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Over a billion people worldwide have livers with excess fat, which can lead to a host of medical problems. Researchers think AI tools can spot the condition--and help stop it--early enough to save lives. All over the world, a slow, insidious change is taking place in the composition of the livers of more than a billion people. While the presence of fat in a normal, healthy liver is negligible, many adults and even children have livers where fat exceeds 5 percent or even 10 percent of the organ's total weight. Its unnatural presence causes inflammation, cell damage, and the formation of scar tissue known as fibrosis, all hallmarks of fatty liver disease, a condition that now impacts approximately 30 percent of adults worldwide .

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This is health care's Uber moment. Every knowledge-based profession may one day reach the point when AI outperforms the human experts. In medicine, that day appeared to come in April. A group of primarily Harvard and Stanford researchers announced the results of a study that pitted ChatGPT against hundreds of physicians in a diagnostic obstacle course involving written medical mysteries and information from real-world patients. The bot had won, and the humans weren't entirely happy about it.

Sample efficiencyof ESRL is independent of the chosen risk aversion threshold and quality of the behavior policy.

Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side

High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained

By pre-training the model on domain-specific data, PubMED BERT is expected to have a better understanding of biomedical concepts, terminology, and language patterns compared to general domain models like BERT -base and BERT -large [ 95 ]. The main advantage of using PubMED BERT for biomedical text mining tasks is its domain-specific knowledge, which can lead to improved performance and more accurate results when fine-tuned on various downstream tasks, such as named entity recognition, relation extraction, document classification, and question answering. Since PubMED BERT is pre-trained on a large corpus of biomedical text, it is better suited to capturing the unique language patterns, complex terminology, and the relationships between entities in the biomedical domain.