AIMC Topic: Large Language Models

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Large language models in nephrology: applications and challenges in chronic kidney disease management.

Renal failure
Large language models (LLMs) represent a transformative advance in artificial intelligence, with growing potential to impact chronic kidney disease (CKD) management. CKD is a complex, highly prevalent condition requiring multifaceted care and substan...

Quality and efficiency of integrating customised large language model-generated summaries versus physician-written summaries: a validation study.

BMJ open
OBJECTIVES: To compare the quality and time efficiency of physician-written summaries with customised large language model (LLM)-generated medical summaries integrated into the electronic health record (EHR) in a non-English clinical environment.

CLAW-MRM: omprehensive ipidomics utomation orkflow for ultiple eaction onitoring Using Large Language Models.

Analytical chemistry
Lipidomic profiling generates vast datasets, making manual annotation and trend interpretation complex and time-intensive. The structural and chemical diversity of the lipidome further complicates the analysis. While existing tools support targeted l...

GATmath and GATLc: Comprehensive benchmarks for evaluating Arabic large language models.

PloS one
The evolution of Large Language Models (LLMs) has significantly advanced artificial intelligence, driving innovation across various applications. Their continued development relies on a deep understanding of their capabilities and limitations. This i...

Comparative evaluation of large language models performance in medical education using urinary system histology assessment.

Scientific reports
Large language models (LLMs) show potential for medical education, but their domain-specific capabilities need systematic evaluation. This study presents a comparative assessment of thirteen LLMs in urinary system histology education. Using a multi-d...

Development and Validation of a Large Language Model-Based System for Medical History-Taking Training: Prospective Multicase Study on Evaluation Stability, Human-AI Consistency, and Transparency.

JMIR medical education
BACKGROUND: History-taking is crucial in medical training. However, current methods often lack consistent feedback and standardized evaluation and have limited access to standardized patient (SP) resources. Artificial intelligence (AI)-powered simula...

Token Probabilities to Mitigate Large Language Models Overconfidence in Answering Medical Questions: Quantitative Study.

Journal of medical Internet research
BACKGROUND: Chatbots have demonstrated promising capabilities in medicine, scoring passing grades for board examinations across various specialties. However, their tendency to express high levels of confidence in their responses, even when incorrect,...

Evaluating large language models as graders of medical short answer questions: a comparative analysis with expert human graders.

Medical education online
The assessment of short-answer questions (SAQs) in medical education is resource-intensive, requiring significant expert time. Large Language Models (LLMs) offer potential for automating this process, but their efficacy in specialized medical educati...

Large language models underperform in European general surgery board examinations: a comparative study with experts and surgical residents.

BMC medical education
BACKGROUND: Artificial intelligence (AI) has become a transformative tool in medical education and assessment. Despite advancements, AI models such as GPT-4o demonstrate variable performance on high-stakes examinations. This study compared the perfor...

Discovery of CRISPR-Cas12a clades using a large language model.

Nature communications
CRISPR-Cas systems revolutionize life science. Metagenomes contain millions of unknown Cas proteins. Traditional mining relies on protein sequence alignments. In this work, we employ an evolutionary scale language model (ESM) to learn the information...