A research team led by investigators from the NIH 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 approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.
A group of melanoma experts, joined by three advocacy groups focused on melanoma, has engaged FDA in a public discussion of the challenges of developing new drugs in the refractory setting and the role crossover can play in such trials.
