In the near future, eye care providers will have devices that, give a picture of a patient’s retina, can tell us the detailed diagnosis at the touch of a button. AI will play an increasingly significant role in the way healthcare providers look after their patients within the clinic. There’s real and imminent potential for earlier, more accurate, and more uniform diagnosis. AI (AI) is not a new term for the industry as its presence in the news.
AI will play an increasingly significant role within the way healthcare providers take care of their patients in the clinic for treatments. There’s real and imminent potential for earlier, more accurate, and more uniform diagnosis.
AI also can improve the stratification of disease, which can successively open currently unseen avenues for developing therapeutic interventions. AI will enable us to get latent features in ophthalmic images, genetics, and clinical profiles, which can improve both our understanding and treatment of eye diseases. AI is here and is gaining momentum.
Considering this, it’s important to know its historical origins, how it works, and the way it’ll change the way eye care is provided.
History of AI
As early as the late 1970s, Kunihiko Fukushima had designed an algorithm- the neocognitron -which had all the key parts of today’s AI algorithms.2 There, however, was no real AI back within the 1970s, 1980s, 1990s, or maybe 2000s because we didn’t have the computational power to create real AI systems. In other words, computers were too weak.
However, the PC gaming industry has for many years been gradually chipping away at the matter of computers too-slow processing. Computers have gotten explosively faster within the last five years because of the graphics processing units that power increasingly sophisticated video games.
By 2012, computers were fast enough and excitement began to create that the age of AI was upon us. As AI systems are rapidly evolving, excitement has not at a notable high level as it is rapidly transforming all industry-including eye care.
How AI works?
A type of algorithm termed as a convolutional neural network (ConvNet) is the workhorse of AI. Reference: LeCun Y, Bengio Y, Hinton G. Nature. 2015 May 27
The core idea behind ConvNets is that a programmer provides an abundance of data, like ophthalmic images, to a computer algorithm. The algorithm then attempts to start assumptions (initially randomly) the diagnosis of any particular image. If the algorithm guesses wrongly, it’s penalized for the incorrect guess; it adjusts itself to decrease its likelihood of guessing wrongly again on its next attempt. If it guesses the diagnosis correctly, it’s rewarded, and it remembers the reward and adjusts itself to extend its likelihood of guessing correctly again on a subsequent attempt.
This process continues iteratively for as repeatedly as is prespecified by the programmer or until the accuracy of the algorithm exceeds a particular point.
As noted, the more the info to which the algorithm is exposed during training time, the more accurate the algorithm is going to be in making the right diagnosis during test time.
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