Computers have been taught to use data to establish patterns where possible. And while the delegation of these activities to machines has helped mankind in many ways, bias still exists in technologies such as artificial intelligence. For instance, there are biases in facial recognition systems, according to Alex Hanna (pictured), director of research at The Distributed AI Research Institute. "The fact remains that facial recognition is used and is disproportionally deployed on marginalized populations," she said. "So in the U.S., that means black and brown communities. That's where facial recognition is used disproportionately."

Researchers from the National Institutes of Health (NIH) and Global Good have created a computer algorithm capable of identifying precancerous changes in women which place them at risk of developing cervical cancer. Known as automated visual evaluation, this new form of artificial intelligence (AI), "has the potential to revolutionize cervical cancer screening" for women in low income communities worldwide by giving their healthcare providers the ability to use digitized images collected during routine, annual screenings for cervical cancer to identify potential precancerous changes. According to America's National Cancer Institute (which is part of the NIH), this technology holds the promise of enabling physicians to more quickly catch and treat such potential changes before they develop into cancer, and could eventually replace visual inspection with acetic acid (VIA) -- the current method of screening used by healthcare professionals who work with limited resources in challenging medical care environments -- a testing system which is "known to be inaccurate." The researchers involved in this project "trained" the machine learning algorithm (automated visual evaluation) to recognize patterns in medical images and other "complex visual inputs" by digitizing and entering more than 60,000 images from an NCI archive of photographs which had been collected from more than 9,400 women in Costa Rica during a 1990s cervical cancer screening study which included follow-up studies for roughly 18 years. These images subsequently enabled the algorithm to "learn" which "cervical changes became precancers and which did not," according to NIH representatives, who added that the AI approach to cervical cancer screening was developed by NCI researchers in collaboration with the Intellectual Ventures Fund, Global Good, with findings confirmed independently by personnel from the National Library of Medicine (NLM), another component of the NIH.

A research team led by investigators from the National Institutes of Health and Global Good has developed a computer algorithm that can analyze digital images of a woman's cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings. To develop the method, researchers used comprehensive datasets to "train" a deep, or machine, learning algorithm to recognize patterns in complex visual inputs, such as medical images. The approach was created collaboratively by investigators at the National Cancer Institute (NCI) and Global Good, a fund at Intellectual Ventures, and the findings were confirmed independently by experts at the National Library of Medicine (NLM). The results appeared in the Journal of the National Cancer Institute on January 10, 2019.

Researchers from the National Institutes of Health and Global Good have developed an artificial intelligence (AI) based computer algorithm that can identify cervical pre-cancer with greater accuracy than a human expert. The algorithm, called automated visual evaluation, can analyse digital images of a cervix and accurately identify precancerous changes that require medical attention, an advance the researchers say could revolutionise screening, particularly in low-resource settings. The new method can be performed with minimal training, making it ideal for countries with limited healthcare resources, where cervical cancer is a leading cause of illness and death among women. According to the researchers, automated visual evaluation is easy to perform. Health workers can use a cell phone or similar camera device for cervical screening and treatment during a single visit.

Researchers have developed a computer algorithm that they say can analyze digital images of a woman's cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings. Led by investigators from the National Institutes of Health and humanitarian tech investment fund Global Good, the researchers used comprehensive datasets to "train" a machine-learning algorithm to recognize patterns in complex visual inputs, such as medical images. The findings were confirmed independently by experts at the National Library of Medicine. The results appeared in the Journal of the National Cancer Institute (NCI).

Algorithm could revolutionize cervical cancer screening, especially in low-resource settings. A research team led by investigators from the National Institutes of Health and Global Good has developed a computer algorithm that can analyze digital images of a woman's cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings. To develop the method, researchers used comprehensive datasets to "train" a deep, or machine, learning algorithm to recognize patterns in complex visual inputs, such as medical images. The approach was created collaboratively by investigators at the National Cancer Institute (NCI) and Global Good, a fund at Intellectual Ventures, and the findings were confirmed independently by experts at the National Library of Medicine (NLM).

The image above has been cropped. Could cervical cancer be brought under control? Not quite, but the results of a study published in the Journal of the National Cancer Institute seem promising. Researchers from the National Institutes of Health and Global Good have developed a deep learning algorithm that can analyze digital images of a woman's cervix and identify precancerous changes that require medical attention -- with more accuracy than human experts. The team used comprehensive datasets to train the algorithm to recognize patterns in complex visual inputs, like medical images.

Can the diverse artificial intelligence (AI) community come together to build an infrastructure to advance the United Nation's sustainable development goals (SDGs, https://sustainabledevelopment.un.org/sdgs) around the world? Can global projects be developed that begin to address pressing issues surrounding some of our greatest humanitarian challenges to help all? Those were the goals of the second annual AI for Good Global Summit, the leading United Nations platform for dialogue on Artificial Intelligence held in Geneva, Switzerland, over three days in May. The conference was organized by the International Telecommunication Union (ITU), the United Nations' specialized agency for information and communication technology (ICT), in partnership with the XPRIZE Foundation, the Association for Computing Machinery (ACM), and 32 sister UN agencies. The 500 attendees consisted of a diverse set of multi-stakeholders with wide-ranging expertise--from the individual UN agencies (including everything from UNESCO and UNICEF to The World Health Organization, The World Bank, and UNHCR), AI researchers, public- and private-sector decision-makers, potential financial partners and sponsor organizations.

A new microscope will use image recognition software and machine learning technology to identify and count malaria parasites in a blood smear. The EasyScan GO, announced at MEDICA, the medical industry's leading trade fair, is the result of a partnership between the Global Good Fund, a Seattle-based group funded by philanthropist Bill Gates, and Motic, a China-based company that specializes in manufacturing microscopes. Field tests have demonstrated that the machine learning algorithm is as reliable as an expert microscopist in fighting the spread of drug resistant malaria. EasyScan GO is the latest example of Global Good's partnering to bring emerging technologies to health systems in low resource settings. Based at the invention company Intellectual Ventures, Global Good is focused on developing and deploying technologies for the poorest parts of the world.

A new microscope will use image recognition software and machine learning technology to identify and count malaria parasites in a blood smear. The EasyScan GO, announced at MEDICA, the medical industry's leading trade fair, is the result of a partnership between the Global Good Fund, a Seattle-based group funded by philanthropist Bill Gates, and Motic, a China-based company that specializes in manufacturing microscopes. Field tests have demonstrated that the machine learning algorithm is as reliable as an expert microscopist in fighting the spread of drug resistant malaria. EasyScan GO is the latest example of Global Good's partnering to bring emerging technologies to health systems in low resource settings. Based at the invention company Intellectual Ventures, Global Good is focused on developing and deploying technologies for the poorest parts of the world.