Benchmarking Large Language Models' Clinical Skills Using an AI-Structured Clinical Examination (AI-SCE) Framework
The core message of this article is to introduce MedQA-CS, a comprehensive AI-SCE framework for evaluating large language models' (LLMs) clinical skills, which goes beyond traditional multiple-choice question benchmarks by assessing LLMs' ability to follow complex clinical instructions and interact with simulated patients.
Oncologists Express Ethical Concerns About Integrating AI into Cancer Treatment Decisions
Oncologists have significant ethical concerns about the use of AI in cancer care, including issues with bias, transparency, and responsibility for AI-driven treatment recommendations.
Evaluating Hallucination Risks in Medical Visual Question Answering Models
The core message of this paper is to create a benchmark dataset for evaluating the hallucination phenomenon in state-of-the-art medical visual question answering (Med-VQA) models, and to provide a comprehensive analysis of their performance on this benchmark.
Artificial Intelligence Techniques for Efficient COVID-19 Detection Using Blood Tests and Radiographic Images
This study demonstrates the potential of integrating artificial intelligence methods, including machine learning, neural networks, and ensemble techniques, to accurately detect COVID-19 using blood test parameters and radiographic images.