Ronald Summers, MD, Ph.D.

Locally run large language models (LLMs) may be a feasible option for extracting data from text-based radiology reports while preserving patient privacy, according to a new study from the National Institutes of Health Clinical Center (NIH CC) published in Radiology.

Recently released LLM models such as ChatGPT and GPT-4 have garnered attention. However, they are not compatible with health care data due to privacy constraints.

“ChatGPT and GPT-4 are proprietary models that require the user to send data to OpenAI sources for processing, which would require de-identifying patient data,” said senior author Ronald M. Summers, MD, PhD, senior investigator in the Radiology and Imaging Sciences Department at the NIH. “Removing all patient health information is labor-intensive and infeasible for large sets of reports… Continue reading.

New research showed that drug discovery, infection biology, and diagnostics are functions of AI and machine learning in treating infectious diseases.

A new study described that despite the continued threat of infectious diseases on public health, the capabilities of artificial intelligence (AI) and machine learning (ML) can help handle this issue and provide a framework for future pandemics.

Regardless of research and biological advancements, infectious diseases remain an issue. To keep up with the conflict, common methods that are applied include therapies and diagnostics. Often, synthetic biology approaches provide a platform for innovation. Research indicated that synthetic biology is often divided into two development categories: quantitative biological hypotheses and data from experimentation, and the comprehension of the factors such as nucleic acids and peptides, which allow for the control of biology… Continue reading.

Using a fully-automated artificial intelligence (AI) deep learning model, researchers were able to identify early signs of type 2 diabetes on abdominal CT scans, according to a new study published in the journal Radiology.

Type 2 diabetes affects approximately 13% of all U.S. adults and an additional 34.5% of adults meet the criteria for prediabetes. Due to the slow onset of symptoms, it is important to diagnose the disease in its early stages. Some cases of pre-diabetes can last up to 8 years and an earlier diagnosis will allow patients to make lifestyle changes to alter the progression of the disease… Continue reading.

Researchers at the National Institutes of Health and the University of Wisconsin have demonstrated that using artificial intelligence to analyze CT scans can produce more accurate risk assessment for major cardiovascular events than current, standard methods such as the Framingham risk score (FRS) and body-mass index (BMI).

More than 80 million body CT scans are performed every year in the U.S. alone, but valuable prognostic information on body composition is typically overlooked. In this study, for example, abdominal scans done for routine colorectal cancer screening revealed important information about heart-related risks – when AI was used to analyze the images… Continue reading.

WASHINGTON, D.C.—The American Institute for Medical and Biological Engineering (AIMBE) has announced the induction of Ronald Summers, MD, Ph.D., Senior Investigator, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, to its College of Fellows.

Election to the AIMBE College of Fellows is among the highest professional distinctions accorded to a medical and biological engineer. The College of Fellows is comprised of the top two percent of medical and biological engineers. College membership honors those who have made outstanding contributions to “engineering and medicine research, practice, or education” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of medical and biological engineering, or developing/implementing innovative approaches to bioengineering education.”

Dr. Summers was nominated, reviewed, and elected by peers and members of the College of Fellows for “outstanding contributions to computer-assisted interpretation of radiology images.”