Seven million people's cancer could be prevented each year, according to the first global analysis. A report by World Health Organization (WHO) scientists estimates 37% of cancers are caused by infections, lifestyle choices and environmental pollutants that could be avoided. This includes cervical cancers caused by human papilloma virus (HPV) infections which vaccination can help prevent, as well as a host of tumours caused by tobacco smoke from cigarettes. The researchers said their report showed there is a powerful opportunity to transform the lives of millions of people. Some cancers are inevitable - either because of damage we unavoidably build up in our DNA as we age or because we inherit genes that put us at greater risk of the disease.

The world is full of extraordinary young people brimming with ideas for how to crack tough problems. Every year, we recognize 35 such individuals from around the world--all of whom are under the age of 35. These scientists, inventors, and entrepreneurs are working to help mitigate climate change, accelerate scientific progress, and alleviate human suffering from disease. Some are launching companies while others are hard at work in academic labs. They were selected from hundreds of nominees by expert judges and our newsroom staff. Get to know them all--including our 2025 Innovator of the Year-- in these profiles .

On the southeastern coast of Puerto Rico lies the country's only coal-fired power station, flanked by a mountain of toxic ash. The plant, owned by the utility giant AES, has long plagued this part of Puerto Rico with air and water pollution. Before the coal plant opened Guayama had on average just over 103 cancer cases per year. In 2003, the year after the plant opened, the number of cancer cases in the municipality surged by 50%, to 167. In 2022, the most recent year with available data, cases hit a new high of 209.

To evaluate the use of artificial intelligence (AI) to shorten digital breast tomosynthesis (DBT) reading time while maintaining or improving accuracy. A deep learning AI system was developed to identify suspicious soft-tissue and calcified lesions in DBT images. A reader study compared the performance of 24 radiologists (13 of whom were breast subspecialists) reading 260 DBT examinations (including 65 cancer cases) both with and without AI. Readings occurred in two sessions separated by at least 4 weeks. Area under the receiver operating characteristic curve (AUC), reading time, sensitivity, specificity, and recall rate were evaluated with statistical methods for multireader, multicase studies. Radiologist performance for the detection of malignant lesions, measured by mean AUC, increased 0.057 with the use of AI (95% confidence interval [CI]: 0.028, 0.087; P .01), Reading time decreased 52.7% (95% CI: 41.8%, 61.5%; P .01), Sensitivity increased from 77.0% without AI to 85.0% with AI (8.0%; 95% CI: 2.6%, 13.4%; P .01), The concurrent use of an accurate DBT AI system was found to improve cancer detection efficacy in a reader study that demonstrated increases in AUC, sensitivity, and specificity and a reduction in recall rate and reading time. See also the commentary by Hsu and Hoyt in this issue. Reading times were significantly reduced, and sensitivity, specificity, and recall rate improved in a nonclinical reader study when an artificial intelligence system was utilized concurrently with image interpretation for digital breast tomosynthesis.

Powered by artificial intelligence, a new lung cancer blood test developed at Johns Hopkins, combined with other metrics, correctly identified 94% of cancer cases in almost 800 patients. The lung cancer blood test, published in Nature Communications, searches for tiny fragments of DNA released by the tumor cells. The AI looks for patterns in this shattered DNA, rather than looking for specific pieces of cancer DNA like other blood tests in development, New Atlas explained. Lung cancer kills the most people in the world, the authors note, "largely due to the late stage at diagnosis where treatments are less effective than at earlier stages" -- and lung cancer rates are increasing, worldwide. "We believe that a blood test, or'liquid biopsy,' for lung cancer could be a good way to enhance screening efforts, because it would be easy to do, broadly accessible, and cost-effective," study first author Dimitrios Mathios said. The DNA difference: Blood tests for cancer typically focus on finding pieces of mutated tumor DNA.

We recently reported on a systemdeveloped by Scottish scientists to help identify mesothelioma, a rare form of cancer. A team of researchers from the Department of Pathology at Brigham and Women's Hospital has designed an artificial intelligence model that can find the origin of metastases. In addition, this tool could generate a "differential diagnosis" for patients with cancers whose origin is not known to doctors. For doctors, knowing the primary site of origin of a tumor is essential in order to target the actions they wish to take to fight the cancer and thus increase the survival rate. Most modern therapies are specific to the primary tumor, hence the importance of locating and analyzing it.

How much credit do you get if you're "pretty right" -- meaning, more right than wrong? If you're an artificial intelligence algorithm, you're given a lot of credit. AI programs don't have to have a definitive answer, just a probabilistic one, a percentage likelihood of the right answer, whether the task is performing natural-language translation or diagnosing cancer. The latest example of AI's probabilistic achievements is in this week's issue of Nature magazine, titled "International evaluation of an AI system for breast cancer screening," and is authored by an army of 31 scholars from Google's Google Health unit, its DeepMind unit, and the Imperial College of London, led by authors Scott Mayer McKinney, Marcin T. Sieniek, Varun Godbole, and Jonathan Godwin (DeepMind CEO Demis Hassabis is among the authors). In addition, a blog post gives commentary by Google's Google Health scholars Shravya Shetty, M.S., and Daniel Tse, M.D. Google's Google Health team, its DeepMind unit, and London's Imperial College used a trio of three different deep learning neural networks, consisting of, from the top, Facebook AI's "RetinaNet," combined with Google's "MobileNetV2," followed by the now standard ResNet-v2-50 in the middle section, and lastly a ResNet-v1-50 on the bottom layer.

Validating and testing our supervised machine learning models is essential to ensuring that they generalize well. SAS Viya makes it easy to train, validate, and test our machine learning models. Training data are used to fit each model. Training a model involves using an algorithm to determine model parameters (e.g., weights) or other logic to map inputs (independent variables) to a target (dependent variable). Model fitting can also include input variable (feature) selection.

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. The same research suggests there will be 27.5 million new cancer cases diagnosed each year by 2040.

One in three people are expected to develop cancer during their lifetime. With the help of AI-based algorithms, pathologists will be able to deliver better and faster diagnoses. New cancer cases will increase by almost 70% over the next two decades, from 14 million in 2012 to 22 million. In the US, the National Cancer Institute reports that one in three people are expected to develop cancer in their lifetime. Pathologists are dealing with growing caseloads while patients are increasingly expecting high-quality diagnoses and treatments, causing delays in delivering diagnoses.