Current applications and future impact of machine learning in radiology

G Choy, O Khalilzadeh, M Michalski, S Do, AE Samir… - Radiology, 2018 - pubs.rsna.org
Recent advances and future perspectives of machine learning techniques offer promising
applications in medical imaging. Machine learning has the potential to improve different …

Machine learning in oncology: a clinical appraisal

R Cuocolo, M Caruso, T Perillo, L Ugga, M Petretta - Cancer letters, 2020 - Elsevier
Abstract Machine learning (ML) is a branch of artificial intelligence centered on algorithms
which do not need explicit prior programming to function but automatically learn from …

FUTURE-AI: guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging

K Lekadir, R Osuala, C Gallin, N Lazrak… - arXiv preprint arXiv …, 2021 - arxiv.org
The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …

Interventional Radiology ex-machina: Impact of Artificial Intelligence on practice

M Gurgitano, SA Angileri, GM Rodà, A Liguori… - La radiologia …, 2021 - Springer
Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process
data, understand its meaning and provide the desired outcome, continuously redefining its …

Barriers to lung cancer screening engagement from the patient and provider perspective

GX Wang, TP Baggett, PV Pandharipande, ER Park… - Radiology, 2019 - pubs.rsna.org
Lung cancer remains the leading cause of cancer mortality in the United States. Lung cancer
screening (LCS) with low-dose CT reduces mortality among high-risk current and former …

Narrowing the gap: imaging disparities in radiology

S Waite, J Scott, D Colombo - Radiology, 2021 - pubs.rsna.org
It may seem unlikely that the field of radiology perpetuates disparities in health care, as most
radiologists never interact directly with patients, and racial bias is not an obvious factor when …

[HTML][HTML] Current applications of big data and machine learning in cardiology

R Cuocolo, T Perillo, E De Rosa, L Ugga… - Journal of geriatric …, 2019 - ncbi.nlm.nih.gov
Abstract Machine learning (ML) is a software solution with the ability of making predictions
without prior explicit programming, aiding in the analysis of large amounts of data. These …

Chronic noncancer pain management and systemic racism: Time to move toward equal care standards

M Ghoshal, H Shapiro, K Todd… - Journal of pain …, 2020 - Taylor & Francis
Although it is widely recognized that the United States has a severe and broad systemic
racism problem, recent events have dramatically elevated the issue. Widespread protests in …

Not all biases are bad: equitable and inequitable biases in machine learning and radiology

M Pot, N Kieusseyan, B Prainsack - Insights into imaging, 2021 - Springer
The application of machine learning (ML) technologies in medicine generally but also in
radiology more specifically is hoped to improve clinical processes and the provision of …

Optimization of radiology workflow with artificial intelligence

E Ranschaert, L Topff, O Pianykh - Radiologic Clinics, 2021 - radiologic.theclinics.com
Over the past few years, artificial intelligence (AI) has made a significant advance in the
medical world, particularly due to developments in the field of machine learning (ML) and …