In theory, artificial intelligence should be great at helping out. "Our job is pattern recognition," says Andrew Norgan, a pathologist and medical director of the Mayo Clinic's digital pathology platform. "We look at the slide and we gather pieces of information that have been proven to be important." Visual analysis is something that AI has gotten quite good at since the first image recognition models began taking off nearly 15 years ago. Even though no model will be perfect, you can imagine a powerful algorithm someday catching something that a human pathologist missed, or at least speeding up the process of getting a diagnosis.

An ultra-sensitive robotic'finger' that could help detect breast cancer is being developed by scientists. Experts have created a device with a sophisticated sense of touch that can take patient pulses and check for abnormal lumps. The technology could make it easier for doctors to detect diseases such as breast cancer early on, when they are more treatable. And it may also help patients feel at ease during physical examinations that can seem uncomfortable and invasive, the researchers said. While rigid robotic fingers already exist, experts have raised concerns that these devices might not be up to the delicate tasks required in a doctor's office setting.

During her Ph.D., Dr. Qurrat Ul Ain developed a computer-aided diagnostic system that can identify certain characteristics of the disease from a photograph of a skin lesion. "Skin cancer has certain unique visual features that help to differentiate it from normal skin," Dr. Qurrat Ul Ain says. "These include color, texture, and the shape of lesions. By showing our artificial intelligence program images of cancerous skin, we were able to teach it to identify cancer when shown other photographs." Dr. Qurrat Ul Ain's diagnostic system achieved a 100% accuracy rating in identifying images of melanoma based on the more than 600 images tested so far.

Te Herenga Waka-Victoria University of Wellington PhD graduate Dr Qurrat Ul Ain has developed an artificial intelligence programme that could help diagnose skin cancer, using just a photograph. During her PhD, Dr Qurrat Ul Ain developed a computer-aided diagnostic system that can identify certain characteristics of the disease from a photograph of a skin lesion. "Skin cancer has certain unique visual features that help to differentiate it from normal skin," Dr Qurrat Ul Ain says. "These include colour, texture, and the shape of lesions. By showing our artificial intelligence programme images of cancerous skin, we were able to teach it to identify cancer when shown other photographs."

Researchers at the Tampa veterans' hospital are training computers to diagnose cancer. It's one example of how the Department of Veterans Affairs is expanding artificial intelligence development. Inside a laboratory at the James A. Haley Veterans' Hospital in Tampa, Fla., machines are rapidly processing tubes of patients' body fluids and tissue samples. Pathologists examine those samples under microscopes to spot signs of cancer and other diseases. But distinguishing certain features about a cancer cell can be difficult, so Drs.

Big thanks to technological advances in areas like genetics, imaging, cancer is now more likely to be caught at an earlier stage than it was decades ago. Though, the accuracy in medical imaging diagnosis is still low, with the professionals witnessing 20-30 percent wrong negatives in chest X-rays and mammographies. AI can prevent this, and the fact that healthcare is data-rich is an added benefit. The more data visible to them, the more likely they can uncover the hidden patterns inside it that can be used to perform diagnosis. Over time, many machine learning algorithms have been introduced, but traditional forms, like logistic regression, have demonstrated the most usefulness in clinical oncology research.

The new decade opened with some intriguing news: The journal Nature reported that artificial intelligence was better at identifying breast cancers on mammograms than radiologists. Researchers at Google Health teamed up with academic medical centers in the United States and Britain to train an AI system using tens of thousands of mammograms. To understand why, it helps to have a sense of how AI systems learn. In this case, the system was trained with images labeled as either "cancer" or "not cancer." From them, it learned to deduce features -- such as shape, density and edges -- that are associated with the cancer label.

The new decade opened with some intriguing news: The journal Nature reported that artificial intelligence was better at identifying breast cancers on mammograms than radiologists. Researchers at Google Health teamed up with academic medical centers in the United States and Britain to train an AI system using tens of thousands of mammograms. To understand why, it helps to have a sense of how AI systems learn. In this case, the system was trained with images labeled as either "cancer" or "not cancer." From them, it learned to deduce features -- such as shape, density and edges -- that are associated with the cancer label.

The new decade opened with some intriguing news: the journal Nature reported that artificial intelligence was better at identifying breast cancers on mammograms than radiologists. Researchers at Google Health teamed up with academic medical centers in the United States and Britain to train an AI system using tens of thousands of mammograms. To understand why, it helps to have a sense of how AI systems learn. In this case, the system was trained with images labeled as either "cancer" or "not cancer." From them, it learned to deduce features from the images -- such as shape, density and edges -- that are associated with the cancer label.

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.