In this way, these LLMs could facilitate medical decision-making at the point of care for a broad range of clinical and operational tasks.
In retrospective evaluations, they head-to-head compared six physicians at different levels of seniority against NYUTron. In prospective evaluations running between January and April 2022, the team tested NYUTron in an accelerated format. They loaded it into an inference engine which interfaced with the EHR and read discharge notes duly signed by treating physicians.
Related StoriesReadmission prediction is a well-studied task in the published literature on medical informatics. In its retrospective evaluation, NYUTron performed better than a physician, with a median false positive rate of 11.11% for both NYUTron and the physician. However, the median true positive rate was higher for NYUTron than physicians, 81.72% vs. 50%.
The researchers used 24 NVIDIA A100 GPUs with 40 GB of VRAM for three weeks for pretraining NYUTron, and eight A100 GPUs for six hours per run for fine-tuning. Generally, this amount of computation is inaccessible to researchers. However, the study data demonstrated that high-quality datasets for fine-tuning were more valuable than pretraining. Based on their experimental results, the authors recommended that users use local fine-tuning when their computational ability is limited.
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