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Artificial intelligence, in the form of queried databases, is helping to tackle certain cancers. Algorithms have been developed to cross-reference a patient's medical records, habits and genetic information to spot any early signs of cancer. With cancer, the key problem is about the late diagnosis of cancer and the argument of using artificial intelligence is to identify those members of the population who are at greatest risk and to then bring them in earlier for screening. A pilot has been developed in the U.K., where the focus is particularly with prostate, lung and bowel cancer, and then you can undertake procedures like surgery or administer treatment sooner in order to increase survival rates. Similarly, a U.S. study, published in 2022, found that a machine learning algorithm trained to predict cancer outcomes zeroed in to finds the prostate on male patients and successfully outlines any cancer-suspicious areas without any human supervision.
Building your organization's digital acumen is a critical priority for CIOs today. You won't be future-ready by simply hiring people with the most cutting-edge skills – you also need to create a culture where everyone is continually working on growing and evolving their skills together. I often tell my IT organization that the half-life of an IT professional is about 18 months because technology is constantly changing; thus, it is critical we are all continual learners. That's true not only at Johnson & Johnson; I've observed this across other companies and industries too. To support building our IT organization's digital acumen, we implemented a program that uses artificial intelligence to assess our skills.
Special report AI can study chemical molecules in ways scientists can't comprehend, automatically predicting complex protein structures and designing new drugs, despite having no real understanding of science. The power to design new drugs at scale is no longer limited to Big Pharma. Startups armed with the right algorithms, data, and compute can invent tens of thousands of molecules in just a few hours. New machine learning architectures, including transformers, are automating parts of the design process, helping scientists develop new drugs for difficult diseases like Alzheimer's, cancer, or rare genetic conditions. In 2017, researchers at Google came up with a method to build increasingly bigger and more powerful neural networks.
Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain.
New software developed by Peter Mac and collaborators is helping patients diagnosed with acute lymphoblastic leukemia (ALL) to determine what subtype they have. ALL is the most common childhood cancer in the world, and also affects adults. "Thirty to forty percent of all childhood cancers are ALL, it's a major pediatric cancer problem," says Associate Professor Paul Ekert from Peter Mac and the Children's Cancer Institute, who was involved in this work. More than 300 people are diagnosed with the disease in Australia each year, and more than half of those are young children under the age of 15. Determining what subtype of ALL a patient has provides valuable information about their prognosis, and how they should best be treated.
Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills. The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations. Once virtual, the AI Cures Conference returned to in-person attendance at MIT's Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB). MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the "transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise."
On May 17, two Toulouse-based institutes, the IRT Saint Exupéry and the IUCT-Oncopole, a European center of expertise in oncology, signed a partnership focused on artificial intelligence. The aim of this partnership is to pool cutting-edge skills around AI-based research projects designed to improve prevention, diagnosis and care in oncology, particularly by predicting therapeutic effectiveness. Two of these projects are already at an advanced stage. The Saint Exupéry Institute of Technological Research aims to accelerate scientific and technological research and transfer to the aeronautics and space industries for the development of reliable, robust, certifiable and sustainable innovative solutions. A private research foundation supported by the French government, the IRT's mission is to promote French technological research for the benefit of industry and to develop the ecosystem of the aeronautics, space and critical systems sectors by providing access to its research projects, technological platforms and expertise.
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Researchers at Memorial Sloan Kettering Cancer Center (MSK) have developed a sensor that can be trained to sniff for cancer, with the help of artificial intelligence. Although the training doesn't work the same way one trains a police dog to sniff for explosives or drugs, the sensor has some similarity to how the nose works. The nose can detect more than a trillion different scents, even though it has just a few hundred types of olfactory receptors. The pattern of which odor molecules bind to which receptors creates a kind of molecular signature that the brain uses to recognize a scent. Like the nose, the cancer detection technology uses an array of multiple sensors to detect a molecular signature of the disease.