Research

With Huge Patient Dataset, AI Accurately Predicts Treatment Outcomes

Scientists have designed a new artificial intelligence (AI) model that emulates randomized clinical trials at determining the treatment options most effective at preventing stroke in people with heart disease.

The model was front-loaded with de-identified data on millions of patients gleaned from health care claims information submitted by employers, health plans and hospitals - a foundation model strategy similar to that of generative AI tools like ChatGPT.

GPT-4, Google Gemini Fall Short in Breast Imaging Classification

Use of publicly available large language models (LLMs) resulted in changes in breast imaging reports classification that could have a negative effect on patient management, according to a new international study published today in the journal Radiology, a journal of the Radiological Society of North America (RSNA). The study findings underscore the need to regulate these LLMs in scenarios that require high-level medical reasoning, researchers said.

ChatGPT fails at heart risk assessment

Despite ChatGPT's reported ability to pass medical exams, new research indicates it would be unwise to rely on it for some health assessments, such as whether a patient with chest pain needs to be hospitalized.

In a study involving thousands of simulated cases of patients with chest pain, ChatGPT provided inconsistent conclusions, returning different heart risk assessment levels for the exact same patient data.

Study Shows ChatGPT Failed when Challenging ESCMID Guideline for Treating Brain Abscesses

With artificial intelligence (AI) poised to become a fundamental part of clinical research and decision making, many still question the accuracy of ChatGPT, a sophisticated AI language model, to support complex diagnostic and treatment processes.

Now a new study, being presented at this year's ESCMID Global Congress (formerly ECCMID) in Barcelona, Spain (27-30 April), which pitted ChatGPT against the ESCMID guideline for the management of brain abscesses,

A Shortcut for Drug Discovery

For most human proteins, there are no small molecules known to bind them chemically (so called "ligands"). Ligands frequently represent important starting points for drug development but this knowledge gap critically hampers the development of novel medicines. Researchers at CeMM, in a collaboration with Pfizer, have now leveraged and scaled a method to measure the binding activity of hundreds of small molecules against thousands of human proteins.

Cleveland Clinic Study Finds AI can Develop Treatments to Prevent "Superbugs"

Cleveland Clinic researchers developed an artficial intelligence (AI) model that can determine the best combination and timeline to use when prescribing drugs to treat a bacterial infection, based solely on how quickly the bacteria grow given certain perturbations. A team led by Jacob Scott, MD, PhD, and his lab in the Theory Division of Translational Hematology and Oncology, recently published their findings in PNAS.

New AI-Technology Estimates Brain Age Using Low-Cost EEG Device

As people age, their brains do, too. But if a brain ages prematurely, there is potential for age-related diseases such as mild-cognitive impairment, dementia, or Parkinson's disease. If "brain age" could be easily calculated, then premature brain aging could be addressed before serious health problems occur.

Researchers from Drexel University’s Creativity Research Lab developed an artificial intelligence (AI) technique that can effectively estimate an individual’s brain age based on electroencephalogram (EEG) brain scans.

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