Large language models reshaping molecular biology and drug development.

The utilization of large language models (LLMs) has become a significant advancement in the domains of medicine and clinical informatics, providing a revolutionary potential for scientific breakthroughs and customized therapies. LLM models are trained on large datasets and exhibit the capacity to comprehend and analyze intricate biological data, encompassing genomic sequences, protein structures, and clinical health records. With the utilization of their comprehension of the language of biology, they possess the ability to reveal concealed patterns and insights that may evade human researchers.

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology.

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm's performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve.

Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care.

Purpose: This article explores the potential of large language models (LLMs) to automate administrative tasks in healthcare, alleviating the burden on clinicians caused by electronic medical records.

Potential: LLMs offer opportunities in clinical documentation, prior authorization, patient education, and access to care. They can personalize patient scheduling, improve documentation accuracy, streamline insurance prior authorization, increase patient engagement, and address barriers to healthcare access.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines.

Understanding Biases and Disparities in Radiology AI Datasets: A Review.

Artificial intelligence (AI) continues to show great potential in disease detection and diagnosis on medical imaging with increasingly high accuracy. An important component of AI model creation is dataset development for training, validation, and testing. Diverse and high-quality datasets are critical to ensure robust and unbiased AI models that maintain validity, especially in traditionally underserved populations globally.

Turing test-inspired method for analysis of biases prevalent in artificial intelligence-based medical imaging.

Due to the growing need to provide better global healthcare, computer-based and robotic healthcare equipment that depend on artificial intelligence has seen an increase in development. In order to evaluate artificial intelligence (AI) in computer technology, the Turing test was created. For evaluating the future generation of medical diagnostics and medical robots, it remains an essential qualitative instrument.