AIMC Topic: Large Language Models

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Multiple Large Language Models' Performance on the Chinese Medical Licensing Examination: Quantitative Comparative Study.

JMIR human factors
BACKGROUND: ChatGPT excels in natural language tasks, but its performance in the Chinese National Medical Licensing Examination (NMLE) and Chinese medical education remains underexplored. Meanwhile, Chinese corpus-based large language models (LLMs) s...

Large Language Model-Based Patient Simulation to Foster Communication Skills in Health Care Professionals: User-Centered Development and Usability Study.

JMIR medical education
BACKGROUND: Case-based learning using standardized patients is a key method for teaching communication skills in medicine. Besides logistical and financial hurdles, standardized patients portrayed by actors cannot cover the complete diversity of soci...

Enhancing clinicians' trust in large language models via transparent source attribution: A randomized controlled evaluation in uro-oncology.

European journal of cancer (Oxford, England : 1990)
INTRODUCTION: Large language models (LLMs) are utilized to answer queries in urology and oncology, yet the performance is limited due to outdated data and missing source transparency, which undermines clinical reliability and therefore adoption. MATE...

Evaluating large language models in biomedical data science challenges through a classroom experiment.

Proceedings of the National Academy of Sciences of the United States of America
Large language models (LLMs) have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges in real-world settings remains poorly understood. We conducted a classroom experiment in which graduate st...

Information Extraction of Doctoral Theses Using Two Different Large Language Models vs Health Services Researchers: Development and Usability Study.

JMIR formative research
BACKGROUND: The Archive of German-Language General Practice (ADAM) stores about 500 paper-based doctoral theses published from 1965 to today. Although they have been grouped in different categories, no deeper systematic process of information extract...

Performance of large language models in non-English medical ethics-related multiple choice questions: comparison of ChatGPT performance across versions and languages.

BMC medical ethics
BACKGROUND: As large language models (LLMs) evolve, assessing their competence in ethically sensitive domains such as medical ethics has become increasingly important. Since medical ethics is a universal component of medical education, disparities in...

Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.

JMIR medical informatics
BACKGROUND: The use of large language models (LLMs) in radiology is expanding rapidly, offering new possibilities in report generation, decision support, and workflow optimization. However, a comprehensive evaluation of their applications, performanc...

Comparative analysis of AI on human nutrition knowledge: Evaluating large language model-based conversational agents against dietetics students and the general population.

PloS one
Understanding the core principles of nutrition is essential in the contemporary context of abundant and often contradictory dietary advice, to empower individuals to make informed dietary choices and manage diet-related non-communicable diseases. The...

Integrating a Large Language Model Into a Socially Assistive Robot in a Hospital Geriatric Unit: Two-Wave Comparative Study on Performance, Engagement, and User Perceptions.

JMIR human factors
BACKGROUND: Addressing the complex medical and psychosocial needs of older adults is increasingly difficult in resource-limited care settings. In this context, socially assistive robots (SARs) provide support and practical functions such as orientati...

Feasibility of a Specialized Large Language Model for Postgraduate Medical Examination Preparation: Single-Center Proof-Of-Concept Study.

JMIR formative research
BACKGROUND: Large language models (LLMs) are increasingly used in medical education for feedback and grading; yet their role in postgraduate examination preparation remains uncertain due to inconsistent grading, hallucinations, and user acceptance.