Health Care Neural network application in Clinical diagnosis

During my research for a neural network I wanted to find out how neural networks are being used in the medical industry. There's a company named Open Clinical that produces knowledge management software for the health care industry. Open Clinical is producing a neural network called "Papnet". Papnet is a commercial neural network-based computer program for assisted screening of Pap (cervical) smears. Traditionally, Pap smear testing relies on the human eye to look for abnormal cells under a microscope. A Pap smear test examines cells taken from the uterine cervix for signs of precancerous or cancerous changes. Detected early, cervical cancer has an almost 100% chance of cure, but come to find out a pap smear is the only large scale medical laboratory test that is not automated. A patient with serious conditions can have fewer than a dozen abnormal cells among the 30,000 - 50,000 normal cells on her Pap smear, and it is very difficult to detect all cases since there is no automation. Imagine proof-reading of these pap smears is mostly done by the human eye. With no automation, things as small as spelling errors can really cause days of delays making hard to depend solely 100% on humans to read the test. Margret Sordo from Harvard University explains, the best laboratories can miss from 10% - 30% abnormal cases (Sordo, M. 1995 pg 9). Papnet-assisted reviews of cervical smears result in a more accurate screening process than the current practice, and quicker detection of precancerous and cancerous cells in the cervix. I really feel that more neural networks should be in the medical industry, so that more lives are saved most of all, and that it would allow for better efficiency since your neural network can now handle what humans are asked to do with pap smear.