AIMC Topic: Internship and Residency

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The Use of Artificial Intelligence in Residency Application Evaluation-A Scoping Review.

Journal of graduate medical education
Several residency programs have begun investigating artificial intelligence (AI) methods to facilitate application screening processes. However, no unifying guidelines for these methods exist. We sought to perform a scoping review of AI model devel...

Anti-HBs persistence and anamnestic response among medical interns vaccinated in infancy.

Scientific reports
Medical interns are at high risk of acquiring Hepatitis B Virus (HBV) infection during their training. HBV vaccination is the most effective measure to reduce the global incidence of HBV. The duration of protection after HBV vaccination is still cont...

AI-generated questions for urological competency assessment: a prospective educational study.

BMC medical education
BACKGROUND: The integration of artificial intelligence (AI) in medical education assessment remains largely unexplored, particularly in specialty-specific evaluations during clinical rotations. Traditional question development methods are time-intens...

AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study.

JMIR medical education
BACKGROUND: As health care moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of artificial intelligence (AI). However, family medicine training in AI has often been inconsistent or lackin...

Artificial intelligence based assessment of clinical reasoning documentation: an observational study of the impact of the clinical learning environment on resident documentation quality.

BMC medical education
BACKGROUND: Objective measures and large datasets are needed to determine aspects of the Clinical Learning Environment (CLE) impacting the essential skill of clinical reasoning documentation. Artificial Intelligence (AI) offers a solution. Here, the ...

Can Artificial Intelligence Coach Faculty to Utilize Growth Mindset Language? A Qualitative Analysis of Feedback Statements.

The Journal of surgical research
INTRODUCTION: Feedback is at the core of competency-based medical education. Learner perceptions of the evaluation process influence how feedback is utilized. Systems emphasize a fixed mindset, prioritizing evaluation over growth. Embracing growth mi...

Accuracy and quality of ChatGPT-4o and Google Gemini performance on image-based neurosurgery board questions.

Neurosurgical review
Large-language models (LLMs) have shown the capability to effectively answer medical board examination questions. However, their ability to answer imagebased questions has not been examined. This study sought to evaluate the performance of two LLMs (...

Exploring the role of artificial intelligence in Turkish orthopedic progression exams.

Acta orthopaedica et traumatologica turcica
OBJECTIVE: The aim of this study was to evaluate and compare the performance of the artificial intelligence (AI) models ChatGPT-3.5, ChatGPT-4, and Gemini on the Turkish Specialization Training and Development Examination (UEGS) to determine their ut...

Integration of artificial intelligence in radiology education: a requirements survey and recommendations from faculty radiologists, residents, and medical students.

BMC medical education
BACKGROUND: To investigate the perspectives and expectations of faculty radiologists, residents, and medical students regarding the integration of artificial intelligence (AI) in radiology education, a survey was conducted to collect their opinions a...

Artificial Intelligence for Teaching Case Curation: Evaluating Model Performance on Imaging Report Discrepancies.

Academic radiology
RATIONALE AND OBJECTIVES: Assess the feasibility of using a large language model (LLM) to identify valuable radiology teaching cases through report discrepancy detection.