Machines don't have emotions -- at least not yet. So you have good reason to wonder about a digital doctor's bedside manner. But look at it this way: No matter how caring and compassionate that flesh-and-blood physician is, she may get things wrong an alarming amount of the time. Studies of autopsies, in fact, have shown that doctors seriously misdiagnose fatal illnesses in about one in five cases. And even more alarmingly, that error rate hasn't improved much since the 1930s, despite the vast amount of new medical knowledge and diagnostic tools -- ranging from MRI scanning to genetic tests -- that we've amassed since then [source: Leonhardt].
The unsettling fact is that human healers miss signs and make mistakes, and that's where computers can be of help. In the late 1990s, a British girl named Isabel Maude nearly died after her doctors diagnosed a flesh-eating bacteria infection as chicken pox. Her father Jason was so shaken by the experience that he decided to change his career. He left his job in corporate finance to found a new company, Isabel Healthcare, to help improve diagnoses. The company developed a program called Isabel, which allows doctors to type in a patient's symptoms. After probing a database of 100,000 medical sources, Isabel uses a set of specially developed problem-solving algorithms to generate a list of all the possible causes, including non-obvious ones that doctors may have overlooked. Within months of its launch in 2002, Isabel attracted 20,000 physician users in 100 countries. Today, the program is utilized in hospitals in the U.S. and abroad, and the American Medical Association offers it to members as a support tool on its Web site [source: Leonhardt, Isabelhealthcare.com].
Recently, the National Library of Medicine has been working to develop a similar intelligent medical assistant that could answer patients' questions based in part on the answers to close to 200,000 queries posed by both medical and lay users at the Web site AskTheDoctor.com [source: PRWeb].
But the use of supercomputers, which can amass and crunch even more massive amounts of information, may enhance AI's potential even further.