AIMC Topic: Physicians

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Comparison of physician and large language model chatbot responses to online ear, nose, and throat inquiries.

Scientific reports
Large language models (LLMs) can potentially enhance the accessibility and quality of medical information. This study evaluates the reliability and quality of responses generated by ChatGPT-4, an LLM-driven chatbot, compared to those written by physi...

A machine learning model using clinical notes to identify physician fatigue.

Nature communications
Clinical notes should capture important information from a physician-patient encounter, but they may also contain signals indicative of physician fatigue. Using data from 129,228 emergency department (ED) visits, we train a model to identify notes wr...

Comparison of quality, empathy and readability of physician responses versus chatbot responses to common cerebrovascular neurosurgical questions on a social media platform.

Clinical neurology and neurosurgery
BACKGROUND: Social media platforms are utilized by patients prior to scheduling formal consultations and also serve as a means of pursuing second opinions. Cerebrovascular pathologies require regular surveillance and specialized care. In recent years...

Comparison of artificial intelligence-generated and physician-generated patient education materials on early diabetic kidney disease.

Frontiers in endocrinology
BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus and has become the most important cause of end-stage renal disease (ESRD). In light of the rising prevalence of diabetes, there is a growing imperativ...

While GPT-3.5 is unable to pass the Physician Licensing Exam in Taiwan, GPT-4 successfully meets the criteria.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: This study investigates the performance of ChatGPT-3.5 and ChatGPT-4 in answering medical questions from Taiwan's Physician Licensing Exam, ranging from basic medical knowledge to specialized clinical topics. It aims to understand these a...

Investigating Whether AI Will Replace Human Physicians and Understanding the Interplay of the Source of Consultation, Health-Related Stigma, and Explanations of Diagnoses on Patients' Evaluations of Medical Consultations: Randomized Factorial Experiment.

Journal of medical Internet research
BACKGROUND: The increasing use of artificial intelligence (AI) in medical diagnosis and consultation promises benefits such as greater accuracy and efficiency. However, there is little evidence to systematically test whether the ideal technological p...

Physician Perspectives on Ambient AI Scribes.

JAMA network open
IMPORTANCE: Limited qualitative studies exist evaluating ambient artificial intelligence (AI) scribe tools. Such studies can provide deeper insights into ambient AI implementations by capturing lived experiences.

Machine learning tools match physician accuracy in multilingual text annotation.

Scientific reports
In the medical field, text annotation involves categorizing clinical and biomedical texts with specific medical categories, enhancing the organization and interpretation of large volumes of unstructured data. This process is crucial for developing to...

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Nature medicine
Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an artificial intelligence (AI) algorithm for direct-to-physician reporting ...