Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.

Artificial intelligence (AI) is rapidly transforming numerous aspects of daily life, including clinical practice and biomedical research. In light of this rapid transformation, and in the context of medical genetics, we assembled a group of leaders in the field to respond to the question about how AI is affecting, and especially how AI will affect, medical genetics. The authors who contributed to this collection of essays intentionally represent different areas of expertise, career stages, and geographies, and include diverse types of clinicians, computer scientists, and researchers.

AtlasGPT: a language model grounded in neurosurgery with domain-specific data and document retrieval.

Objective: Large language models (LLMs) have shown promising performance on medical licensing examinations, but their ability to excel in subspecialty domains and their robustness under adversarial conditions remain unclear. Herein, the authors present AtlasGPT, a subspecialty-focused LLM for neurosurgery, and evaluate its performance on a benchmark multiple-choice question bank and under adversarial testing, as well as its ability to generate high-quality explanations.

Methods: AtlasGPT was built by fine-tuning GPT-4 architecture and retrieval-augmented generation from neurosurgical knowledge sources.

Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models.

We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations.

Is the Readability of Spine-Related Patient Education Material Improving?: An Assessment of Subspecialty Websites.

Study Design: Analysis of spine-related patient education materials (PEMs) from subspecialty websites.

Objective: The aim of this study was to assess the readability of spine-related PEMs and compare to readability data from 2008.

Summary Of Background Data: Many spine patients use the Internet for health information.