AI models in health care are a double-edged sword, with models improving diagnostic decisions for some demographics, but worsening decisions for others when the model has absorbed biased medical data.

Given the very real life and death risks of clinical decision-making, researchers and policymakers are taking steps to ensure AI models are safe, secure and trustworthy - and that their use will lead to improved outcomes.

Physician-investigators at Beth Israel Deaconess Medical Center (BIDMC) compared a chatbot's probabilistic reasoning to that of human clinicians. The findings, published in JAMA Network Open, suggest that artificial intelligence could serve as useful clinical decision support tools for physicians.

Can AI save us from the arduous and time-consuming task of academic research collection? An international team of researchers investigated the credibility and efficiency of generative AI as an information-gathering tool in the medical field.

The research team, led by Professor Masaru Enomoto of the Graduate School of Medicine at Osaka Metropolitan University, fed identical clinical questions and literature selection criteria to two generative AIs; ChatGPT and Elicit.

Artificial intelligence (AI) may attempt to mimic the human brain, but it has yet to fully grasp the complexity of what it means to be human. While it may not truly understand feelings or original creativity, it can help us better understand ourselves - especially our physical bodies in health and in disease, according to a series of articles recently published by the journal Quantitative Biology.

Health care organizations are looking to artificial intelligence (AI) tools to improve patient care, but their translation into clinical settings has been inconsistent, in part because evaluating AI in health care remains challenging. In a new article, researchers propose a framework for using AI that includes practical guidance for applying values and that incorporates not just the tool's properties but the systems surrounding its use.

A team of researchers from LMU, ETH Zurich, and Roche Pharma Research and Early Development (pRED) Basel has used artificial intelligence (AI) to develop an innovative method that predicts the optimal method for synthesizing drug molecules. "This method has the potential to significantly reduce the number of required lab experiments, thereby increasing both the efficiency and sustainability of chemical synthesis,” says David Nippa, lead author of the corresponding paper, which has been published in the journal Nature Chemistry.

Researchers at UMC Utrecht have developed an AI model to predict long-term outcome in extremely premature babies early in life. The model can identify which infants might face intellectual disability as they grow. When further developed, it could offer crucial insights for healthcare providers as well as valuable information for parents about their child’s expected developmental journey.