Goto

Collaborating Authors

Oncology


Artificial intelligence in oncology: current applications and future perspectives – Docwire News

#artificialintelligence

Br J Cancer. 2021 Nov 26. doi: 10.1038/s41416-021-01633-1. Online ahead of print. ABSTRACT. Artificial intelligence (AI) is concretely reshaping …


La veille de la cybersécurité

#artificialintelligence

Artificial intelligence is present in everyday life, from booking flights and applying for loans to steering driverless cars. It is also used in specialized fields such as cancer screening or to help create inclusive environments for the disabled. According to UNESCO, AI is also supporting the decision-making of governments and the private sector, as well as helping combat global problems such as climate change and world hunger. However, the agency warns that the technology'is bringing unprecedented challenges'. "We see increased gender and ethnic bias, significant threats to privacy, dignity and agency, dangers of mass surveillance, and increased use of unreliable AI technologies in law enforcement, to name a few. Until now, there were no universal standards to provide an answer to these issues", UNESCO explained in a statement.


Artificial intelligence in oncology: current applications and future perspectives - British Journal of Cancer

#artificialintelligence

Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the ‘AI-revolution’ in oncology.


GE Healthcare and Optellum Join Forces to Advance Lung Cancer Diagnosis with Artificial Intelligence

#artificialintelligence

GE Healthcare and Optellum today announced that they have signed a letter of intent to collaborate to advance precision diagnosis and treatment of lung cancer. GE Healthcare is a global leader in medical imaging solutions. Optellum is the leader in AI decision support for the early diagnosis and optimal treatment of lung cancer. This press release features multimedia. Together, the companies are seeking to address one of the largest challenges in the diagnosis of lung cancer, helping providers to determine the malignancy of a lung nodule: a suspicious lesion that may be benign or cancerous.


193 countries adopt the first global agreement on the Ethics of Artificial Intelligence

#artificialintelligence

Artificial intelligence is present in everyday life, from booking flights and applying for loans to steering driverless cars. It is also used in specialized fields such as cancer screening or to help create inclusive environments for the disabled. According to UNESCO, AI is also supporting the decision-making of governments and the private sector, as well as helping combat global problems such as climate change and world hunger. However, the agency warns that the technology'is bringing unprecedented challenges'. "We see increased gender and ethnic bias, significant threats to privacy, dignity and agency, dangers of mass surveillance, and increased use of unreliable AI technologies in law enforcement, to name a few. Until now, there were no universal standards to provide an answer to these issues", UNESCO explained in a statement.


Previously Unknown Cell Components Revealed by AI-Based Technique

#artificialintelligence

Most human diseases can be traced to malfunctioning parts of a cell -- a tumor is able to grow because a gene wasn't accurately translated into a particular protein or a metabolic disease arises because mitochondria aren't firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts. By combining microscopy, biochemistry techniques and artificial intelligence, researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells. The technique, known as Multi-Scale Integrated Cell (MuSIC), is described November 24, 2021 in Nature. "If you imagine a cell, you probably picture the colorful diagram in your cell biology textbook, with mitochondria, endoplasmic reticulum and nucleus. But is that the whole story? Definitely not," said Trey Ideker, PhD, professor at UC San Diego School of Medicine and Moores Cancer Center.


Heartbeat Anomaly Detection

#artificialintelligence

According to a report of WHO, around 17.9 million people die each year due to Cardiovascular Diseases.Over the years it has been found that these deaths can be prevented if the diseases are diagnosed at an early stage and even the disease can be cured. Artificial Intelligence has been applied in various fields and one of them is AI for healthcare.We have seen AI practitioners coming up with solution for various disease diagnosis such as Cancer Detection, Detection of Diabetic Retinopathy and much more.The techniques used in these detections mostly involve Deep Learning. So, by combining our knowledge of deep learning and with its integration Iot we can develop a smart digital-stethoscope which can help in diagnosing anomalies in heartbeat in real-time and can help in classifying Cardio-diseases. While working in cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import your dataset to your workspace when you work on them.To load the data we just have to enter the following commands: As with all unstructured data formats, audio data has a couple of preprocessing steps which have to be followed before it is presented for analysis. Another way of representing audio data is by converting it into a different domain of data representation, namely the frequency domain.


Deep learning reveals how proteins interact

#artificialintelligence

Scientist are now combining recent advances in evolutionary analysis and deep learning to build three-dimensional models of how most proteins in eukaryotes interact. The research effort has implications for understanding the biochemical processes that are common to all animals, plants, and fungi. The open-access work appears Nov. 11 in Science. As part of a multi-institutional collaboration, the lab of David Baker at the UW Medicine Institute for Protein Design helped guide this new development. "To really understand the cellular conditions that give rise to health and disease, it's essential to know how different proteins in a cell work together," Baker said.


Readiness for mammography and artificial intelligence

#artificialintelligence

One area that has attracted great attention for the use of deep learning artificial intelligence (AI) in health care is medical imaging, especially mammography. Many initial AI studies proclaimed remarkable improvement in accuracy over the performance of radiologists, but a recent systematic review highlighted there is insufficient scientific evidence to support such findings. The UK National Screening Committee commissioned Freeman and colleagues to review the quality and results of studies that assessed the accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in digital mammograms; 34 of 36 AI systems evaluated were less accurate than a single radiologist, and all were less accurate than the consensus of two or more radiologists.


comparison-of-time-to-event-machine-learning-models-in-predicting-oral-cavity-cancer-prognosis

#artificialintelligence

Applying machine learning to predicting oral cavity cancer prognosis is important in selecting candidates for aggressive treatment following diagnosis. However, models proposed so far have only considered cancer survival as discrete rather than dynamic outcomes. To compare the model performance of different machine learning-based algorithms that incorporate time-to-event data. These algorithms included DeepSurv, DeepHit, neural net-extended time-dependent cox model (Cox-Time), and random survival forest (RSF). Retrospective cohort of 313 oral cavity cancer patients were obtained from electronic health records.