Artificial Intelligence is either a silver shot for each issue on the planet or the ensured reason for the end of the world, contingent upon whom you address. The fact of the matter is probably going to be unmistakably progressively unremarkable. Artificial intelligence is a tool and like numerous technological breakthroughs before it, it will be utilized for good and for terrible. Artificial intelligence is progressively being utilized to impact the products we purchase and the music and movies we appreciate; to protect our money; and, dubiously, to settle on hiring decisions and procedure criminal behaviour. The Western world has been digitized for more, so there are more records for AIs to parse.
A recent study by IBM Research, together with Sage Bionetworks, Kaiser Permanente Washington Health Research Institute, and the University of Washington School of Medicine, has uncovered how combining machine learning algorithms and assessments by radiologists could improve the overall accuracy of breast cancer screenings. Mammogram screenings, commonly used by radiologists for the early detection of breast cancer, according to IBM researcher Stefan Harrer, frequently rely on a radiologist's expertise to visually identify signs of cancer, which is not always accurate. "Through the current state of human interpretation of mammography images, two things happen: Misdiagnosis in terms of missing the cancer and also diagnosing cancer when it's not there," Harrer told ZDNet. "Both cases are highly undesirable -- you never want to miss a cancer when it's there, but also if you're diagnosing a cancer and it's not there, it creates enormous pressure on patients, on the healthcare system, that could be avoided. "That is exactly where we aim to improve things through the incorporation of AI (artificial intelligence) to decrease the rate of false positives, which is the diagnosis of cancer, and also to decrease missing the cancer when there is one." The research used more than 310,800 de-identified mammograms and clinical data from Kaiser Permanente Washington (KPWA) and the Karolinska Institute (KI) in Sweden. Of the combined datasets, KI contributed around 166,500 examinations from 6,800 women, of which 780 were cancer positive; while the remaining 144,200 examinations were provided by KPWA from 85,500 women, of which 941 were cancer positive. "We had hundreds of thousands of mammograms that were annotated.
On Friday 6 March, Professor Wajcman will deliver the inaugural Jessie Street lecture at the University of Sydney, during which she will explain why the hyperproductive – and often hypermasculine – work culture of Silicon Valley needs to be challenged. "Whether it's seat belts that don't protect pregnant women, voice-recognition technology that is less likely to understand women than men or smart houses that facilitate entertainment rather than housework; gendered stereotypes will inevitably affect the kinds of technology that we choose to develop and invest in," Professor Wajcman will explain. "My research has shown that too often, designers build technologies for people like themselves. A lack of diversity among designers or engineers will inevitably skew the kinds of technology that are developed." Last year Professor Wajcman was appointed as Turing Fellow and Principal Investigator on the Women in Data Science and Artificial Intelligence research project at the Alan Turing Institute in London.
When the European Commission released the long awaited white paper "On Artificial Intelligence - A European approach to excellence and trust" on February 19, much of the initial public reaction focused on potential AI regulation further challenging the EU's position in light of fierce technological competition from China and the United States. Few discussed the European Commission's document mention of gender and ethical guidelines. Importantly, the white paper calls for "requirements to take reasonable measures aimed at ensuring that [the] use of AI systems does not lead to outcomes entailing prohibited discrimination." This is not simply about a theoretical approach to discrimination. It is largely also about saving (women's) lives - and ensuring that essential products and services meet the needs of both women and men.
Matthew Sappern is the CEO of PeriGen, which is a global leader in applying artificial intelligence to obstetrics and fetal outcomes. How can technology help reduce adverse events and fetal outcomes in the events surrounding childbirth? Click here to get the transcript for this podcast! Joanna McGrath, an experienced OB nurse educator and also an expert witness who shares tips about this highly litigated area. You will learn to identify the most common sources of obstetrical nursing malpractice, including fetal distress and also shoulder dystocia.
If you want to master Python programming language then you can't skip projects in Python. After publishing 4 advanced python projects, DataFlair today came with another one that is the Breast Cancer Classification project in Python. To crack your next Python Interview, practice these projects thoroughly and if you face any confusion, do comment, DataFlair is always ready to help you. An intensive approach to Machine Learning, Deep Learning is inspired by the workings of the human brain and its biological neural networks. Architectures as deep neural networks, recurrent neural networks, convolutional neural networks, and deep belief networks are made of multiple layers for the data to pass through before finally producing the output.
Almost 400,000 babies were born prematurely--before 37 weeks gestation--in 2018 in the United States. One of the leading causes of newborn deaths and long-term disabilities, preterm birth (PTB) is considered a public health problem with deep emotional and challenging financial consequences to families and society. If doctors were able to use data and artificial intelligence (AI) to predict which pregnant women might be at risk, many of these premature births might be avoided. "Premature birth prediction has been an exceedingly challenging problem," said Ansaf Salleb-Aouissi, a senior lecturer in discipline from the computer science department. "But we are now at a point where we can use machine learning to develop a dynamic risk prediction system for pregnant women. Creating a system that can process large models of data with AI algorithms we develop would be a great benefit to supplement physicians' 'real-life' expertise."
NAIROBI, Jan 31 (Thomson Reuters Foundation) - Ugandan doctors are giving new mothers artificial intelligence-enabled devices to remotely monitor their health in a first-of-its-kind study aiming to curb thousands of preventable maternal deaths across Africa, medics and developers said. Doctors at Mbarara Hospital in western Uganda will give devices to more than 1,000 women who have undergone caesarean section births to wear on their upper arms at all times. Algorithms detect at-risk cases and alert doctors. Joseph Ngonzi from Mbarara University of Science and Technology, which is conducting the study, said it would help "improve monitoring in a resource-constrained environment". The World Health Organization says almost 300,000 women worldwide die annually from preventable causes related to pregnancy and childbirth - that's more than 800 women every day.
Roughly one in eight women in the U.S. will develop invasive breast cancer over the course of her lifetime, according to the American Cancer Society. Early detection of abnormal tissue or tumor is critically important for treatment, but mammograms -- the best screening tools currently available for doctors -- have their limitations. Clinicians fail to catch about a fifth of all breast cancer cases, and half of U.S. women receiving annual mammograms in a given decade will be incorrectly told they have breast cancer when they actually don't, the cancer group says. Google Health's new tool, developed in tandem with its British subsidiary DeepMind, Northwestern Medicine and Imperial College London and the University of Cambridge, improved mistakes in both areas. False positives were reduced by 5.7% and 1.2%, and false negatives were reduced by 9.4% and 2.7% in the U.S. and U.K., respectively.
Artificial intelligence is being used in IVF to select embryos with the highest chance of resulting in a successful pregnancy. The AI algorithm, called Ivy, analyses time-lapse videos of embryos as they are incubated after being fertilised, and identifies which ones have the highest likelihood of successful development. It was developed by Harrison.ai, Women who undergo IVF using Ivy are informed about the algorithm and consent to its use.