This article is an installment of Future Explored, a weekly guide to world-changing technology. You can get stories like this one straight to your inbox every Thursday morning by subscribing here. Patient outcomes are almost always better when a disease is diagnosed and treated early, but some illnesses don't trigger symptoms until a patient is already really sick -- ovarian cancer, for example, can go undetected for 10 years or more, giving it time to spread to other organs. By screening healthy patients for these sneaky diseases, doctors can spot them earlier -- and new artificial intelligence (AI) tools promise to help in the hunt. The challenge: Cardiovascular diseases (CVDs) kill nearly 18 million people every year, making them the leading cause of death worldwide.

A biopsy that turns out to have benign results can be a relief. But in some cases, it could also mean a patient whose risk of cancer was low from the start has gone through an unnecessarily invasive procedure. By and large, radiologists recommend that patients whose breast MRI scans raise suspicion of a cancerous growth get a biopsy done. But MRIs often pick up on benign lesions that other mammograms and ultrasound may not. This leads to some patients having their lesions falsely classified as higher risk than they are, and undergoing a biopsy.

A lump in the thyroid gland is called a thyroid nodule, and 5-10% of all thyroid nodules are diagnosed as thyroid cancer. Thyroid cancer has a good prognosis, a high survival rate, and a low recurrence rate, so early diagnosis and treatment are crucial. Recently, a joint research team in Korea has proposed a new non-invasive method to distinguish thyroid nodules from cancer by combining photoacoustic (PA) and ultrasound image technology with artificial intelligence. The joint research team--composed of Professor Chulhong Kim and Dr. Byullee Park of POSTECH's Department of Electrical Engineering, Department of Convergence IT Engineering and Department of Mechanical Engineering, Professor Dong-Jun Lim and Professor Jeonghoon Ha of Seoul St. Mary's Hospital of Catholic University of Korea, and Professor Jeesu Kim of Pusan National University--conducted a research to acquire PA images from patients with malignant and benign nodules and analyzed them with artificial intelligence. In recognition of their significance, the findings from this study were published in Cancer Research.

Artificial intelligence (AI) use in dermatology is primed to become a powerful tool in skin cancer assessment, but it remains to be seen how diagnostic devices in dermatology will influence decision making in the clinic and affect patient outcomes, according to the authors of a Perspective published online today by the Medical Journal of Australia. In dermatology the primary focus for the use of AI has been on developing machine learning systems that facilitate classification and decision support for skin cancer management. "Recent studies show that machine learning algorithms have the potential to surpass the diagnostic performance of experts, and the challenge now is how to implement this new technology safely into clinical practice," wrote the authors, led by Associate Professor Victoria Mar, a consultant dermatologist and Director of the Victorian Melanoma Service at Alfred Hospital. "There are two potentially negative implications for clinical practice: first, clinicians may have difficulty upskilling by following the algorithms' outputs; and second, there exists the potential for deskilling and underperforming due to an over-reliance on technology. Algorithm performance is dependent on both the size and quality of the training image dataset and on whether the algorithm is used in situations for which it was intended," wrote Mar and colleagues.

Artificial intelligence may be the new face of medical diagnostics. For the first time, a flavor of A.I. called deep learning is being implemented in new ultrasound imaging equipment to aid in breast exams and help patients avoid unnecessary biopsies. A new feature in Samsung Medison's ultrasound system uses a deep-learning algorithm to make recommendations about whether a breast abnormality is benign or cancerous. The "S-Detect for Breast" feature is now included in an upgrade to the company's RS80A ultrasound system and is commercially available in parts of Europe, the Middle East and Korea and is pending FDA approval in the U.S., according to PR manager Doug Kim. Deep learning relies on large amounts of data to inform complex decision-making algorithms, has aided in everything from speech and image recognition software to pharmaceutical research.

Samsung Medison's new ultrasound system quickly screens for abnormalities. Artificial intelligence may be the new face of medical diagnostics. For the first time, a flavor of A.I. called deep learning is being implemented in new ultrasound imaging equipment to aid in breast exams and help patients avoid unnecessary biopsies. A new feature in Samsung Medison's ultrasound system uses a deep-learning algorithm to make recommendations about whether a breast abnormality is benign or cancerous. The "S-Detect for Breast" feature is now included in an upgrade to the company's RS80A ultrasound system and is commercially available in parts of Europe, the Middle East and Korea and is pending FDA approval in the U.S., according to PR manager Doug Kim.