An AI model developed by the Beckman Institute enables precise medical diagnoses with visual maps for explanation, enhancing doctor-patient communication and facilitating early disease detection.
These networks — just like humans, onions, and ogres — have layers, which makes them tricky to navigate. The more thickly layered, or nonlinear, a network’s intellectual thicket, the better it performs complex, human-like tasks. “They get it right sometimes, maybe even most of the time, but it might not always be for the right reasons,” he said. “I’m sure everyone knows a child who saw a brown, four-legged dog once and then thought that every brown, four-legged animal was a dog.”The black box problem
These models work well, but their bedside manner leaves much to be desired when, for example, a patient asks why an AI system flagged an image as containing a tumor. “And now the question is: which interpretation do you believe?” he said. “There is a chance that your choice will be influenced by your subjective bias, and therein lies the main problem with traditional methods.”
The map — referred to by the researchers as an equivalency map, or E-map for short — is essentially a transformed version of the original X-ray, mammogram, or other medical image medium. Like a paint-by-numbers canvas, each region of the E-map is assigned a number. The greater the value, the more medically interesting the region is for predicting the presence of an anomaly. The model sums up the values to arrive at its final figure, which then informs the diagnosis.
Once the mapmaking model had been trained, the researchers compared its performance to existing black-box AI systems — the ones without a self-interpretation setting. The new model performed comparably to its counterparts in all three categories, with accuracy rates of 77.8% for mammograms, 99.1% for retinal OCT images, and 83% for chest X-rays compared to the existing 77.8%, 99.1%, and 83.33.%
Source: Healthcare Press (healthcarepress.net)
United States Latest News, United States Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
Source: newscientist - 🏆 541. / 51 Read more »
Source: AustinChronicle - 🏆 593. / 51 Read more »
Source: screencrushnews - 🏆 544. / 51 Read more »
Source: comingsoonnet - 🏆 578. / 51 Read more »
Source: News4SA - 🏆 251. / 63 Read more »
Source: News4SA - 🏆 251. / 63 Read more »