Medicolegal

Toward autonomous design and synthesis of novel inorganic materials.

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity t...

Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?

We argue why interpretability should have primacy alongside empiricism for several reasons: first, i...

Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal.

Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML)...

Increasing the confidence of F-Florbetaben PET interpretations: Machine learning quantitative approximation.

AIM: To assess the added value of semiquantitative parameters on the visual assessment and to study ...

Clinical applications of AI in MSK imaging: a liability perspective.

Artificial intelligence (AI) applications have been gaining traction across the radiology space, pro...

Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.

Policy Points With increasing integration of artificial intelligence and machine learning in medicin...

Artificial Intelligence DENTOMO: Opportunities and Prospects for Interpretation of Cone Beam CT in Dentistry.

The model of artificial intelligence DENTOMO, which allows automated deciphering the CT in maxillofa...

Predicting drug release from diazepam FDM printed tablets using deep learning approach: Influence of process parameters and tablet surface/volume ratio.

The aim of this study was to apply artificial neural networks as deep learning tools in establishing...

Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning.

Information extraction (IE), the distillation of specific information from unstructured data, is a c...

Timely and Efficient AI Insights on EHR: System Design.

A patient's electronic health record (EHR) contains extensive documentation of the patient's medical...

Deep learning in breast radiology: current progress and future directions.

This review provides an overview of current applications of deep learning methods within breast radi...

Development of artificial intelligence system for quality control of photo documentation in esophagogastroduodenoscopy.

BACKGROUND: Esophagogastroduodenoscopy (EGD) is generally a safe procedure, but adverse events often...

Fully automatic wound segmentation with deep convolutional neural networks.

Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems ar...

[Enhanced imaging in urological endoscopy].

White light cystoscopy and the concise documentation of pathological findings are standard diagnosti...

Medicolegal considerations of radiology report writing in the new pjmirodigm of artificial intelligence.

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mec...

How Much Can Potential Jurors Tell Us About Liability for Medical Artificial Intelligence?

Artificial intelligence in medicine: A matter of joy or concern?

Artificial Intelligence (AI), a concept which dates back to the 1950s, is increasingly being develop...

The Utility of Clinical Notes for Sexual Minority Health Research.

INTRODUCTION: Despite improvements in electronic medical record capability to collect data on sexual...

Bruise dating using deep learning.

The bruise dating can have important medicolegal implications in family violence and violence agains...

When Does Physician Use of AI Increase Liability?

An increasing number of automated and artificial intelligence (AI) systems make medical treatment re...