Oncology


Generate More Training Data When You Don't Have Enough

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Computers outperform humans in image and object recognition. Big corporations like Google and Microsoft have beat the human benchmark on image recognition [1, 2]. On average, human makes an error on image recognition tasks about 5% of the time. As of 2015, Microsoft's image recognition software reached an error rate of 4.94%, and at around the same time, Google announced that its software achieved a reduced error rate of 4.8% [3]. This was possible by training deep convolutional neural networks on millions of training examples from ImageNet dataset which contains hundreds of object categories [1].


SkinVision an AI-powered app could detect Skin Cancer with 95.1% accuracy - Morning Tick

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SkinVision app claims to detect the most common forms of skin cancer. It is an Android and iOS app that allows the user to assess and track changes in the skin spots over time. The user has to submit a photo of their skin using their smartphone camera. After analyzing the image with Artificial Intelligence algorithm, the app delivers the risk assessment. There are three levels of risk described by the app: low, low with symptoms or high.


Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers. - PubMed - NCBI

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Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology.


ASTRO: AI predicts radiation side effects for cancer patients

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"Being able to identify which patients are at greatest risk would allow radiation oncologists to take steps to prevent or mitigate these possible side effects," Reddy added. "If the patient has an intermediate risk, and they might get through treatment without needing a feeding tube, we could take precautions such as setting them up with a nutritionist and providing them with nutritional supplements. If we know their risk for feeding tube placement is extremely high – a better than 50% chance they would need one – we could place it ahead of time so they wouldn't have to be admitted to the hospital after treatment. We'd know to keep a closer eye on that patient."


Ping An Leads Investment in Riverain Technologies to Advance AI in Healthcare

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Ping An Insurance (Group) Company of China, Ltd. (hereafter "Ping An" or the "Group", HKEX: 2318; SSE: 601318) is pleased to announce Ping An Global Voyager Fund is leading an investment of US$15 Million in Riverain Technologies, a leading provider of clinical artificial intelligence software used to efficiently detect lung disease at its earliest stages. Riverain Technologies markets advanced artificial intelligence imaging software used by leading hospitals around the world. The software significantly improves a clinician's ability to accurately and efficiently detect cancer and other cell anomalies in thoracic CT and X-ray images. The company's suite of patented ClearReadTM software tools are FDA-cleared, deployable in the clinic or in the cloud, and powered by the most advanced artificial intelligence and machine learning methods available to the medical imaging market. Its products are relied upon by leading healthcare institutions, including Duke University, Mayo Clinic, University of Chicago, University of Michigan, and Veterans Affairs hospitals.


Artificial Intelligence – Improving How We Diagnose Cancer

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The Journal Metabolism Clinical and Experimental mentions in a recent review that the use of artificial intelligence (AI) in medicine has come to cover such broad topics from informatics to the application of nanorobots for the delivery of drugs. AI has come a long way from its humble beginnings. With the advanced development of AI systems and machine learning, more significant medical applications for the technology are emerging. According to Cloudwedge, FocalNet, an AI system recently developed by researchers at UCLA, can aid radiologists and oncology specialists in diagnosing prostate cancer. According to UK Cancer Research Magazine, over 17 million cancer cases were diagnosed across the globe throughout 2018.



Cancer Commons Cancer Commons Founder to Speak at Stanford Medicine X Conference

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We are developing an AI-based platform to run "Virtual Trials" (VT) that continuously learn from the experiences of all patients, on all treatments, all the time. Each patient is treated using the best available knowledge and therapies, and these are continuously refined based on clinical results This approach shifts the clinical trials paradigm from approving drugs to curing patients and understanding the disease. AI is used in three ways: It democratizes access to the thinking of top cancer experts; it coordinates treatment recommendations across patients and institutions to maximize learning; and it prioritizes promising treatments to accelerate their development. By tightly integrating cancer research and clinical care, we are enabling physicians to make better treatment decisions, bio-pharmas to slash the time and cost of developing drugs, and patients to achieve superior outcomes.


Why the AI story needs to be told from end to beginning

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It is an unfortunate truth that most people around the world don't have a real understanding of what artificial intelligence (AI) is. The same is true for many of the influential people who are in active discussions and working with companies where AI plays a significant part in our future. In fact, a recent survey found that most organisations don't have a clear understanding of how AI or machine learning will help their businesses. In general, people's perception of what AI is comes from movies and media, with doomsday scenarios and super intelligent robots taking over the world. It's not surprising given the unprecedented speed of development in AI, and the hype that has come with it.


Artificial Intelligence and Health Care: Our Progressing Relationship with Medicine - Legal Reader

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As technology advances, particularly with artificial intelligence, changes are being seen in all industries. Health care is no exception. The prime reason for all sorts of technological advancements made throughout history are in one way or another is the desire of people to better their lives. This is particularly relevant to the natural human longing for longevity and eternal youth. Those two concepts are often heavily associated with having high living standards and better health care.