In an in-depth study, scientists found that a new artificial intelligence (AI) computer program can generate doctors' notes with such precision that two physicians could not tell the difference. This indicates AI may soon provide healthcare workers with groundbreaking efficiencies when it comes to providing their work notes.
Across the globe, artificial intelligence has emerged as one of the most popular topics with tools like the DALL E 2, ChatGPT, as well as other solutions that are assisting users in various ways.
A new study has found that a new automated tool for creating doctor's notes can be so reliable that two doctors were unable to distinguish between the two versions, thus opening the door for Al to provide breakthrough efficiencies to healthcare personnel.
An evaluation of the proof-of-concept study conducted by the authors involved doctors examining patient notes that were authored by real medical professionals as well as by the new Al system. There was a 49% accuracy rate for determining the author of the article only 49% of the time. There have been 19 research studies conducted by a group of University of Florida and NVIDIA researchers, who trained supercomputers to create medical records using a new model known as GatorTronGPT, which works similarly to ChatGPT.
There are more than 430,000 downloads of the free versions of GatorTron models from Hugging Face, an open-source AI website that provides free AI models to the public. Based on Yonghui Wu's post from the Department of Health Outcomes and Biomedical Informatics at the University of Florida, GatorTron models are the only models on the site that can be used for clinical research, said lead author.
Among more than 430,000 people who have downloaded the free version of GatorTron models from the Hugging Face website, there has been an increase of more than 20,000 since it went live.
There is no doubt that these GatorTron models are the only ones on the site that would be suitable for clinical research, according to lead author Yonghui Wu of the University of Florida's Department of health outcomes and Biomedical Informatics.
According to the study, published in the journal npj Digital Medicine, a comprehensive language model was developed to enable computers to mimic natural human language using the database.
Adapting these models to handle medical records offers additional challenges, such as safeguarding the privacy of patients as well as the requirement for highly technical precision, as compared to how they handle conventional writing or conversation. Using a search engine such as Google or a platform such as Wikipedia these days makes it impossible for users to access medical records within the digital domain.
Researchers at the University of Pittsburgh utilized a cohort of two million patients' medical records, which contained 82 billion relevant medical terms that provided the dataset necessary to overcome these challenges. They also trained the GatorTronGPT model using an additional collection of 195 billion words to make use of GPT-3 architecture, a variant of neural network architecture, to analyze medical data by using GPT-3 architecture, based on a dataset combined with 195 billion words.
Consequently, GatorTronGPT was able to produce clinical text that resembled doctors' notes as part of its capability to create clinical text.
A medical GPT has many potential uses, but among those is the option of replacing the tedious process of documenting with a process of capturing and transcribing notes by AI instead.
As a result of billions upon billions of words of clinical vocabulary and language usage accumulated over weeks, it is not surprising that AI has reached the point where it is similar to human writing.
The GatorTronGPT model is the result of recent technological advances in AI, which have demonstrated that they have considerable potential for producing doctors' notes that appear almost indistinguishable from those created by professionals who have a high level of training.
There is substantial potential for enhancing the efficiency of healthcare documentation due to the development of this technology, which was described in a study published in the NPJ Digital Medicine journal.
Developed through a successful collaboration between the prestigious University of Florida and NVIDIA, this groundbreaking automated tool signifies a pivotal step towards revolutionizing the way medical note-taking is conducted.
The widespread adoption and utilization of the highly advanced GatorTron models, especially in the realm of clinical research, further emphasizes the practicality and strong demand for such remarkable innovations within the medical field.
Despite the existence of certain challenges, including privacy considerations and the requirement for utmost technical precision, this remarkable research showcases the remarkable adaptability of advanced language models when it comes to effectively managing and organizing complex medical records. This significant achievement offers a promising glimpse into a future where AI seamlessly integrates into various healthcare systems, thereby providing a highly efficient and remarkably accurate alternative to the traditional and often labour-intensive documentation processes.
Consequently, this remarkable development represents a significant milestone in the realm of medical technology, effectively paving the way for improved workflows, enhanced efficiency, and elevated standards of patient care, which are all paramount in the ever-evolving healthcare landscape.
