AIMC Topic: Humans

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Comparing letters written by humans and ChatGPT: A preliminary study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: There are no criteria for what type of manuscript and to what extent ChatGPT use is permissible in writing manuscripts. I aimed to determine which, human or ChatGPT, writes more readable letters to the editor and whether ChatGPT writes let...

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

Drug safety
INTRODUCTION: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse ev...

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVES: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC)...

Machine Learning Methods in Classification of Prolonged Radiation Therapy in Oropharyngeal Cancer: National Cancer Database.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: To investigate the accuracy of machine learning (ML) algorithms in stratifying risk of prolonged radiation treatment duration (RTD), defined as greater than 50 days, for patients with oropharyngeal squamous cell carcinoma (OPSCC).

Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.

Renal failure
BACKGROUND: With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated acute kidney injury has made potential breakthroughs in early identification, grading, diagnosis, and ...

Progress in the application of artificial intelligence in molecular generation models based on protein structure.

European journal of medicinal chemistry
The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyz...

Spectrochemical and explainable artificial intelligence approaches for molecular level identification of the status of critically ill patients with COVID-19.

Talanta
This study explores the molecular alterations and disease progression in COVID-19 patients using ATR-FTIR spectroscopy combined with spectrochemical and explainable artificial intelligence (XAI) approaches. Blood serum samples from intubated patients...

A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation.

International journal of medical informatics
BACKGROUND: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Existing scoring systems or models have limited predictive capabilities for AF patients in ICU. Our study...