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Clinical Competence

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Evaluating AI Competence in Specialized Medicine: Comparative Analysis of ChatGPT and Neurologists in a Neurology Specialist Examination in Spain.

JMIR medical education
BACKGROUND: With the rapid advancement of artificial intelligence (AI) in various fields, evaluating its application in specialized medical contexts becomes crucial. ChatGPT, a large language model developed by OpenAI, has shown potential in diverse ...

Evaluating AI proficiency in nuclear cardiology: Large language models take on the board preparation exam.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Previous studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology. This study assesses four LL...

Enhancements in artificial intelligence for medical examinations: A leap from ChatGPT 3.5 to ChatGPT 4.0 in the FRCS trauma & orthopaedics examination.

The surgeon : journal of the Royal Colleges of Surgeons of Edinburgh and Ireland
INTRODUCTION: ChatGPT is a sophisticated AI model capable of generating human-like text based on the input it receives. ChatGPT 3.5 showed an inability to pass the FRCS (Tr&Orth) examination due to a lack of higher-order judgement in previous studies...

Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee.

Pain practice : the official journal of World Institute of Pain
OBJECTIVES: Artificial intelligence (AI) represents an exciting and evolving technology that is increasingly being utilized across pain medicine. Large language models (LLMs) are one type of AI that has become particularly popular. Currently, there i...

Evaluating ChatGPT's competency in radiation oncology: A comprehensive assessment across clinical scenarios.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: Artificial intelligence (AI) and machine learning present an opportunity to enhance clinical decision-making in radiation oncology. This study aims to evaluate the competency of ChatGPT, an AI language model, in interpreting clinical scenari...

Prospective Evaluation of Real-Time Artificial Intelligence for the Hill Classification of the Gastroesophageal Junction.

United European gastroenterology journal
BACKGROUND: Assessment of the gastroesophageal junction (GEJ) is an integral part of gastroscopy; however, the absence of standardized reporting hinders consistency of examination documentation. The Hill classification offers a standardized approach ...

Artificial Intelligence as a Discriminator of Competence in Urological Training: Are We There?

The Journal of urology
PURPOSE: Assessments in medical education play a central role in evaluating trainees' progress and eventual competence. Generative artificial intelligence is finding an increasing role in clinical care and medical education. The objective of this stu...

RADHawk-an AI-based knowledge recommender to support precision education, improve reporting productivity, and reduce cognitive load.

Pediatric radiology
BACKGROUND: Using artificial intelligence (AI) to augment knowledge is key to establishing precision education in modern radiology training. Our department has developed a novel AI-derived knowledge recommender, the first reported precision education...

Accuracy of radiologists and radiology residents in detection of paediatric appendicular fractures with and without artificial intelligence.

BMJ health & care informatics
OBJECTIVES: We aim to evaluate the accuracy of radiologists and radiology residents in the detection of paediatric appendicular fractures with and without the help of a commercially available fracture detection artificial intelligence (AI) solution i...

The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies.

Dento maxillo facial radiology
OBJECTIVES: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effec...