The healthcare landscape is changing fast thanks to the introduction of artificial intelligence. These technologies have shifted decision-making power away from nurses and on to the robots. Michael Kennedy, who works as a neuro-intensive care nurse in San Diego, believes AI could destroy nurses’ intuition, skills, and training. The result being that patients are left
Yes… well, sorta. For example, AI was found to be better at identifying TB than medical doctors. The catch here is that it also falsely diagnosed st a much higher rate than doctors. When an investigation was done as to how the AI was evaluating the imaging that it was given, they found that sets of virtually indistinguishable images were given different diagnoses by the AI. In many cases where there were no visible indicators of TB, a positive diagnosis wss given. The reason for this is that the AI was not weighting their TB diagnosis based on markers that doctors would look for alone, but also the age of the machine. Older machines have a much greater chance of being located in developing countries where TB is both more common and more deadly, leading to the age of the machine being considered an important factor, whereas a human would know that the age of a machine has absolutely zero relationship with the chance of getting TB, and doctors in these areas are already aware of and watching out for TB as it’s a much more serious illness than in Germany, for example.
Idk much about the cancer thing, but basically the machine learning for diagnosis thing is iffy at best afaik.
Yes… well, sorta. For example, AI was found to be better at identifying TB than medical doctors. The catch here is that it also falsely diagnosed st a much higher rate than doctors. When an investigation was done as to how the AI was evaluating the imaging that it was given, they found that sets of virtually indistinguishable images were given different diagnoses by the AI. In many cases where there were no visible indicators of TB, a positive diagnosis wss given. The reason for this is that the AI was not weighting their TB diagnosis based on markers that doctors would look for alone, but also the age of the machine. Older machines have a much greater chance of being located in developing countries where TB is both more common and more deadly, leading to the age of the machine being considered an important factor, whereas a human would know that the age of a machine has absolutely zero relationship with the chance of getting TB, and doctors in these areas are already aware of and watching out for TB as it’s a much more serious illness than in Germany, for example.
Idk much about the cancer thing, but basically the machine learning for diagnosis thing is iffy at best afaik.