New BiomedGPT AI model expected to transform medical and research practices

A picture may be worth a thousand words, but still… they both have a lot of work to do to catch up with BiomedGPT.

Recently covered in the prestigious magazine Natural medicineBiomedGPT is a new type of artificial intelligence (AI) designed to support a wide range of medical and scientific tasks. This new study, carried out in collaboration with several institutions, is described in the article as “the first open source and lightweight basic vision-language model, designed as a generalist capable of performing diverse biomedical tasks.”

This work combines two types of AI into a decision support tool for medical providers. One side of the system is trained to understand biomedical images, and the other side is trained to understand and evaluate biomedical text. The combination of these allows the model to address a wide range of biomedical challenges, using information derived from biomedical imaging databases and the analysis and synthesis of scientific and medical research reports.

Lichao Sun, assistant professor of computer science and engineering at Lehigh University and lead author of the study

“16 cutting-edge results” for doctors and patients

The key innovation described in the August 7 Natural medicine The article titled “A basic general vision-language model for various biomedical tasks” is that this AI model does not need to be specialized for each task. Typically, AI systems are trained for specific tasks, such as recognizing tumors on X-rays or summarizing medical articles. However, this new model can handle many different tasks using the same underlying technology. This versatility makes it a “generalist” model and a powerful new tool in the hands of medical providers.

“BiomedGPT is based on base models, a recent development in AI,” Sun explains. “Base models are large, pre-trained AI systems that can be adapted to various tasks with minimal additional training. The general model described in the article was trained on large amounts of biomedical data, including images and text, allowing it to perform well. in different applications.”

“By evaluating 25 datasets across 9 biomedical tasks and different modalities,” says Kai Zhang, a doctoral student at Lehigh advised by Sun and first author of the study. Nature article, “BiomedGPT achieved 16 cutting-edge results. One out of three human evaluations of BiomedGPT radiology the tasks showcased the strong predictive capabilities of the model.

Zhang says he’s proud that the open source code base is available for other researchers to use as a springboard to drive further development and adoption.

The team reports that the technology behind BiomedGPT could one day help doctors by interpreting complex medical images, help researchers by analyzing scientific literature, or even contribute to drug discovery by predicting the behavior of molecules.

“The potential impact of such technology is significant,” says Zhang, “because it could streamline many aspects of healthcare and research, making them faster and more accurate. Our method demonstrates that effective training with various data can lead to more practical biomedical AI for improvement diagnosis and workflow efficiency.”

A team effort for clinical validation, and much more

A crucial step in the process was validating the effectiveness and applicability of the model in real-world healthcare settings.

“Clinical testing involves applying the AI ​​model to real patient data to evaluate its accuracy, reliability and safety,” Sun explains. “These tests ensure that the model performs well in different scenarios. The results of these tests helped refine the model, demonstrating its potential to improve clinical decision-making and patient care.

Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham Health System and a teaching affiliate at Harvard Medical School, played a crucial role in the development and validation of the BiomedGPT model. The institution’s involvement has primarily focused on providing clinical expertise and facilitating the evaluation of the model’s effectiveness in real-world healthcare settings. For example, the model was tested with MGH radiologists, where it demonstrated superior performance in tasks such as visually answering questions and generating radiology reports. This collaboration ensured that the model was both accurate and practical for clinical use.

Other contributors to BiomedGPT include researchers from the University of Georgia, Samsung Research America, University of Pennsylvania, Stanford University, University of Central Florida, UC-Santa Cruz , University of Texas-Health, Children’s Hospital of Philadelphia and Mayo Clinic.

“This research is highly interdisciplinary and collaborative,” says Sun. “The research involves expertise in several fields, including computer science, medicine, radiology and biomedical engineering. Each author brings the specialized knowledge necessary to develop, test and validate the model in various biomedical tasks. Large-scale projects like this often require access to diverse datasets and computational resources, as well as access to skills in algorithm development, model training, evaluation, and application to real-world scenarios, as well as ‘in clinical testing and validation.

“It was a real team effort,” he says. “Creating something that can truly help the medical community improve patient outcomes across a wide range of issues is a very complex challenge. With such complexity, collaboration is essential to creating impact through the application of science and engineering. »