Explaining chest x-ray pathologies in natural language

M Kayser, C Emde, OM Camburu, G Parsons… - … Conference on Medical …, 2022 - Springer
Most deep learning algorithms lack explanations for their predictions, which limits their
deployment in clinical practice. Approaches to improve explainability, especially in medical …

Lessons learned in transitioning to AI in the medical imaging of COVID-19

I El Naqa, H Li, J Fuhrman, Q Hu… - Journal of Medical …, 2021 - spiedigitallibrary.org
The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world.
It also created a need for the urgent development of efficacious predictive diagnostics …

Integrating eye tracking and speech recognition accurately annotates MR brain images for deep learning: proof of principle

JN Stember, H Celik, D Gutman… - Radiology: Artificial …, 2020 - pubs.rsna.org
Purpose To generate and assess an algorithm combining eye tracking and speech
recognition to extract brain lesion location labels automatically for deep learning (DL) …

Augmented radiologist workflow improves report value and saves time: a potential model for implementation of artificial intelligence

HM Do, LG Spear, M Nikpanah, SM Mirmomen… - Academic radiology, 2020 - Elsevier
Rationale and Objectives Our primary aim was to improve radiology reports by increasing
concordance of target lesion measurements with oncology records using radiology …

AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?

YR Shin, S Kim, YH Lee - Skeletal Radiology, 2022 - Springer
Artificial intelligence (AI) is expected to bring greater efficiency in radiology by performing
tasks that would otherwise require human intelligence, also at a much faster rate than …

[HTML][HTML] Automated liver lesion detection in 68Ga DOTATATE PET/CT using a deep fully convolutional neural network

J Wehrend, M Silosky, F Xing, BB Chin - EJNMMI research, 2021 - Springer
Background Gastroenteropancreatic neuroendocrine tumors most commonly metastasize to
the liver; however, high normal background 68 Ga-DOTATATE activity and high image noise …

[HTML][HTML] Understanding the use of artificial intelligence for implant analysis in total joint arthroplasty: a systematic review

AK Shah, MS Lavu, CJ Hecht, RJ Burkhart, AF Kamath - Arthroplasty, 2023 - Springer
Introduction In recent years, there has been a significant increase in the development of
artificial intelligence (AI) algorithms aimed at reviewing radiographs after total joint …

Intelligent imaging: anatomy of machine learning and deep learning

G Currie - Journal of nuclear medicine technology, 2019 - Soc Nuclear Med
The emergence of artificial intelligence (AI) in nuclear medicine and radiology has been
accompanied by AI commentators and experts predicting that AI would make radiologists, in …

Intelligent imaging: artificial intelligence augmented nuclear medicine

GM Currie - Journal of nuclear medicine technology, 2019 - Soc Nuclear Med
Artificial intelligence (AI) in nuclear medicine and radiology represents a significant
disruptive technology. Although there has been much debate about the impact of AI on the …

Attention deficit/hyperactivity disorder classification based on deep spatio-temporal features of functional magnetic resonance imaging

S Liu, L Zhao, J Zhao, B Li, SH Wang - Biomedical Signal Processing and …, 2022 - Elsevier
Attention deficit/hyperactivity disorder is a neurological disorder characterized by inattention,
hyperactivity and impulsivity. Since the resting functional magnetic resonance imaging (rs …