The Ronald Reagan Presidential Library in Simi Valley, California, has opened "Dead Sea Scrolls: The Exhibition," featuring a collection of ancient Jewish manuscripts along with 200 other artifacts. The Dead Sea Scrolls may be even older than researchers thought, according to a new study driven by an artificial intelligence (AI) model. A group of researchers from the Netherlands, Italy and Denmark recently published their findings in the journal PLOS One on June 4. The researchers said they developed an AI-based date-prediction model named Enoch, a nod to the biblical patriarch of the same name. Enoch "was trained as a machine learning-based date-prediction model applying Bayesian ridge regression on established handwriting-style descriptors," according to the study.

Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We introduce MedQA-CS, an AI-SCE framework inspired by medical education's Objective Structured Clinical Examinations (OSCEs), to address this gap. MedQA-CS evaluates LLMs through two instruction-following tasks, LLM-as-medical-student and LLM-as-CS-examiner, designed to reflect real clinical scenarios. Our contributions include developing MedQA-CS, a comprehensive evaluation framework with publicly available data and expert annotations, and providing the quantitative and qualitative assessment of LLMs as reliable judges in CS evaluation. Our experiments show that MedQA-CS is a more challenging benchmark for evaluating clinical skills than traditional multiple-choice QA benchmarks (e.g., MedQA). Combined with existing benchmarks, MedQA-CS enables a more comprehensive evaluation of LLMs' clinical capabilities for both open- and closed-source LLMs.