A.I In Healthcare
July 15, 2024, 3:54 a.m.
One of the most useful applications of "A.I" manifests itself in the health care sector; translating the lightning speed of data processing using AI algorithms with gigantic amounts of raw data.
The data can be medical reports, x-rays, or MRI images, A.I algorithms can scan and iterate over such data sources redundantly for tries only limited to the "computing power" one has. Now imagine a healthcare facility with so many data sources; the AI algorithms in that facility would have countless data to run on: and here is the "jackpot".
Of course I'm not speaking about replacing doctors, rather about "aiding doctors". There are so many details that the human-eye misses during analysis; especially when the data is redundant and large in quantity. A.I algorithms can aid the health sector and bring a game-changing difference in the way sicknesses and diseases are diagnosed.
Even in the discovery of treatments; A.I can pinpoint bits and pieces of the infection or virus that the human-eye missed during the analysis; leading to better production of treatments and medications to treat those "bits and pieces".
In summary, A.I can be an essential tool in the health care sector in the future, by producing well-made algorithms and providing decent data sets a revolution in treatment would defiantly grow from this splendid integration.
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Comments:
On July 19, 2024 Mohammed Ba'ashar wrote:
Thank you Biju,
I can see your point; the data labeling in healthcare applications would require daunting and costly reviews on the quality of the collected data and a high level of educational qualification for the person carrying the labeling itself. i.e you need a doctor to pinpoint "what to label in the dataset" in the first place.
Mohammed Ba'ashar
Simply put, I'm the creator of Byts N Bytes !
On July 19, 2024 Biju Krishnan wrote:
Excellent post. Since healthcare is a regulated industry, implementing AI use cases face additional challenges and scrutiny. Also acquiring LABELLED data for training models is challenging and expensive as labelling requires expert doctors. Hence although the potential for AI in healthcare is massive, actual implementation is going to take time.