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To examine whether comfort with the use of ChatGPT in society differs from comfort with other uses of AI in society and to identify whether this comfort and other patient characteristics such as trust, privacy concerns, respect, and tech-savviness are associated with expected benefit of the use of ChatGPT for improving health.

To assess the performance of large language models (LLMs) for zero-shot disambiguation of acronyms in clinical narratives.

This study evaluates ChatGPT's symptom-checking accuracy across a broad range of diseases using the Mayo Clinic Symptom Checker patient service as a benchmark.

Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process?

The objective of this study is to systematically examine the efficacy of both proprietary (GPT-3.5, GPT-4) and open-source large language models (LLMs) (LLAMA 7B, 13B, 70B) in the context of matching patients to clinical trials in healthcare.

To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD.

 The method of documentation during a clinical encounter may affect the patient-physician relationship.

The integration of large language models (LLMs) like ChatGPT into medical education presents potential benefits and challenges. These technologies, aligned with constructivist learning theories, could potentially enhance critical thinking and problem-solving through inquiry-based learning environments. However, the actual impact on educational outcomes and the effectiveness of these tools in fostering learning require further empirical study. This technological shift necessitates a reevaluation of curriculum design and the development of new assessment [...]

To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data.