If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
UK Prime Minister Theresa May has announced plans to invest in a "whole new industry around AI in healthcare". Researchers at the University of Southern California have developed a new predictive model for heart disease, which makes use of a smartphone app. Machine-learning techniques are poised to hit the mainstream over the next few years. Machine learning has long been touted as the next big thing for healthcare. With countless startups investing in that promise, applications are emerging across everything from diagnostics to drug discovery.
Early detection can mean a world of difference for patients diagnosed with skin cancer. In fact, those lucky enough to detect skin cancer early achieve a five-year survival rate of 99 percent. Resource or time constraints often keep people from heading to the doctor early, however, which is why two developers decided to take the world of cancer screening into their own hands and create a free, artificial intelligence-powered screening program completely online. Peter Ma, an independent developer and part of the Intel Software Innovator Program, along with co-founder Mike Borozdin, has developed an AI solution that has the power to determine and classify skin cancer types with the same level of intelligence as a dermatologist. The technology, known as Doctor Hazel, uses deep-learning neural networks to screen and classify skin cancer with 80 percent accuracy.
Gait speed in older patients with cancer is associated with mortality risk. One approach to assess gait speed is with the ‘Timed Up and Go’ (TUG) test. We utilized machine learning algorithms to automatically predict the results of the TUG tests and its association with survival, using patient-generated responses. A decision tree classifier was trained based on functional status data, obtained from preoperative geriatric assessment, and TUG test performance of older patients with cancer. The functional status data were used as input features to the decision tree, and the actual TUG data was used as ground truth labels.
TEMPO.CO, Jakarta - Google has reportedly developed an artificial intelligence (AI) that is able to predict a person's death with an accuracy level of 95 percent, as stated in a journal published by Nature Research and Futurism.com. The records include clinical trial results, readmission, the use of hospital facilities, and patient diagnostics. The Google team reportedly used a medical brain algorithm to construct the AI. The same algorithm is said to be similar to the one used to predict the death of a breast cancer patient, which was considered to be extremely accurate. The main goal of this AI is to be able to utilize it in numerous clinical scenarios to calculate which patient would need the highest priority.
Researchers are developing AI algorithms to detect breast cancer in mammograms.BSIP/UIG/Getty Dexter Hadley thinks that artificial intelligence (AI) could do a far better job at detecting breast cancer than doctors do -- if screening algorithms could be trained on millions of mammograms. The problem is getting access to such massive quantities of data. Because of privacy laws in many countries, sensitive medical information remains largely off-limits to researchers and technology companies. So Hadley, a physician and computational biologist at the University of California, San Francisco, is trying a radical solution. He and his colleagues are building a system that allows people to share their medical data with researchers easily and securely -- and retain control over it.
A new trial in Australia is exploring how robots can improve the outcomes of prostate cancer surgery. On Friday, a new study was published in the academic journal Lancet which outlined a research project conducted by scientists at Griffith University, Queensland. See also: IBM calls healthcare industry a'leaky vessel in a stormy sea' The study focused on a new trial which compared patient outcomes and the success of prostatectomies, which are procedures designed to correct urinary and erectile dysfunction, as well as treat men diagnosed with prostate cancer. Prostate cancer is one of the most common forms of cancer diagnosed in men in the country. Approximately 18,300 men in Australia alone are diagnosed with prostate cancer every year.
A healthtech company using AI to digitize the diagnosis system has raised a new funding round. MetaOptima Technology has raised $8.6 million to grow its cutting-edge DermEngine platform. The Series A round was led by the Australian Skip Capital and AirTree Ventures, with respective fund principals Scott Farquhar and Daniel Petre joining the MetaOptima board. "Our vision is bold: we want to be in every major dermatology centre and skin cancer clinic in Australia, and we're well on track to making that a reality," said Maryam Sadeghi, CEO and co-founder of MetaOptima. "With the support of AirTree Ventures and Skip Capital, we're confident our platform will continue to shape and change the state of play for both healthcare professionals and patients."
When it comes to data curation, the problem isn't the rise of Big Data, but the haphazard way data often present themselves. That's how John Quackenbush characterized the issue in a panel Wednesday morning at the MedCity CONVERGE conference in Philadelphia on practical applications of artificial intelligence (AI) and machine learning (ML) in oncology. Quackenbush, the director of the Center for Cancer Computational Biology at the Dana-Farber Cancer Institute in Boston, was referring to the difficulties of faced by curators when they had to go into clinical trial protocol pages and take down the studies' entry criteria manually due to the inconsistent way they were written. "I like to characterize it not as a Big Data problem, but as a messy data problem," he said. Moderator Ayan Bhattacharya, who serves as advanced analytics specialist leader at Deloitte Consulting, noted that health management organizations, health plans and others have been investing in technology to assist curation that had previously been the work of human editors.
IBM Watson Health and medical imaging contrast agent company Guerbet have entered a strategic partnership to develop artificial intelligence (AI) software to support liver cancer diagnostics and care by utilizing CT and MRI technology. The collaboration will have Guerbet and IBM Watson Health co-develop clinical decision support solutions including Watson Imaging Care Advisor for Liver, a diagnostic support tool that will utilize AI to automate the detection, staging, tracking, monitoring, therapy prediction and response of primary and second liver cancer for clinicians, according to a Guerbet press release published July 10. "Imaging is a critical area of healthcare where we believe artificial intelligence can be used to expand the physician's view so they can be more informed in their diagnostic and treatment decisions for their patients," said Anne Le Grand, vice president of imaging at IBM Watson Health.
The Mark Foundation Institute for Integrated Cancer Medicine will be funded by an £8.6 million award to the University of Cambridge from The Mark Foundation for Cancer Research – the first time that the New York-based philanthropic organisation has made an award to a UK institution. The virtual institute aims to exploit recent advances in big data processing and machine learning to capture and integrate clinical, genomic, and image data collated from hundreds of cancer patients in real-time. Laboratory and clinic-based researchers and data experts will work together to determine whether sophisticated computational integration of all these diverse data types into a single platform can inform and predict the best treatment decisions for each individual patient. Blood tests, biopsies, medical imaging, and genetic tests are a routine part of current cancer care; however, it is not always clear which of these increasingly large datasets are most important in guiding treatment at specific points in the patient journey. "Doctors have long dreamed of an objective system that can integrate all the results generated from their cancer patients, guiding comprehensive treatment decisions both for current treatment and to predict how a particular disease will behave in the future," explains Professor Richard Gilbertson, Director of the Cancer Research UK Cambridge Centre where the new institute will be based.